Dorling, D. (1991) PhD Thesis: The Visualization of Spatial Social Structure,
University of Newcastle upon Tyne.
Scans of the prints referred to can be found here:
http://www.sasi.group.shef.ac.uk/thesis/prints.html
The Visualization of Spatial Social Structure
Thesis submitted for the
degree of Doctor of Philosophy to
the University of Newcastle upon Tyne,
September 1991.
by
Daniel F. L. Dorling
i
ii
Abstract
A great deal of information about the social geography of Britain is
contained within databases such as the census. To comprehend this
information it needs to be effectively visualized. Conventional maps
contain an unwanted distortion however, and have been rejected by many
as an unsuitable means of showing spatial social structure. A more human
cartography is developed here to show the events of people's lives and the
shape of society. This thesis argues that a truer picture is obtained by being
able to see the whole, in as much detail as possible, at a glance.
A total of 179 high resolution prints show original techniques to study
many aspects of life in Britain today. They include pictures of the
distribution of age, sex, birthplace and occupation in 1981, changes in
these from 1971, unemployment and house price dynamics throughout the
1980s, general election results from 1955 to 1987 (followed by all local
election voting from 1987 to 1990), migration flows from one part of the
country to another and daily commuting streams. These are of interest for
the various methods of visualization used, their content, and the extremely
high levels of detail achieved. Over ten thousand places are shown in most
of the images produced.
Much of the work involved the creation of computer generated cartograms
where each areal unit (up to one hundred thousand to a page) is drawn in
proportion to the number of people who live there. Colour and complex
symbols are used to study several factors simultaneously and visually
effective means of showing millions of flows and other changes over time
are developed. A case study of the distribution of childhood leukaemia in
space and time is also undertaken. Tables give the detailed results of the
last ten general elections (with a basis for dealing with constituency
boundary changes). The algorithm to create a detailed cartogram is
presented and an index is included.
iii
To Benjamin Dorling
iv
Contents
Abstract
Dedication
Contents
List of Figures
List of Prints
Preface
Acknowledgments
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Introduction: Human Cartography
1
Chapter 1: Envisioning Information
1.1 Visual Thinking
1.2 Pictures Over Time
1.3 Beyond Illustration
1.4 Texture and Colour
1.5 Perspective and Detail
1.6 Pattern and Illusion
1.7 From Mind to Mind
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11
14
17
19
22
24
26
Chapter 2: People, Spaces and Places
2.1 Which People
2.2 Why Study Places?
2.3 What Are Spaces?
2.4 Drawing Lines
2.5 Picturing Points
2.6 Population Space
2.7 Adding Time
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41
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Chapter 3: Artificial Reality
3.1 Imagining Reality
3.2 Abstract Spaces
3.3 Area Cartograms
3.4 The Nature of Space
3.5 Producing Illusions
3.6 Population Space
3.7 Stretching Spacetime
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60
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Chapter 4: Honeycomb Structure
4.1 Viewing Society
4.2 Who the People Are
4.3 Disparate Origins
4.4 Lost Opportunities
4.5 Work, Industry and Home
4.6 How People Vote
4.7 The Social Landscape
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Chapter 5: Transforming the Mosaic
5.1 Still Images of Change
5.2 Forming the Structure
5.3 Structure Transformed
5.4 Variable Employment
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95
v
5.5 House Price Inflation
5.6 Reshaping Votes
5.7 Erosion and Deposition
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Chapter 6: Cobweb of Flows
6.1 What Flow Is
6.2 What Flows There Are
6.3 Unravelling the Tangles
6.4 Drawing the Vortices
6.5 Commuting Chaos
6.6 Migration Networks
6.7 A Space of Flows
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108
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114
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Chapter 7: On the Surface
7.1 2D Vision, 3D World
7.2 Surface Definition
7.3 Depth Cues
7.4 Landscape Painting
7.5 Surface Geometry
7.6 Travel Time Surface
7.7 Surface Value
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Chapter 8: The Wood and the Trees
8.1 Sculptured Characters
8.2 Circles, Pies and Rings
8.3 Bars and Pyramids
8.4 Flocks of Arrows
8.5 Trees and Castles
8.6 Crowds of Faces
8.7 Information Overload
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Chapter 9: Volume Visualization
9.1 The Third Dimension
9.2 Spaces, Times and Places
9.3 Spacetime Continuum
9.4 Three Dimensional Graphs
9.5 Flows Through Time
9.6 Volume Rendering
9.7 Interactive Visualization
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Conclusion: Another Geography
172
Bibliography
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Appendices
Appendix A: Circular Cartogram Algorithm
Appendix B: Parliamentary Constituencies 1955-1987 Continuity
Appendix C: Parliamentary Constituencies 1955-1987 Results
Appendix D: Average Housing Price by Constituency 1983-1989
Appendix E: Scottish Ward to Postcode Sector Look-up Table
Appendix F: Local Government Wards, 1981 and 1987
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Index
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List of Figures
Figure 1: Creating the Graphics
16
Figure 2: Printing in Colour
20
Figure 3: Recording the Places
28
Figure 4: Drawing the Maps
31
Figure 5: Storing the Geometry
35
Figure 6: The Areal Hierarchy
38
Figure 7: The Mercator Projection
52
Figure 8: The Algorithm at Work
59
Figure 9: Deriving a Constant
61
Figure 10: Many-dimensional Cartograms
64
Figure 11: Storing the Census
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Figure 12: Working Definitions
78
Figure 13: Two-dimensional Smoothing
80
Figure 14: Linking the Censuses
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Figure 15: How Closely Connected?
91
Figure 16: Measuring the Changes
93
Figure 17: Storing the Flows
105
Figure 18: A Significant Flow
111
Figure 19: Drawing Overlapping Arrows
112
Figure 20: The Electoral Triangle
126
Figure 21: The Perspective Projection
128
Figure 22: Travel Time Surface
135
Figure 23: Areal Interpolation
139
Figure 24: Trees and Pyramids
144
Figure 25: Constructing Face Glyphs
150
Figure 26: Three-dimensional Smoothing
160
Figure 27: The Electoral Tetrahedron
163
Figure 28: Three-dimensional Structure
167
Figure 29: References Over Time
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List of Prints
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XXXII
XXXIII
XXXIV
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XXXVII
XXXVIII
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XLVIII
XLIX
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LI
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LVIII
Two images from the infinity of the Mandelbrot set (Colour).
1
Land use close-up of Northern Britain (Colour).
2
Journey to work flows of over ten people between wards from the 10% sample.
3
The changing distribution of housing by price, attributes and sales, 1983-1987.
4
Migration flows between all regions in 1976 — flows sorted by contiguity order.
5
Yearly migration flows between English and Welsh wards 1980/1981.
6
The changing distribution of age and gender in Britain 1971-1981 (Colour).
7
Voting composition on the electoral cartograms of Northern Britain (Colour).
8
Voting composition on the electoral cartograms of Southern Britain (Colour).
9
The distribution of employment by industry, status and gender (Colour).
10
Stills from a conventional animation of the computer (Colour).
11
Stills from a ray-traced animation of the computer (Colour).
12
Ray-traced surfaces of the Mandelbrot and Julia sets.
13
Visualizing Fourier transforms — the art in the science (Colour).
14
A maze of colour — the detail a low resolution image can show (Colour).
15
Visualization of the Mandelbrot set — magnification and generalization (Colour).
16
Travel time from the Tyneside road network (Colour).
17
Three alternative colour schemes and keys (Colour).
18
The concentration of British born place of birth (Colour).
19
The distributions of population, age, gender and children in London (Colour).
20
The distributions of place of birth in London (Colour).
21
The distributions of employment, occupation and graduates in London (Colour).
22
The distribution of broad industrial groups in Britain, 1987 (Colour).
23
The changing distribution of broad industrial groups, 1984-87, increases (Colour).
24
The changing distribution of broad industrial groups, 1984-87, decreases (Colour).
25
The change in employment by industry, status and gender, 1984-1987 (Colour).
26
Political swing on the electoral cartograms of Northern Britain (Colour).
27
Political swing on the electoral cartograms of Southern Britain (Colour).
28
The distribution of voting in English and Welsh local elections (Colour).
29
Land use in Britain by 1km square grid (Colour).
30
Level II European Regions — annotated base map shaded by unemployment rate.
31
Counties and Scottish Regions — annotated base map shaded by unemployment rate.
32
Family Practitioner Committee Areas — annotated base map shaded by unemployment rate. 33
Local Education Authorities — annotated base map shaded by unemployment rate.
34
“Functional Cities” — annotated base map shaded by unemployment rate.
35
Local Labour Market Areas — annotated base map shaded by unemployment rate.
36
Travel-to-work Areas — annotated base map shaded by unemployment rate.
37
Local government districts — annotated base map shaded by unemployment rate.
38
Parliamentary Constituencies — annotated base map shaded by unemployment rate.
39
Amalgamated Office Areas — annotated base map shaded by unemployment rate.
40
Postcode Areas — coloured at random (Colour).
41
Postcode Districts — coloured at random (Colour).
42
Postcode Sectors — coloured at random (Colour).
43
The British mainland rail network on an equal land area projection.
44
The British mainland rail network on an equal population projection.
45
The British primary road network on an equal land area projection.
46
The British primary road network on an equal population projection.
47
Experiments with area cartograms (Colour).
48
Continuous area cartograms of the British population (Colour).
49
County boundaries showing bridges which maintain ward continuity.
50
The evolution of a cartogram of population by County.
51
The County population cartogram with arrows representing topology.
52
Local authority districts on an equal land area projection indexed for identification.
53
Local authority districts — indexed list in alphabetical order.
54
Local authority districts cartogram indexed for identification.
55
Parliamentary Constituencies on an equal area projection indexed for identification.
56
Parliamentary Constituencies — indexed, list in alphabetical order.
57
Parliamentary Constituency cartogram indexed for identification.
58
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LIX
LX
LXI
LXII
LXIII
LXIV
LXV
LXVI
LXVII
LXVIII
LXIX
LXX
LXXI
LXXII
LXXIII
LXXIV
LXXV
LXXVI
LXXVII
LXXVIII
LXXIX
LXXX
LXXXI
LXXXII
LXXXIII
LXXXIV
LXXXV
LXXXVI
LXXXVII
LXXXVIII
LXXXIX
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XCI
XCII
XCIII
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XCV
XCVI
XCVII
XCVIII
XCIX
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CI
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CIX
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CXIII
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Census wards — 1981 resident population area cartogram.
The concentration of unemployment by ward.
The distribution of unemployment by ward.
Counties and Scottish regions — four colour map.
Counties and Scottish Regions on the enumeration district cartogram.
Enumeration district population cartogram.
1981 equal population grid squares.
1981 population enumeration district cartogram showing the national grid.
The changing distribution of total population in Britain, 1971-1981.
The distribution of age and gender in Britain, 1981 (Colour).
The concentration of age and gender in Britain, 1981 (Colour).
The distribution of children by age in Britain, 1981 (Colour).
The distribution of Irish born in Britain, 1981.
The distribution of British born place of birth, 1981 (Colour).
The distribution of Overseas born place of birth, 1981 (Colour).
The concentration of Overseas born place of birth, 1981 (Colour).
The distribution of employment in Britain, 1981 (Colour).
The concentration of employment in Britain, 1981 (Colour).
The distribution of occupation in Britain, 1981 (Colour).
The concentration of occupation in Britain, 1981 (Colour).
The distribution of graduates in Britain, 1981 (Colour).
The distribution of housing price in Britain, 1983.
The distribution of voting in the 1987 British general election (Colour).
The map of voting in the 1987 British general election (Colour).
The distribution of first placed parties in the 1987 British general election (Colour).
The distribution of second placed parties in the 1987 British general election (Colour).
The distribution of non-voting in the 1987 British general election.
The distribution of voting composition in the 1987 British local elections (Colour).
The changing distribution of British born place of birth, 1971-1981.
The changing distribution of overseas born place of birth, 1971-1981 (Colour).
The changing distribution of employment in Britain, 1971-1981 (Colour).
The space/time trend of unemployment in Britain by office areas, 1978-1990.
The space/time trend of unemployment in Britain by counties, 1978-1990.
The changing distribution of occupation in Britain, 1971-1981 (Colour).
The distribution of housing price inflation in Britain, 1983/1984.
The distribution of housing price inflation in Britain, 1984/1985.
The distribution of housing price inflation in Britain, 1985/1986.
The distribution of housing price inflation in Britain, 1986/1987.
The distribution of housing price inflation in Britain, 1987/1988.
The distribution of housing price inflation in Britain, 1988/1989.
The distribution of housing price in Britain, 1989.
Voting composition by constituency, 1955-1987 (Colour).
The distributions of first placed party, 1955-1987 (Colour).
The distributions of second placed party, 1955-1987 (Colour).
The distributions of non-voting by constituency, 1955-1987.
Migration flows between all regions in 1976, sorted by contiguity order.
Migration flows between metropolitan counties and other areas, 1975-1976.
Daily commuting flows on an equal land area projection in 1981.
Daily commuting flows on an equal population projection in 1981.
Daily commuting flows as a proportion of destination employees.
Daily commuting flows as a proportion of destination residents.
Daily commuting flows on an equal area projection by occupation, 1981 (Colour).
Daily commuting flows in population space by occupation, 1981 (Colour).
Migration flows between family practitioner areas of 1 in 200 people.
Migration flows between family practitioner areas of 1 in 300 people.
Migration flows between family practitioner areas of 1 in 500 people.
Migration flows between family practitioner areas of 1 in 1000 people.
Migration flows between family practitioner areas of 1 in 2000 people.
Migration flows between family practitioner areas of 1 in 2777 people.
Migration flows between family practitioner areas on an equal area projection.
Migration flows between English and Welsh counties on an equal area projection.
59
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CXX
Yearly migration flows on an equal area projection by occupation, 1981 (Colour).
CXXI
Yearly migration flows in population space by occupation, 1981 (Colour).
CXXII
The use of contours and colour to depict surface height (Colour).
CXXIII
The use of contours without colour to depict surface height.
CXXIV
British population surface showing the 1987 general election results (Colour).
CXXV
British population two-way surface of the 1987 general election results (Colour).
CXXVI
The distribution of voting composition in the 1987 British general election (Colour).
CXXVII The national constituency voting compositions, 1955-1987 (Colour).
CXXVIII 1981 County council elections — English voting composition.
CXXIX
1985 County council elections — English voting composition.
CXXX
1989 County council elections — English voting composition.
CXXXI
1981 County council elections — English voting composition surface.
CXXXII 1985 County council elections — English voting composition surface.
CXXXIII 1981/1985 County council elections — changing English voting composition surface.
CXXXIV 1989 County council elections — English voting composition surface.
CXXXV 1985/1989 County council elections —changing English voting composition surface.
CXXXVI 1981/85/89 County council elections —changing English voting composition surface.
CXXXVII The distribution of unemployment in Britain 1981— shown as a surface.
CXXXVIII The changing distribution of first place party in Britain, 1983-1987 (Colour).
CXXXIX The changing distribution of first place party in Britain, 1955-1987 (Colour).
CXL
The space/time trend of unemployment in Britain, 1978-1990.
CXLI
The detailed national composition of industry in Britain, 1981
CXLII
The changing national composition of industry in Britain, 1984-1987.
CXLIII
The distribution of employment by industry, status and gender, 1987.
CXLIV
The change in employment by industry, status and gender, 1984-1987.
CXLV
The changing distribution of voting composition in Britain, 1983-87 (Colour).
CXLVI
The changing distribution of voting composition in Britain, 1955-87 (Colour).
CXLVII The distribution of housing by price, attributes and sales, 1987.
CXLVIII The distribution of housing by price, attributes and sales, 1987 (Colour).
CXLIX
The changing distribution of housing by price, attributes and sales, 1983/1987 (Colour).
CL
Chernoff faces showing all permutations of five levels of four features.
CLI
The distribution of voting, housing, employment and industry in 1983 (Colour).
CLII
The distribution of voting, housing, employment and industry in 1987 (Colour).
CLIII
The change in voting, housing, employment & industry, 1983-1987 (Colour).
CLIV
The distribution of population by county in 1981 (Colour).
CLV
Population change in Britain by district, 1961-1991 (Colour).
CLVI
The space/time trend of unemployment in Britain by cubes, 1978-1990.
CLVII
The space/time trend of unemployment in Britain by rings, 1978-1990.
CLVIII
The distribution of years of highest house price inflation in Britain, 1983 to 1989.
CLIX
The distribution of childhood leukaemia in Britain, 1966-1983 (Colour).
CLX
Six views of the childhood leukaemia spacetime distribution, 1966-1986.
CLXI
Key to cancer types shown by spheres in the spacetime diagrams (Colour).
CLXII
The distribution of childhood cancers in Euclidean spacetime (Colour).
CLXIII
The distribution of childhood cancers in spacetime 1968-1979 (Colour).
CLXIV
The distribution of childhood cancers in spacetime 1980-1991 (Colour).
CLXV
The distribution of childhood cancers in Teesside spacetime, from the east (Colour).
CLXVI
The distribution of childhood cancers in Teesside spacetime, from the west (Colour).
CLXVII The 1988 district election results: Scottish voting composition tetrahedron.
CLXVIII A schematic representation of four party voting compositions.
CLXIX
The 1988 district election results: Scottish voting composition unfolded.
CLXX
Four perspective views of the 1988 Scottish district elections composition (Colour).
CLXXI
A ray-traced image of the 1988 Scottish district elections composition (Colour).
CLXXII The ward cartogram drawn using Theisson polygons.
CLXXIII The Transformed map of voting in the 1987 British general election (Colour).
CLXXIV Transforming the political map of northern Britain to population space (Colour).
CLXXV Transforming the political map of southern Britain to population space (Colour).
CLXXVI The distribution of non-voting by voting composition in the 1987 general election.
CLXXVII The distribution of non-voting in constituencies by voting composition, 1955-1987.
CLXXVIII The distribution of occupation in Britain, 1981, after binomial smoothing (Colour).
CLXXIX The distribution of voting composition in British local elections 1987-1990 (Colour).
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x
Preface
This dissertation is about new ways of presenting and understanding
some of the vast amounts of information which have been collected
about our society. It is based on the premise that huge numbers of
figures, incomprehensible in themselves, could contain a lot of
information that is distorted, even lost, in conventional statistical
analysis and cartographic display. This work has used the hopes and
ideas of numerous sources as to how to visualize information about
society more effectively. Recent dramatic increases in computer
capability have allowed me to create images which would not have
been conceivable two or three years ago. The approach has been
experimental. I often had little idea what an image would look like on
screen or paper until it was actually produced. The illustrations
presented here are only a small selection of those created.
Many ideas and several themes are contained in this thesis. The text
describes the rationale for, and development of, a new way of
visualizing information in geographical research. Through the pictures
the methods are illustrated; mistakes, techniques and discoveries
shown. From the footnotes, which are largely quotations from a
disparate literature, the origins of many of the ideas can be found.
Time and again the suggestions of others to move in these directions
are cited. Through technical asides some of the practical realities of
the work are described. Through the illustrations and their legends, a
picture of what has been happening to Britain in recent years unfolds.
Many of the pictures justify an extended discussion, but I have aimed
to keep the commentary brief. I have not included much detail about
the computer software I have written and used because much of that is
dependant on a novel (but inexpensive) hardware configuration and
progress is so rapid that such knowledge is of only transient value.
xi
Periodically I have commented on the changing political and social
geography of Britain that the mapping has revealed.
Numerous case studies are included. Questions concerning the
implications of the spread of people in time and space are addressed at
many points. The patterns through ten British general elections are
depicted. The distribution of voting in ten thousand local ward
contests is shown in a unique illustration. Some aspects of what the
census can tell us about many people from their ages, sexes,
occupations, activities and qualifications is revealed over a large set of
very small areas. How people get to work, and the structure of the
towns and cities in which they live, is examined. Migration is studied
in several ways. The changing patterns of migration from birthplace
are shown, and the streams of movement that cut across the country
are drawn in unprecedented detail. House price change is visualized
across several years and thousands of places. New techniques are
developed to show the structure of local housing markets. Through
other methods, the changes in this country’s industrial structure are
seen as they have affected people in actual communities. The spatial
and social manoeuvering of political allegiances is viewed from
several angles, over the same period, and the relationships discussed.
Finally, a smaller scale of analysis is considered, looking at what
many images can tell us about the distribution of a disease, viewed
from many different directions in space and time.
These social and political subjects are not arranged in their own,
individual chapters, but run through the dissertation, which itself looks
at methods of visualization, rather than the visualization of subjects.
The work begins with a plea for a more human cartography to depict
the events of our lives. The long history, but recent explosion, of
envisioning information is then introduced. The rationale for this
method of studying people, places and spaces is discussed. The form
of artificial reality we require — area cartograms — are produced.
xii
The central part of the work looks at the honeycomb structure formed
by the spatial patterns of society at single points in time; and how that
alters through transforming the mosaic. The cobweb of flows which is
responsible for most of the changes and stability is then drawn. This
part of the dissertation is illustrated by many case studies. The last
part attempts to show more complex aspects on the surface of social
landscapes. Sculptured symbols allow us to see the relationships
between the wood and the trees of social structure. Finally, a threedimensional volume visualization of geographical and historical social
structures is attempted. The thesis concludes by describing how all
these methods and insights can together create another geography.
Human geography demands that we consider what is happening in
many places at the same time. We do not need to study aspects of the
world out of context. Here, an attempt is made to cover much ground
and show numerous relationships. To do this it is necessary to be brief
in detail, to be broad in scope, so the pictures often have to speak for
themselves. Much of what has been shown here has never been
successfully analysed by conventional means. However, this research
does not come out of the blue, but accompanies thousands of papers
written in the last three years describing the academic hope for a
revolution in visualization, the history of which goes back to the first
drawings. The message of visualization is that we should literally look
at what is happening, drawing pictures in preference to writing words,
listing numbers or designing theoretical models. It has only recently
become possible to do this in such quantity and quality of detail. The
prints resulting here are the tools of enquiry, not simple pictures for
embellishment, but the foundation of the thesis. This is a story of
invention and discovery.
xiii
Acknowledgements
This thesis is based on two years of research funded by the Strang Studentship of
Newcastle University. During the course of the research I have been helped by
many individuals.
The inter-library loan staff of the Robinson Library kindly waived their normal
restrictions to meet my countless requests. Judith Houston helped secure the
funding and dealt extremely efficiently with the administrative side of my work
throughout the period. Many of the staff of the Centre for Urban and Regional
Development Studies and the Geography and Planning Departments showed an
interest and encouraged me in my work, including Alan Gillard, Tony Champion,
Peter Taylor, David Sear and James Cornford. Colin Wymer, Simon Raybould and
Mike Coombes helped satisfy my appetite for digital information from census
flows, the National On-line Manpower Information System and Building Society
records.
The researchers associated with the NorthEast Regional Research Laboratory were
particularly supportive. Zhilin Li allowed me to use a digital terrain model of part
of Scotland. Anna Cross assisted in accessing the Cancer Registry information.
Steve Carver provided records of road, rail and land-use data. Chris Brunsdon who
advised on the analysis of the house price sample. Stan Openshaw supervised the
project, financed much of the equipment, and supplied the local election and 1971
census information.
In particular, thanks are due to Martin Charlton, who read this document and spent
many hours helping me amass the vast majority of the information used here, as
well as permitting extensive use of a great deal of expensive equipment. Bruce
Tether spent many days assisting with the editing and collection of the thousands
of election results used, and gave useful criticism and advice. Richard Park read
and commented on the final draft. Ile Ashcroft and Edward Jones, corrected much
of the English, while Stacy Hewitt gave the work a professional proof-reading.
Eric Charlesworth advised on the style at an early stage, as well as providing
geographical advice. Bronwen Dorling meticulously corrected my writing and gave
constant encouragement, as well as originally teaching me to read. David Dorling
helped rearrange many of the ideas presented here, and first taught me to program.
Finally Anna Macdonald spent several weeks referencing small scale maps and
typing in numerous extensive quotations and tables of data. She also had to put up
with my obsession to finish the work on time. Nothing is achieved in isolation.
Introduction: Human Cartography
1
Introduction: Human Cartography
Images are only images. But if they are numerous, repeated, identical, they
cannot all be wrong. They show us that in a varied universe, forms and
performances can be similar: there are towns, routes, states, patterns ...
which in spite of everything resemble each other.
[Braudel F., 1979, p.133]
This dissertation presents the thesis that the study of society can be
enlightened through the visualization of social structure. Spatial social
patterns provide the most arresting pictures of the underlying order
and workings of the system, but other facets of the process can also be
transformed into images to illuminate their organization. Visualization
in social science throws light into a dark world of specialisms and
obscurity, showing at an instant how all is connected and everywhere
is different. Most importantly we can begin to see how the structure is
changing what was, and what could be.
The antecedents of this work lie most firmly in human geography and
cartography while being strongly influenced by writings in, and the
combinations of, many other disciplines (Arnheim R. 1976, Muehrcke P.C.
1978, 1981, Bertin J. 1983b, Szegö J. 1984, 1987, Anderson J.M. 1988, Cuff D.J. 1989).
There are contributions from studies in computer and statistical
graphics, graphic design and art, mathematical abstraction (Print I)
and political science. Current thinking in the study of history and
sociology guides much of the writing. Above all, this dissertation is
concerned with designing new ways of seeing the social world we live
in. Before doing that, it is necessary to explain why the accepted
geographical techniques are being discarded by the visual
methodology proposed here. In particular, the conventional use of
2
Introduction: Human Cartography
maps of physical geography, to show the spatial structure of society, is
rejected.
Maps were designed to explore new territories and fight over old ones.
They show where oceans lie and rivers run. Their projections are
calculated to aid navigation by compass or depict the quantity of land
under crops (Print II). They are a flat representation of part of the
surface of the globe; they show things which often cannot be seen.
How can we see social structure, as the map opens up land to the
eye1? How can we begin to see the patterns of society, which, from
being part of it we know are there, but have never seen?
Maps were not designed to show the spatial distributions of people,
although the single spatial distribution of people upon the surface of
the globe, at one instance in time, can be shown on them. They cannot
illustrate the simplest human geography of population. People are but
points on the map, clustered into collections of points called homes,
into groups of points known as villages or cities. Communities of
people are not like fields of crops. The paths through space which they
follow are not long wide rivers of water, and yet, to see anything on
maps of people they must be shown as such.
Conventional maps cannot show how many people live in small areas,
instead they show how little land supports so many people. They
cannot show who the people are, what they do, where they go. They
show no temporal distribution, they do not need to — how quickly do
rivers move or mountains shrink on a human timescale? They will not
show the distributions of people changing — international migration,
moving house, or just going to work. They cannot portray the
1 The advantage of maps is simple — they provide context:
Maps frown upon the isolation of single items. They preserve
the continuity of the real world. They show things in their
surroundings and therefore call for more active discernment
on the part of the user, who is offered more than he came for;
but the user is also being taught how to look at things
intelligently. One aspect of looking at things intelligently is to
look at them in context. [Arnheim R. 1976 p.5]
2 [a] The search for a definition of "maps" never ceases:
The current definition of cartography is inadequate largely
because it does not define clearly the focus of the subject,
namely maps. The description of maps is circular — "maps
may be regarded as including all types of maps, charts,
sections ...". This implies two types of maps, namely a
subclass of specific forms, called maps, and a superclass of
generic forms also called maps. The subclass of maps is
defined as a "representation, normally to scale and on a flat
3
Introduction: Human Cartography
distribution of the wealthy or the poor; on the map, at almost any
scale, they live in much the same square inch of paper. Nor will they
show where and when people had certain jobs, certain power, voted,
were out of work, lived and died. What, after all, is a map2 (Hsu M.L.
1979, Brannon G. 1989, Phillips R.J. 1989)?
Pictures can make ideas plausible, paper beautiful, millions of
numbers meaningful. They have intrigued many, as maps and charts
of rivers and mountains, to the point of being the pretext for their
studying geography. Here traditional maps are the inspiration, but not
the foundation, for the generation of new graphics to form pictures of
people, with their rivers of roads down which they flow, and
mountains of cities up which they climb. The theory of how the
patterns, movement and evolution of the lives of millions can be
transformed to be represented visually, is presented here3.
We want to make sense of the reality of thousands of people
simultaneously threading their way through life (Print III). What are
they doing and why are they doing it? How can we see into every
home, know what everyone does? We can't, but we can guess and we
have some clues. We can guess from what, introspectively, we know
from being part of society. We amass clues when people are counted.
There has been an obsession for counting people since recording
medium, of a selection of material or abstract features on, or
in relation to, the surface of the earth or of a celestial body"
(ICA [International Cartographic Association], 1973, p.7).
This second definition makes it clear that the subclass differs
from its generic class in some ways. But, the two definitions
taken together do not identify the common properties shared
by all maps, which set them apart from artefacts which are not
maps. [Visvalingam 1989 p.26]
[b] The most important aspect of definition concerns
visualization:
For the ICA, oblivious to the contradiction inherent in its own
definition, the end 'product' or cartographic process (the map)
is to be 'visual, digital, or tactile'. Yet how can numbers, the
constituents of what has been called, appropriately enough,
the 'invisible map' be described as a map before they have
been processed into an image (the visual map)? In following
the politics of expediency rather than linguistic logic, and
anxious to ward off (in the words of one President of the ICA)
the threat of 'rapid submergence' by the new GIS-based
technology, the ICA has managed to shoot itself in the foot. It
has given the non-map parity with the map! [Harley J.B. 1990
p.16]
[c] An old definition is surprisingly apt:
One of the definitions of the word "map" that appears in the
Oxford Dictionary dates from a source of 1586, where it was
used to describe "a circumstantial account of a state of things
("circumstantial" is defined as "full of circumstances, details
or minutiae"): not a bad objective 400 years later! [Bickmore
D.P. 1975 p.328]
3 The term and philosophy of visualization did not appear
overnight:
The medium of graphics has long been used to create twodimensional representation of spatial phenomena for the
primary purpose of visualization and, for many, this has also
been the essence of "cartography". [Muehrcke P. 1972 p.27]
4
Introduction: Human Cartography
became possible. Every ten years, in many countries, hundreds of
thousands of people count people (the census). Increasingly our
actions are being recorded; we are each noted now several times a
day, from the heat we register on satellite images, to almost every
transaction and phone call we make or unit of electricity we consume.
The question this thesis addresses is how can the part of this huge
disparate collection of clues that is available to us be built up to form
an at least partial picture of the patterns we imagine exist. The answer
is, as it has often before been, in the form of pictures.
The conventional statistical treatments of numerical information about
people averages them, agglomerates them and destroys the detail that
is of interest4. They take a million numbers and return half a dozen.
These techniques were conceived when little better could be done.
Now it is possible to show you a million bits of information at a
glance that would be challenging to describe in a thousand words
(Print IV). Our minds are the most powerful tools we have to address
these problems. The difficulty comes in trying to address these
problems to our minds.
Orthodox cartographic methodology has been translated onto the
computer screen (Bickmore D.P. 1975, Hagen C.B. 1982, Taylor D.R.F. 1985, Jupe D.
1987, Goodchild M.F. 1988b, Visvalingham M. 1989, Muller J.C. 1989). The name has
4 It is the great wealth of pattern and variation that is of
interest in many of the pictures drawn in this dissertation:
The dismissal of geographical diversity as merely 'noise' or
'residuals' is a betrayal of what geography is. [Taylor P.J. 1991
p.24]
5 [a] The cartographic basis of physical geography dates
from a time when land was all important and people had few
rights:
Traditional cartography is seen as an optimal response to a
highly constrained technology based largely on pen and paper.
Although many of the conventions of manual cartography
appear to be intelligent choices, they have nevertheless been
made in an extremely restricted environment which imposes a
limited view of reality. Early digital technology did little to
broaden the constraints, and led cartography, map analysis and
spatial analysis in different directions. More recent hardware
and the results of intensive research have produced a digital
cartography which can successfully emulate its analogue
parent. However, its true potential lies in less conventional
methods of analysis and display and in the degree to which it
can escape its traditional constraints. [Goodchild M.F. 1988
p.311]
[b] Putting the argument less gracefully:
In physical geography, only that which has an effect on
mankind is studied. Now that men are much less dependent on
the countryside than on cities, why have geographers not
followed mankind? Why have geographers left their minds
back on the farm? [Bunge W. 1975 p.177]
[c] New computer systems often fail practically as well as
theoretically:
There seems to be an inverse law where, as the sophistication
of GIS software grows, the attention to basic principles of
graphic design lessens. The emphasis is on getting something
on the screen quickly rather than getting something on the
5
Introduction: Human Cartography
changed to geographic information systems, but the fundamental basis
of physical geography has remained5. Thematic maps drastically
distort the reality they purport to contain, at worse reversing the
patterns that exist. People who study people, who are interested in
societies, politics, history, economics and increasingly even human
geography, do not use these maps. A topographic map base allows, at
most, the depiction of human land use. People have created maps
based on human geography in the past, but only with the advent of
sophisticated computer graphics has it become possible to do this on
an easily replicable basis.
This thesis is presented as part of the academic revolution known as
visualization, and parallels are drawn with the wider world of
computer graphics. Therefore the basics of what can physically be
seen have to be introduced. How these images are created has to be
explained — from the theoretical to the practical problems. Most
importantly I address the problem of how time and space can be
transformed to represent clearly the patterns within them, on paper, or
in animation.
That transformation is essential for representation is a most difficult
screen that is meaningful. [Medyckyj-Scott D. 1991 p.21]
6 [a] The geographical features most of us recognise are
not physical — we do not live near mountains:
Base data is so traditional that it invites a critical review.
Consider the use of rivers on base maps. With the invention of
bridges to cross them and railroads to compete with
transportation on them, it could be argued that rivers have
become unimportant enough to be eliminated from the map.
They might be replaced by major railroad lines. In general, the
traditional base map data is especially unsatisfactory to human
geographers. Terrain features might be profitably dropped in
favor of a surface of population density. The "continents" of
population clusters on the Eastern shores of the United States,
Western Europe, China and elsewhere are many times more
important to the economic geographer than the distinction
between land and water traditionally shown and memorized.
Major cities are more important "islands" for many purposes
than the atolls of the Pacific. It is probably true that of all the
degrees of latitude and longitude shown on the map, only the
equator and the poles are on the mental map and, therefore,
the other degrees might be dropped as superfluous. Much of
what has become traditional base map material might have
been selected for no better reason than the ease with which the
material could be gathered by early explorers. It is much
easier to plot the continental outlines, rivers and mountain
peaks than to obtain a census of population or an accurate map
of arable land. [Bunge W. 1966 pp.45-46]
[b] A more human-based geography is being called for:
The inspiration may come not only from the field of
geography / cartography but also from different fields of
artistic endeavour, and lead to the design of maps of human
activities which are much more vital than the thematic maps
of today. Paradoxically, developments in computer technology
may lead to the creation of maps which, when it comes to
spontaneity and liveliness, have more in common with the
popularly-admired and beloved, hand-drawn maps of the
middle-ages and the renaissance than with strict, formalized
cartography of the modern day.
However, certain conditions must be fulfilled for this to
happen. Suitable cartographic data must be made available,
and computers must be adapted to user needs in such a way
that the technology does not impose itself between the user
and his future map. An additional precondition is a revived
interest in working with spatial / geographical problems, and a
renewal of the skills involved in solving such problems by
graphic means as well as in presenting these solutions in a
creative way.
If these requirements are fulfilled — and the geographer /
cartographer must assume a great deal of responsibility for
this — a new era will be initiated for human cartography.
Introduction: Human Cartography
6
argument to accept, for it completely alters the images produced and
hopefully the emphasis of the viewer to places, and, more importantly,
the relationship between places and times — the metric. The argument
for transforming to population space, distributions which exist only in
that space, has been made repeatedly over many decades in human
geography. It is simply reiterated in stronger terms here, the new
images being traditionally referred to as cartograms. Put simply,
people no longer exist on paper as points, but as areas6, so can now be
legitimately drawn as fields, their paths as rivers (Print V) perhaps
running through a landscape of accessibility covered with the
vegetation of some aspect of social structure.
This thesis draws on those patterns of people that are familiar to this
writer and the envisaged reader. Britain in the 1970s and 80s is all that
I have known in any detail, and only a very small part of that. The
clues given by the official sources consist mostly of the absolute
numbers, age and sex of people across the country. Then, every ten
years the combinations of their answers to a few questions at the
census are provided, where they were born, what job they do or did,
where they did it, where they moved (Print VI) and so on. But there
are other forms of information that can be drawn on, and, as one claim
of visualization is the ability to handle large quantities of loosely
related data coherently, other sources and surveys are called upon.
[Szegö J. 1987 p.231]
7 [a] There are many sources of digital information about
people in Britain:
The only nation-wide count of the population in Britain occurs
at census year; the last two censuses were held in 1971 and
1981. With no 1976 or 1986 mid-term censuses, this
information is currently produced only once every ten years.
However, during the inter-censal years there are a number of
other sources which can provide information on the changing
socioeconomic, demographic and manpower characteristics of
the population at the local scale ... [McKee C. 1989 p.1]
[b] Pre-eminent in all these data sets is the decennial
census:
Some information on other characteristics of the population
such as house-hold structure, employment status, ethnic
composition and housing situation can be gleaned from the
annual General Household and Labour Force surveys, but the
problem of small sample size virtually rules out their use at
scales below the Standard Region. As a result, the Population
Census is not just the best, but in practice the only, source of
reliable data on a reasonably wide range of demographic and
socio-economic characteristics at sub-regional scale.
Moreover, it has the advantage of providing data down to the
level of the individual enumeration district covering roughly
500 inhabitants, which, even if too small for certain purposes,
can be treated as a building block for areas specially defined
by the user (Rhind, 1983). [Champion A. G. 1989 p.113]
[c] National and local election results also provide
information:
In many ways elections are a positivist's dream. Millions of
people go through the process of voting in numerous countries
every year and these decisions are put together and published
by areal units ready for analysis by social scientists. [Taylor
P.J. 1978 p.153]
Introduction: Human Cartography
7
How people voted in the local and national elections of the decades,
national surveys of workplaces which were conducted in several
years, the health service records of migration, building societies' lists
of house sales, and information on the infrastructure of roads, railways
and settlements for example, are all digitally available7. What is
sought here is the means of putting these numbers together, as a
collection of images forming one picture of one place during a short,
twenty year or so period of time.
The most simple of spatial distributions to envisage are those captured
at single instances of time, and so it is these with which the examples
of the visualization of spatial distributions in this dissertation begin.
Much of the static spatial social structure is already known intuitively
to social scientists, if not in such great detail and with all places shown
in immediate relation to each other. The degree of complexity and
interdependence shown by the images in this work may also be new to
many. The dissertation then moves on to show changes in the
population over time in a single picture (Print XVII). Much of what
this shows about Britain will be unexpected, as it is only through the
methods employed here that such things can be seen. The way people
move about, day to day, and year to year, is visualized as streams
flowing through space. It must always be remembered that I am not
concerned with two hundred, or a few thousand people, but the
activities of as many as fifty million. The computer is used to handle
these vast numbers, not to produce more numbers, but pictures —
black and white, coloured, and, when required, moving.
Finally I can begin to produce images to depict the little that is known
about large numbers of people, which are totally different from
anything we would recognise in current practice. A notional surface is
proposed where the distance between points is equal to how long it
would take to travel between them, upon which we can then drape
other distributions. It may soon be possible to create true volumes of
8
Introduction: Human Cartography
pattern and colour to depict the entire evolution of a single
phenomenon, for example unemployment at every place, every month.
The alternative is to cut through this distribution, collapsing all of
space to one point, to draw graphs over time, or all of time to a point,
to show a simple spatial pattern. Inevitably we ask, can we now
combine these disparate images and compare the evolution of one
thing with the flows of another and the distribution of yet others,
without collapsing reality into dimensions which cannot contain its
complexity?
Presented here is a methodology for studying relatively data rich
spatio-temporal distributions and their interrelations. This goes
beyond the accepted format of book chapters (containing a few tables,
perhaps a graph, or a coarse thematic map) on each of a small number
of topics, with an overview chapter implying that everything is related
but that it's all very complicated. If it is complicated it is interesting —
so let's look at it, rather than repeatedly explaining away the simplest
points, tabulating and sorting the basic rates, or drawing yet more
examples of inappropriate poor quality choropleth maps (by computer
of course).
Images of recent British history are being created here which allow
[d] We use whatever information is accessible:
In this book, votes receive rather more emphasis than other
activities only because they have become the currency of
political sociology rather than because they are more "special"
or necessarily more legitimate than other activities. [Agnew
J.A. 1987 p.6]
8 [a] The fundamental cartographic questions are:
"What to map?" "How to map?" "What to do with the maps?"
These three questions sum up the main problems connected
with the mapping of population phenomena and statistical data
generally. Each question gives birth to a brood of lesser
questions, the lesser questions to a third generation, and so on.
The outlines of this genealogy will be traced in the present
paper.
I. WHAT TO MAP?
The offspring of "What to map? are (1) "What has been
mapped?" (2) "What can be mapped?" and (3) "What should
be mapped?" ... [Wright J.K. (ed.) 1938 p.1]
[b] Yet fewer and fewer people are asking these crucial
questions:
Eavesdropping in the conference bar, the cartographer's
chatter is of the virtuoso Macintosh rather than the question of
why and what we map. Are the mechanics of the new
technology so preoccupying that cartographers have lost
interest in the meaning of what they represent? And in its
social consequences? And in the evidence that maps
themselves can be said to embody a social structure? If
material efficiency is allowed to dominate the design and
construction of maps, we can see why the ethical issues tend
to pass unnoticed. The technology of Geographic Information
Systems (GIS) becomes the message, not just the new form or
medium of our knowledge. [Harley J.B. 1990 p.7]
[c] Questions can often be more important than answers:
It is surprising to learn that such a seemingly perverse world
view is embraced by modern physicists. In the words of John
Wheeler, one of the grand old men of physics, "No elementary
phenomenon is a phenomenon until it is an observed
phenomenon." By this, Wheeler means that the rise of
quantum mechanics has demolished the view that the universe
sits "out there" while we sit back and observe it. The kinds of
questions one asks — and the order one asks them in — has a
Introduction: Human Cartography
9
new questions to be asked, show different distributions to be
explained, the distributions that many social scientists know are there,
but which traditional cartography fails to depict, and hence to
explore8. There are also glaring patterns to be seen in the well trodden
census tables and government figures which have been ignored, before
we even begin to look for the more subtle or complex and detailed
relationships.
Visualization can be claimed to solve many of the fundamental
problems identified in studying spatial social distributions (Prints VIII
& IX). The fact that the way you subdivide the space and time you
choose to study can drastically alter the overall impression of your
results, suggests that there are a variety of different views to be
gained, and we should choose those which we wish to believe, in the
light of all possibilities. Here it is argued that previous numerical
profound influence on the answer one gets, and on the world
view one builds up. [Rucker R. 1984 p.193]
[d] What are we doing this for?:
The analytic power to order data has potential equally for
control or liberation. It is all a matter of questions asked and
interpretations made. [Taylor P.J. 1991 p.30]
9 [a] Many eminent cartographers have called for a change
of approach:
A second challenge requires a greater effort by cartographers
to escape from the constraints of euclidean space and to
exercise more imagination and originality in producing maps.
Barbara Petchenik (Chapter 3) makes a plea that we "... move
our consideration from the domain of rationality and analysis
to an exploration of the domain of synthetic intuition". The
map is a designed object and in our concern with the
"scientific basis" of cartography in recent years we may have
lost sight of the need for more imaginative design. Here
cartographers may have to learn from graphic arts. An
increasing number of thematic maps are being produced by
graphic artists, not by cartographers.
Part of the reason for this is that cartographers are a fairly
conservative group and are still largely prisoners of euclidean
space. Kishimoto (1980) recently drew attention to this fact.
We are increasingly coming to accept the essential difference
between the thematic map and the topographic map but have
not yet accepted that locational accuracy is not always a basic
requirement of the thematic map. We can more effectively and
imaginatively map other "spaces" and give more emphasis to
map content than to geographic location.
Here again, cartographers should take note of the work of
psychologists like Arnheim (1975) and Norman and
Rumelhart (1975) who argue that what a cartographer would
regard as a "distortion" of the "real" euclidean space may in
fact lead to an increase in map clarity. Arnheim uses the
example of the map of the London underground to show how
deliberate distortion of spatial reality can aid the map user and
Norman and Rumelhart demonstrate that when people are
asked to recreate floor plans their drawings rarely represent
euclidean reality. Mills (1981, p.95) comments:
These studies show that human memory is not geared to
produce accurately spatial layouts, even of places with which
one may be very familiar. Instead, people's maps drawn from
memory often distort the shapes and interconnections between
spaces, making them more straight and symmetrical than they
really are, thereby serving to highlight functional, not physical
reality.
If this is true of relationships on maps dealing with euclidean
space then it would be reasonable to assume that it would be
equally if not more true of thematic maps. If the gestalt
psychologists are right then "... the most effective maps may
be those which distort objective realism in order to facilitate
the calculation process" (Mills, 1981, p.95) and "creative
distortion" may be necessary to improve communication.
[Taylor D.R.F. 1983 p.288]
[b] Computer cartography could aid, rather than set back,
this new approach:
Cartography in the information age will centre about a
multifaceted model of geographic reality, the spatial data base.
The challenge facing cartographers will be to devise the
theories, methods, and techniques needed to collect, load,
manage, and transform the data items into useable
information. The new cartographic process will form a
continuum of information flow that can be described in terms
of the various generic functions of a spatial data processor.
Technological advances will provide the potential for
collection of vast quantities of basic spatial data. The
distillation of the data into descriptions of geographic reality
that we can understand will require a conception of the
abstract modelling process used by a human to comprehend
spatial entities. Processes to manipulate the data must bridge
the gap between a user's perception and a computer's
Introduction: Human Cartography
10
solutions to this problem often encouraged even worse symptoms to
emerge. The philosophy adopted here, is to ask how you amalgamate
individuals rather than subdivide society. A logical unit of analysis
does exist for the study of spacetime in human geography — it is a
human life. As yet we have very little information on single people,
but, at least from the census, the data is given at a resolution whereby,
for national pictures what is produced would hardly appear any
different from pictures drawn with the benefit of such information.
For spatial data with a slightly less fine temporal resolution, what we
have can appear as the full picture would, but somewhat blurred. As
long as methods which depend critically on the spatial and temporal
units (or units which would distort any method) are not adopted, such
problems may well be circumvented9.
Social science does need maps; but the maps that are currently drawn
in its name, apart from often being bad examples of physical
geography's cartography, are bad social science. They make
concentrations appear where they are not, and dissolve existing
patterns. They rarely portray anything but the most simple of spatial
distributions, certainly not spacetime evolution, or the interrelation of
a dozen different influences (Print X). Here some of the particular
solutions to mapping that social science requires are given. It is hoped
that while a new methodology is being explained an alternative picture
of Britain will develop through the subjects covered.
representation of spatial reality. Automated cartography will
expand from its robot draftsman roots to a spatial information
system using artificial intelligence techniques to allow the
cartographer not only to produce cartographic products but
also to convey the user-designed view of geographic reality.
[Guptill S.C. & Starr L.E. 1984 p.14]
[c] What ever we do, we must always keep the basic reason
we draw maps in mind:
If the student already carries in his mind's eye the image of a
base map showing the boundaries of the administrative units
by which statistics are tabulated, he may derive from a table of
statistics a hazy idea of the form of a distribution. If no such
picture is present in his mind he can gain no such concept
whatever without the aid of a map. How many of us could
picture the distribution of population in our own state by
studying the census tables alone? Hence statistical maps are
tools for the discovery of new truth. [Wright J.K. (ed.) 1938
p.16]
Chapter 1: Envisioning Information
11
Chapter 1: Envisioning Information
We must create a new language, consider a transitory state of new illusions
and layers of validity and accept the possibility that there may be no
language to describe ultimate reality, beyond the language of visions.
[Denes A. 1979 p.3]
1.1 Visual Thinking
Envisioning means bringing into the condition of vision, making
visible, to enable visualization. It is what this thesis practises. Here the
theory behind it is presented. Envisioning is about giving information
to people who can see10. I argue that there are dramatic potential
advantages in using visual images to allow people to unravel the
spatial patterns in complex social structures (Muehrcke P.C. 1969, Arnheim R.
1970, Bertin J. 1981, 1983a, Marr D.
[c] The ideas visualization are easily applied to mapping:
10 [a] The value of visualization was also appreciated in
the past:
Often we must deal with conditions where no known
equations will connect our experimental results and where a
mere tabulation of figures will not yield the desired
information without much tedious study. The well recognized
superiority of any graphical representation over an equation or
table in conveying a clear impression to the mind of the way
in which a set of variables is related will often in itself be a
sufficient justification for the use of this type of chart. [Peddle
J.B. 1910 p.98]
[b] Visualization is a way of doing research, not just a
technique for presenting results:
In conclusion, visualization should not be viewed as the end
result of a process of scientific analysis, but rather as the
process itself. More than simply the application of techniques
for displaying data, visualization can be used as a paradigm
for exploring regions of untapped reservoirs of knowledge.
The "Knowledge Navigator" discussed by Apple's John
Sculley in his book Odyssey, is, in some sense, the perfect
model for the visualization process. Jim Blinn has used this
process for over a decade in attempting to simulate planetary
exploration by modelling Voyager's journey through the solar
system. Visualization is not new, but its awareness by the
general scientific community is. [Wolff R.S. 1988 p.35]
Scientists confronted with conceptually difficult processes plot
numbers on graphs to "see" what they mean, often under the
assumption that even bad graphs may provide more meaning
than tidy lists of numbers. Normally we need all the insight
we can get, and graphics are closely associated with the
intuition that lies behind so much creative inquiry. The
computer business increasingly uses pictorial output. Graphics
are used in basic research in engineering, mathematics,
physics, and other fields as a means of visualizing complex
formulas and models. The map, as a graphic form of symbolic
representation, also serves the primary function of
visualization in scientific research (Figure 30).
It appears that maps (or graphics) are not designed, intended,
or well suited for precision work. One should not expect
detailed statistics from mapping. The impact of the map is
more often of greater importance than the information. Maps
serve well the need for a general picture of the nature of a
distribution or the relationships between several distributions,
at least when the patterns are not too large. [Muehrcke P. 1972
p.38]
[d] Most importantly, visualization guides and inspires us
to see new questions to ask rather than merely repeat old
answers:
This elusiveness is not so much a particularity of perception as
it is characteristic of cognition in general. The privilege of
observing everything in relation raises understanding to higher
levels of complexity and validity, but it exposes the observer
at the same time to the infinity of possible connections. It
charges him with the task of distinguishing the pertinent
relations from the impertinent ones and of warily watching the
Chapter 1: Envisioning Information
12
1982, Tufte E.R. 1990).
Although I have used computers a great deal in this work, I am not
going to concentrate on the mechanics of getting information into the
machine, but how you get it out to people (Prints XI & XII). To
communicate with people you must involve their senses of sight or
hearing, the former transmitting far more information than the latter.
Language, along with music, the most sophisticated use of hearing, is
an excellent means of conveying ideas and thoughts, but cannot
present a large amount of information in a structured form at speed11.
When you look out of the window you can see a great deal in an
instant. The mind has an extremely powerful system for processing
imagery which can instantly analyse a pattern of colours, of light and
shade, and know that these are trees, houses or people out there. How
long would it take to describe all that you can see in words? Yet we
still have to argue, that in the study of societies, there are many things
which cannot be eloquently described in words or succinctly captured
by equations.
This very thesis is only held together by its text. We have come a long
way with our little symbols, which, after all, exist only because they
effects things have upon each other. [Arnheim R. 1970 p.62]
11 [a] To put the argument somewhat more technically:
Visual displays of information encourage a diversity of
individual viewer styles and rates of editing, personalizing,
reasoning, and understanding. Unlike speech, visual displays
are simultaneously a wideband and a perceiver-controllable
channel. [Tufte E.R. 1990 p.31]
[b] Why does our visual system work so well?:
Human visual perception is performed by the most complex
structure of the known universe, the visual cortex, that
contains at least 1010 neurons, where each neuron in average
contains 104 synapses (gates). This enigmatic processing
network can perform prodigious feats when properly coupled
to the visual stimuli. [Papathomas T.V. & Julesz B. 1988
p.355]
[c] And how does it operate so quickly?:
Humans can recognize unexpected objects in around 100
neuron-firing times. [Plantinga W.H. 1988 p.56]
[d] Our vision has evolved over a long time to become this
powerful:
Average human beings can be beaten at arithmetic by a one
operation per second machine, in logic problems by 100
operations per second, at chess by 10,000 operations per
second, in some narrow "expert systems" areas by a million
operations. Robotic performances can not yet provide this
same standard of comparison, but a calculation based on
retinal processes and their computer visual equivalents
suggests that a billion (109) operations per second are required
to do the job of the retina, and 10 trillion (1013) to match the
bulk of the human brain.
Truly expert human performance may depend on mapping a
problem into structures originally constructed for perceptual
and motor tasks — so it can be internally visualized, felt,
heard or perhaps smelled and tasted. Such transformations
give the trillion-operation-per-second engine a purchase on
the problem. The same perceptual-motor structures may also
be the seat of "common sense," since they probably contain a
powerful model of the world — developed to solve the
merciless life and death problems of rapidly jumping to the
right conclusion from the slightest sensory clues. [Moravec H.
1989 p.177]
Chapter 1: Envisioning Information
13
were easy to scratch with a stick or form quickly with lips and tongue.
Did our ancestors develop the most efficient means of communication,
or did they make do with what was possible? Communication, which
holds a society together, is still developing. We are only beginning to
realize what there is to see.
The spatial structure of British society, which is envisaged in these
pages, is made up of far more than a few large regions which can be
named, and divisions which can be measured. Social structure has a
texture to it, a fine pattern, an elaborate organization, not unlike the
pattern of chaos (Print XIII). Such intricate structures cannot be
captured by writings which say which towns are supposedly faring
worst, or coefficients that tell of a simple widening of the divisions. If
we want to know the how and why of things, the best we can do,
before letting our imaginations take over, is to take a look at what we
are talking about.
We depend on vision, we think visually, we talk in visual idioms and
we dream in pictures, but we cannot easily turn a picture in our mind
into something other people can see. An artist will take days to paint a
[c] Researchers may need to learn to use graphics more:
12 [a] But we may not realise that we have never been
taught how to see:
The lack of visual training in the sciences and technology on
the one hand and the artist's neglect of, or even contempt for,
the beautiful and vital task of making the world of facts
visible to the enquiring mind, strikes me, by the way, as a
much more serious ailment of our civilization than the
"cultural divide" to which C.P. Snow drew so much public
attention some time ago. He complained that scientists do not
read good literature and writers know nothing about science.
Perhaps this is so, but the complaint is superficial. It would
seem that a person is "well rounded" not simply when he has a
bit of everything but when he applies to everything he does
the integrated whole of all his mental powers. [Arnheim R.
1970 p.307]
Having established this high-bandwith communication link
from the computer's vast computation power to the human
brain, we are ready to look at ways of translating scientific
data into pictures. We also need to educate scientists to the use
of computer graphics. I have known many scientists who did
not believe that mere pictures could help them understand
their research. So they continued to burn up hours of
supercomputer time (with over a hundred million calculations
per second) and assumed that they had absorbed the complete
result by studying output numbers. But once scientists begin
to use computer graphics, they wonder how they ever got
along without them. They find those "mere pictures" not only
give them a firmer understanding of problems and provide a
means of more easily explaining their work to colleagues but
quite often open up whole new areas of research through
observation of some subtle feature in an image. [Prueitt M.L.
1987 p.4]
[b] Visual skills can, however, be enhanced:
Researchers increasingly are becoming aware that people need
to be educated graphically in order for them to comprehend
often increasingly complex graphics. It frequently has been
suggested that graphicy is one skill that is generally not
sufficiently developed thoughout our educational system as
are numeracy, articulacy and literacy (Balchin, 1976). This
research incorporated the concept of learning effects in order
to judge its impact. [Halliday S.M. 1987 p.63]
[d] The advantages, once visualization is accepted as a
method, are numerous:
Visualization is often opportunist; that is, an interpreter will
not always have a good idea of exactly which attributes are of
principal interest and indeed may often specify conflicting
aims. It may also be advantageous to generate initial
representations for large quantities of data automatically and
Chapter 1: Envisioning Information
14
single portrait. Suddenly, just as the last generation was given the
camera, we have received the computer, which can turn a huge
amount of data into pictures — snapshots of our society. In the future
we will be able to speak visually. For now we still have to learn
how12.
1.2 Pictures Over Time
Visual communication was possible in the past, but enormously time
consuming and often limited by poor materials and little information.
These limitations led to restricted experimentation and strongly
established conventions as to the right way to paint. Our first
permanent communications were cave paintings and our first textual
scripts made of pictures. Today the computer window system which
abounds with icons is the modern cave wall (Print XIV); we have
rushed forward to the beginnings of visual communication13 (Peddle J.B.
1910, Riggleman J.R. 1936, Royston E. 1956, 1970, Lockwood A. 1969, Herdeg W. 1974,
Feinberg B.M. & Franklin C.A. 1975, Beniger J.R. 1976, Beniger J.R. & Robyn D.L. 1978).
The first maps were drawn on clay. They were invaluable objects for
the control of territory or the projection of religious truth about the
quite independently of analyst interaction. [Robertson P.K.
1990 p.121]
[Phillips R.J. 1989 p.24]
13 [a] The subject matter of the earliest maps is
interesting:
[c] It is the increased availability of information which
necessitates new visual solutions:
Chinese literature tells us that maps were being used in the
East as early as the 7th Century BC, while the earliest
surviving examples of maps are clay tablets found at Nuzi, in
northern Iraq. Believed to be from the period circa 2,300 BC,
they show rivers, settlements, land-holdings and hills.
[Brannon G. 1989 p.38]
The early problem of spatial organization grew with the
amount of data to be analysed. Multiple measurements
proliferated with the Industrial Revolution in Europe, which
brought a spate of new measuring devices: the air and water
thermometer (c. 1590), micrometer (1656), weather-clock (c.
1660), mercury thermometer (1714), etc. Spatial organization
of multiple measurements was achieved in two competing
forms, coordinate systems and tables, which dominated
quantitative graphics in the 17th and 18th centuries ...
[Beniger J.R. & Robyn D.L. 1978 p.2]
[b] Other forms of graphical display of information are
much younger than cartography:
Maps have been used for more than 5,000 years whereas most
other forms of graphic information date from the eighteenth
century — graphs are a surprisingly modern discovery (Tufte,
1983). The earliest use of pictures is, of course, long before
the first map, but perhaps we should exclude pictures from our
definition of graphic information: pictures do not share the
geometrical or conceptual structure of maps and graphs.
[d] The popularity of visualization has been cyclic:
In mathematics, it is considered the most flagrant gauchery to
use a diagram. "Graphics" is thought to be an inflated title for
"mechanical drawing". In fact, all the intrinsically visible
subjects; geography, graphics, and geometry, are suspected of
being really grade school subjects, fit only for brains that are
Chapter 1: Envisioning Information
15
world. Maps were accumulations of innumerable stories, reams of
parchment and hordes of figures. Spatial information about the world
and its people has always been at the forefront of visualization. As
map-making developed into the art of cartography, rules were
formalized and conventions defined (Peuker T.K. 1972, Friis H.R. 1974, Bertin J.
1978, Howe G.M. 1986c). Cartography is no longer a major discipline or
even an important aspect of geography. Its modern tools can be used
by children (Print XV) and its conventions are being challenged as
stale.
The nineteenth century saw the strongest moves, in science, against
pictures. The graphs, which instruments traced onto paper, were
immediately turned into supposedly more accurate and readable
tables. Diagrams were for people without mathematical imagination.
Nevertheless statistical graphics did germinate in these surroundings.
The graph, bar chart and scatter diagram were invented. These, too,
were formalized, rules for their construction produced, while their
supposed subservience to more advanced methods was made clear.
Now the cycle has come round again, and there is a new breed of
statisticians who see visualization as paramount (Fienberg S.E. 1979, Young
F.W., Kent D.P. & Kuhfeld W.F. 1988, Buja A., Asimov D., Hurley C. & McDonald J.A.
1988, Crawford S.L. & Fall T.C. 1990, Hirsh N. & Brown B.L. 1990).
still undergoing biological maturation and whose harmfully
misleading approach will have to be undone later. [Bunge W.
1968 pp.31-32]
14 [a] The history of computer graphics is short, but
eventful:
Computer graphics started with the display of data on
hardcopy plotters and cathode ray tube (CRT) screens soon
after the introduction of computers themselves. It has grown
to include the creation, storage, and manipulation of models
and images of objects. These models come from a diverse and
expanding set of fields, and include physical, mathematical,
engineering, architectural, and even conceptual (abstract)
structures, natural phenomena, and so on. [Foley J.D., Dam A.
van, Feiner S.K. & Hughes J.F. 1990 p.1]
[b] Many milestones mark the way:
The scientific visualization going on today, Rosebush shows
us, has been going on for a long time. In 1964 Ed Zajak of
Bell Labs, who was a programmer animator, did a satellite
orbiting in space... [Neal M. 1988 p.9]
[c] The discipline is now reconstructing its history:
The concept of scientific visualization reaches back into
prehistoric times when a caveman drew a map of his local
environment on his cave wall. In antiquity, legend tells us that
Archimedes was slain by a Roman soldier while visualizing
figures sketched in the sand. In this century, chemists began to
understand the structure of matter and satisfied the need to
visualize molecules with wooden and plastic models.
Visualizing data and concepts is not new, nor is it computer
dependent.
In the computer age, we have progressed through line-printer
output, contour plots, etc., to more sophisticated techniques.
Yet scientific visualization has only emerged as a technology
in the last two or three years. [Rosenblum L.J. 1990 p.209]
[d] And beginning to realise where the future lies:
Structure, however, has been left behind in the race to create
more and more realistic images. While photo-realism is eye
catching, it is not necessarily informative. One of the great
potentials of computer graphics is to provide a vision of what
we might not otherwise be able to see in a photograph or real
Chapter 1: Envisioning Information
16
Computer graphics in the 1960s changed the picture14. Swirling
images were produced from the most simple formulae (Davis P.J. 1974,
Mandelbrot B.B. 1983, Andrews D.F., Fowlkes E.B. & Tukey P.A. 1988). It was
immediately obvious that reading an equation told you little about
what secrets it held. Before computer graphics, people were blind to
the behaviour of relationships they thought they could easily
understand (Print XVI). The programmers then went on to render
reality — creating photographs from numerical descriptions of what
we can already see around us. They now turn their efforts to the
possibilities of rendering abstract worlds.
Visualization grew out of all of this, but a similar philosophy
underlied much of it. Graphics have come in and out of favour in
cycles through time (Pickett R.M. & White B.W. 1966, Baecker R.M. 1973, Neal M.
1988, Nielson G.M. 1989, Anderson G.C. 1989, Voegele K. 1990a). Their resurgences
usually have more to do with taking advantage of new printing
technologies (Figure 1) and the availability of more abundant
information, than a basic understanding of their value. What is
required now is to harness the potential of the computer, that both
provides and renders new information, for a deeper knowledge.
life. [Dooley D. & Cohen M.F. 1990 p.307]
15 [a] The technical term visualization was sprung upon
the scientific community in 1987:
Visualization is a method of computing. It transforms the
symbolic into the geometric, enabling researchers to observe
their simulations and computations. Visualization offers a
method of seeing the unseen. It enriches the process of
scientific discovery and fosters profound and unexpected
insights. In many fields it is already revolutionizing the way
scientists do science. [McCormick B.H. et al. 1987 p.3]
transcends application and technological boundaries. [DeFanti
T.A., Brown M.D. & McCormick B.H. 1989 p.12]
[c] Things have changed very quickly:
Images and animations are no longer merely illustrations in
science and engineering — they have become part of the
content of science and engineering and are influencing how
scientists and engineers conduct their daily work. [Foley J.D.,
Dam A. van, Feiner S.K. & Hughes J.F. 1990 p.22]
[d] Recognition of this revolution is increasing:
[b] Grand claims have been made of the philosophy:
Computer graphics and image processing are technologies.
Visualization, a term used in the industry since the 1987
publication of the National Science Foundation report
Visualization in Scientific Computing, represents much more
than that. Visualization is a form of communication that
Computing imaging is not new, but the term, "scientific
visualization", is justified as an indicator of an important new
phase of development and a novel alignment of several
computational technologies. [Haber R.B. & McNabb D.A.
1990 p.74]
Chapter 1: Envisioning Information
17
1.3 Beyond Illustration
Visualization is a way of working, a methodology. Not only does it
differ from the use of script
Drawfiles are
Hertford
used to create the
and figures — reading and
shire
illustrations in this
calculating to understand
thesis.
Greater
—
but
also
from
A library of
London
Berkshire
procedures was
conventional
graphics
written specifically
to produce these
which aim to illustrate.
Surrey
files.
Illustration is used to
Drawfiles are a
West
convey a discovery from
sophisticated type
East
of computer record. The record contains a list of objects,
one person to another
which can themselves be a list of objects.
which was found by other
Object can include relationships (with other objects),
information (data from other files) and:
means. Visualization is the
text - of a particular font, size, style and colour;
transformation of numbers
sprites - a pixelmap image (raster graphics);
paths - lines, curves and
into pictures in order to see
shapes (vector graphics).
what a mass of figures
In the example above the Greater London "object" has
could not tell us, let alone
been shrunk. In the drawfile it is tagged with its
identification as County no.1 and the relevant boundary
inform others. Visualization
date (1981). Making up the group is its perimeter, the
river Thames, and any islands in the river. All aspects
is how discovery is made.
of scaling, appropriate placement and hyphenation of
names and colouring are automated. Any feature of an
The
method
is
the
object or group of objects can then be edited interactively on the screen - as has been done here.
message15 (McCormick B.H. et
Once a drawfile representing a particular geography has al. 1987, Prueitt M.L. 1987, Winkler
been created, it can be transformed and additional
information incorporated. For example, the places could K.H.A., Chalmers J.W., Hodson
be represented by faces instead of polygons; re-coloured
S.W., Woodward P.R. & Zabusky
and then merged with another drawfile.
N.J. 1987, Wolff R.S. 1988, Forer P.,
Figure 1: Creating the Graphics
Poiker T., Penny J. & Deeker G. 1990,
Robertson P.K. 1990, Nielson G.M., Shriver B. & Rosenblum L.J. (eds) 1990, Foley J.D.,
Dam A. van, Feiner S.K. & Hughes J.F. 1990).
Essex
Kent
[e] Its value to geography was recognised ten years ago:
Visualization is important, if not essential, in human thought.
Visual thinking is not exclusively an artistic talent, but is
constantly used by everyone. It pervades all human activity,
from abstract and theoretical to everyday and down-to-earth.
Yet development of visual thinking has been immobilized by
society and education. Even geographers, in spite of their
historic association with maps and mapping, fare little better.
They seem to have given up on maps at the very time that
other disciplines were discovering the power of graphics and
documenting the physiological basis of visualization. This is
particularly ironical since the geographic map may well be the
most highly developed of the various graphic media that have
been conceived in response to the need for visualization.
Geographers are fortunate to be so closely associated with
such a powerful, sophisticated tool of thought (something
practitioners of other disciplines point out repeatedly). Yet
incredible as it seems, geographers have not taken anything
Chapter 1: Envisioning Information
18
Most visualization research today relies on huge quantities of
numerical information. Before you have such information, you can
only write about what you think is happening. Now you have counted
what is happening, who does what, who has what — how do you
understand it? How should we analyse the information? Statistical
analysis gives you single figures, averages, correlations, parameters of
assumed relationships, probabilities, and so on. They are only of use if
you know exactly what you want, but knowing what questions to ask
is much harder than finding the answers. Social science is not about
defining and testing simple hypotheses; it is about understanding
complex societies.
There are many ways to begin studying society. All involve some
form of ordering, of which the spatial is the most common. Having
projected our figures onto the plane in some way, we can paint
pictures of this ordering and see what patterns emerge, what structure
there is (Print XVII). These patterns usually turn out to show complex
and subtle relationships that tax our mental capabilities to comprehend
near to full advantage of their traditional relationship with
maps. [Muehrcke P. 1981 pp.37-38]
16 [a] We need to be careful in deciding what is forming
the patterns we see:
The boundaries between shadings on a choroplethic map tend
to dominate the visual impact of the representation, because
sharp visual contrasts occur along these lines. Map-readers
tend to assign significance to these boundaries and, as a result,
often assume that they designate breaks in the configuration of
the statistical surface. Since this seems to be the normal
reaction among map-users, the map-maker is obliged to use
generalizations in which there is a concurrence of boundaries
and surface breaks. [Jenks G.F. & Caspall F.C. 1971 p.229]
[b] We have to decide how we want to visualize what we
are studying:
Yet the problem of devising a standard set of eight shadings
for the maps was most troublesome. There were three initial
requirements: first, that the shadings should be smoothly
graded; secondly, that the class status of any area should be
readily identifiable on inspection; and thirdly, that the shading
specification could be applied by draughtsmen working
independently in different cartographic departments without
too much loss of comparability. The first two requirements
proved to be almost incompatible. Smooth grading could best
be achieved by using either a graded series of dots, or a
graded series of lines with a constant direction. Neither of
these produced visually acceptable results and the recognition
of class status proved to be extremely difficult with both
systems. [Hunt A.J. 1968 p.7]
[c] The debate over the simple shading of choropleth
[patch or block] maps continues:
Nowhere is this debate between technical constraint and
effective communication clearer than in the discussion over
continuous shading. Tobler (1973) pointed out that digital
techniques could remove the need to establish a finite number
of levels of shading in making a choropleth map, as it is
technically possible to crosshatch each area with a density of
lines directly proportional to the value of the mapped attribute
for that area. Evans (1977) took the opposing position that
while the need for a finite number of levels can certainly be
regarded as the consequence of technical constraints in
manual cartography, it also has a distinct and legitimate
function in communication. [Goodchild M.F. 1988 p.313]
[d] Use of colour is not necessarily always an added
advantage:
Observer performance experiments are conducted to study the
merits of the proposed color methods. The results of the study
show that observers performed better with a linearized gray
scale than with the newly-developed LOCS [Linearly
Chapter 1: Envisioning Information
19
and explain. This is not a bad thing — stretching the mind forces the
imagination. The visual methods I am discussing take hundreds of
pages full of tables of thousands of digits, and turn them into a single
picture with little loss of detail, in order to see what there is to see. In
terms of storage, most of the pictures in this dissertation required more
disc space individually, than the entire (typeset) text.
Illustration is to clarify — to make clear, pure or transparent.
Visualization does not aim to see through our information, it aims to
see into it. Methodology is about transforming reality to fit inside
particular conceptions. The more we simplify, the more reality is
blurred. Turning people and the events of their lives into numbers is
bad enough. Throwing away almost all of those numbers is worse, and
yet this is what we must do, in one elaborate form or another, if we are
to try and understand without images.
1.4 Texture and Colour
Optimised Colour Scale] at a statistically-significant level of
confidence. They also show that observers performed better
with the LOCS than with another colour scale (the heatedobject scale), but at a non-significant level of confidence.
[Levkowitz H. 1988 p.v]
17 [a] The general appearance:
When it comes to the more important "gestalt" effects of the
various decisions all considered together in a final map
presentation, our ignorance is staggering. Before map data,
map elements (symbols), and map users can be functionally
and most effectively integrated into the geographic
information system, it will be necessary to satisfy the need for
research in which the simultaneous interaction of all mapping
variables is determined. [Muehrcke P. 1972 p.49]
[c] We know a little of the mechanics of vision:
Sharpness falls off so rapidly that at a deviation of ten degrees
from the axis of fixation, where it is at a maximum, it is
already reduced to one fifth. Because retinal sensitivity is so
restricted, the eye can and must single out some particular
spot, which becomes isolated, dominant, central. [Arnheim R.
1970 p.24]
[d] Some aspects can be mimicked on a computer screen ,
for example using an ...:
Optical fish-eye window. Information in the window is
compressed like the image of a convex mirror. [Card S.K.,
Pavel M. and Farrell J.E. 1985 p.240]
[e] Other research has provided explanations for some of
the mechanisms through which vision may operate:
[b] Our ignorance is being realised again in visualization:
In the excitement over the obvious benefits of scientific
visualization, few questions have been asked about the nature
of perceived information and how well the human visual
system actually performs. Because visualization is a new,
emerging discipline, the lack of structure is not surprising, but
their development is necessary and offers significant research
opportunities. [Rosenblum L.J. 1990 p.211]
Our conclusions about the medium follow the research
findings, but they also make sense for other reasons: for one
thing, the medium's limited spatial extent fits in neatly with
how we might expect visual perception and imagery to have
developed over the course of human evolution. That is, the
medium presumably evolved to process information from the
sense organs, which means that it only needs to be large
enough to handle the arc subtended by the eyes. And because
the eyes are spread apart horizontally — as is, presumably, the
spatial medium they feed — they have a greater horizontal
scope. [Kosslyn S.M. 1983 p.71]
Chapter 1: Envisioning Information
20
If we are to envisage information we must first know what can be
seen as well as what there is to see. To decide how to turn numbers
into pictures we must know what pictures can contain and what is seen
in them. The most simple pictures are constructed of pure black and
white from basic geometrical shapes (Bachi R. 1968, Hunt A.J. 1968, Tobler
W.R. 1973b). What they contain, what the eye searches for, is pattern —
from order, repetition, grouping and texture16. What the eye then does,
is to find breaks in that order, discover inconsistencies while ignoring
irrelevancy. The eye does this because that is what it evolved to do,
and to do so extremely quickly.
The eyes are constantly engaged in focusing, panning and zooming.
They compare different sections of the image and home in on
interesting detail (the eyes are designed to scan continuously — they
cannot focus for long on a fixed point). The resolution of the eyes is
enormous, but far finer at the point on which they are centred. This
action can be mimicked and aided when pictures are electronically
produced, and can be instantaneously enlarged or reduced. Focusing is
[f] Yet only the most general understanding has been
gained:
Vision is therefore, first and foremost, an informationprocessing task, but we cannot think of it just as a process. For
if we are capable of knowing what is where in the world, our
brains must somehow be capable of representing this
information - in all its profusion of color and form, beauty,
motion and detail. [Marr D. 1982 p.3]
[b] The use of combinations of colour scales has been well
studied:
All three studies [Olson 1981, Mersey 1980, Carstensen
1981], in addition to this research, indicate that bivariate
mapping is a viable technique with numerous graphic design
possibilities and considerable flexibility. The main criteria is
that students have to be able to comprehend the mapping
technique [Halliday S.M. 1987 p.69]
18 [a] Colour is most useful, after position, to show
information in our pictures:
[c] A theory behind two-colour mapping developed
simultaneously:
A little reflection convinces us that in static displays nothing
does even nearly as well as right-left position and up-down
position for (a) producing impact, (b) facilitating synthesis of
impressions from groups of points, and (c) making fine
distinctions both possible and easy. Nothing is comparable,
but what comes closest?
In order, from stronger to weaker, we feel that the prominent
representatives are:
(1) colour (where the establishment of synthesis-prone
sequences deserves attention, as emphasized to us by W.J.
Dixon);
(2) shape (of characters or symbols);
(3) size (as in pseudo-perspective displays);
(4) contrast (e.g. more gray corresponds to more distant).
Colour deserves more attention than the others, especially in
view of the hope for synthesis. [Tukey P.A. & Tukey J.W.
1981 p.193]
If the relationships, as far as geographic location was
concerned, were essentially random, the resulting map would
show no particular tendency toward an areal concentration of
similar colors but, instead, would exhibit a patchwork of small
contrasting color blocks throughout the country. [Bureau of
the Census 1970]
[d] Experiments have been conducted to confirm many
assumptions:
In Olson's (1981) concluding experiment, experiment IV, it
was discovered that subjects found the spectrally-encoded
two-variable maps to be aesthetically appealing. While the
univariate maps were given a high rank for readability, the
spectrally-encoded bivariate maps were seen as more
innovative and interesting to work with. [Halliday S.M. 1987
p.15]
Chapter 1: Envisioning Information
21
The device used to print the colour
illustrations in this thesis was a Colourview
5912 plotter printer manufactured by
Calcomp in 1988.The plotter can produce
pixels of eight colours by overlaying sheets
of magenta, cyan and
yellow film with an A4
resolution of 2048 by
1600
pixels (3200 by 2048
when A3). A greater
range of colour is
possible by using
dithered patterns of the
eight colours actually
available. Text could
Figure 2: Printing in Colour
19 [a] The combination of three colour scales is
contentious:
It is far more difficult to distinguish the amounts of the three
primary colors painted simultaneously onto a point in space,
but it is possible (barely possible) to do so. Therefore a crude,
but effective, way exists for displaying three functions of three
independent variables. [Staudhammer J. 1975 p.183]
[b] Most people are taught to distinguish the primary
colours as yellow, red and blue:
In the psychological realm, color vision is based on a few
pure, elementary qualities, by no means necessarily or simply
related to the physiological types of receptor. Just as perceived
shapes are more or less complex elaborations of simple
shapes, so color patterns are seen as elaborations of the
elementary, pure qualities of yellow, red, blue. Here and there,
these qualities are encountered in their purity, but most of the
time there are mixtures, which are understood perceptually as
combinations of the underlying primaries. Some of these
combinations are sufficiently precise in themselves to function
as visual concepts in their own right, e.g., orange, green, or
purple. In the system of colors, as we find it applied, for
example, in painting, these secondary concepts serve as
transitional links between the primaries, which are the
fundamentals of the system. It is a heirarchic system, similar
to that of traditional logic, in which a multitude of more
particular concepts derives from a basic few, thereby creating
an order, which defines the nature of each element through its
place in the whole. [Arnheim R. 1970 pp.30-31]
[c] Some have used the default red, green and blue guns of
the computer monitor:
Clearly, since a total of three dimensions are available in color
one of the simplest
attributes of vision, yet we
know very little about how
even it operates17.
Colour is an invaluable
embellishment to basic
vision (Figure 2). It is
wrong to think of it either
as
adding
another
dimension
or
merely
supplying some further
minor tagging of data to
existing features of the
graphic. It alters the
character of the image
space it is possible to construct and transform much more
complex models. It is possible to express a trivariate
distribution by mapping each variable onto one of the
dimensions of color space. [Sibert J.L. 1980 p.214]
[d] Others have suggested the printer's screens of cyan,
yellow and magenta:
The collection of maps does not answer the question "what is
there at a given place?" But maps with the same scale can be
superimposed three by three. It is sufficient to transcribe them
on three different color films: cyan-blue, yellow, magenta-red.
[Bertin J. 1981 p.163]
20 [a] We form a generalize image of a picture:
Generalization, if you wish to call it that, occurs
spontaneously in all perception. Complex though a map may
be, the mind derives from it a simplified pattern. [Arnheim R.
1976 p.9]
[b] Often it is better not to generalize in cartography:
The collection of comprehensive maps does not involve
problems of generalization. Indeed, the eye immediately sees
a shape, whatever its complexity. Each map can thus carry an
impressive amount of data, as with the twenty-five million
buildings in Poland on a scale of 1/2 M (F. UHORCZAK).
But at the same time, the eye is free to focus on any level of
an ordered or quantitative variable and is thus free to
"generalize", that is to regionalize, as it pleases. [Bertin J.
1981 p.161-163]
Chapter 1: Envisioning Information
22
(Staudhammer J. 1975, Mersey J.E. 1984, Lindenberg R.E. 1986, Atkinson D.S. 1988,
Kumler M.P. 1988, Levkowitz H. 1988, Pham B. 1990, Hopgood F.R.A. 1991). Different
colours are perceived variably and convey loaded meanings on their
own, even more so in combination. The human eye is poor at focusing
on blue. Red and green do not combine to form reddish-green, and so
on. Colour adds another level, but not dimension, of complexity. The
careful use of colour can convey more of the depth of organization we
wish to comprehend. In particular, when used in bivariate and
trivariate mapping18 (Print XVIII).
When we only wish to show a single ordering, grey scale shading is
most effective and appropriate. To depict the bivariate relationships
between two variables in the same place and between places, colour
has been shown to produce effective keys when carefully employed.
This thesis makes great use of trivariate colour schemes to show the
combination of individual levels of up to three independent
characteristics (Print XIX). This is both a contentious and potentially
highly effective technique (Sen P.K. 1960, Dannatt L.K. 1981, Cowen D.J. 1984,
Olson J.M. 1987a, Halliday S.M. 1987, Dawsey C.B. 1989, Fels J.E. 1990). It has been
suggested that the printer's primary triplet of cyan, yellow and
magenta be employed (or the computer's red, blue and green)19. The
most intuitively appealing combination was found to be the painter's
red, blue and yellow — which fortunately also coincided with
Britain's major political parties' symbols.
1.5 Perspective and Detail
The most powerful ability of the eye-mind combination which is
employed by visualization is generalization (Tobler W.R. 1968, 1969a, 1989b,
Rhind D. 1975c, Card S.K., Pavel M. & Farrell J.E. 1985, Lavin S. 1986, Bracken I. & Martin
D. 1989, Gilmartin P. & Shelton E. 1989, Herzog A. 1989). The brain only ever sees
Chapter 1: Envisioning Information
23
and understands through constant physical generalization of the light
intensities which are measured by the retina. These are smoothed by
the mind to allow instant assumptions to be made, before more careful
inspection is undertaken20 (Prints XX, XXI & XXII). Such ability is
essential to our survival in everyday life; it was even more so in the
past. Through visualization we are utilizing one of the most finely
tuned pieces of evolutionary good fortune.
We live in a three-dimensional world, despite having essentially twodimensional vision. Perspective is the name given to the effect of
projecting a three-dimensional scene onto our two dimensional
retinas; we use it to try to reconstruct three-dimensional form.
Although we do have binocular vision, if you close one eye you lose
little feel for the three-dimensional reality. We generally only move
about in two-dimensions and, in fact, have a far weaker grasp of the
real three-dimensional world than we may imagine.
It is often claimed that expensive equipment which allows volumes to
be created and seen is at the forefront of visualization. Stereoscopic
vision, though, might not be as great an asset to visualization as it is
often thought to be in seeing the real world. Stereo vision works well
at gauging position when nothing is moving behind or in front of
anything else. Once things begin to move though, it becomes an
irrelevancy. In visualization, if we want things to move, then, through
animation, they move.
Animation can be used for much more than understanding threedimensional form. As the creation of a changing or moving image it
can add another level of sophistication to two-dimensional
visualization. However, like colour, it is not the same type of
dimension as the spatial. In animation things must change smoothly
and relatively slowly. If objects change their colour it can confuse; if
too much is happening we will not have enough time to comprehend
Chapter 1: Envisioning Information
24
it. Surprisingly, animation takes us back towards illustration. It
requires simplicity to work. Far more useful is interactive graphics —
moving pictures which the viewer controls. This is not only control
over how fast or slow or where the pictures move, but simultaneously
over what they contain and how it is presented. This is the next step in
visualization.
1.6 Pattern and Illusion
We do not think in a three dimensional geometry — many tests have
shown this (Parslow R., 1987). The geometry of visual thinking is
essentially two-dimensional. We also have a poor visual memory; we
remember what we extract from images rather than the images
themselves. Furthermore, the emotional overtones of colour are
[c] Some automated generalization can be useful:
The space smoothing techniques can be employed to
investigate the existence of geographical patterns of property
values. The generalization facilitates the recognition of
patterns because it appears to be true, as Holloway (1958,
p.386) suggests, that we do ".... high-pass filtering in our
'mind's eye'." [Tobler W.R. 1989 p.19]
[d] However, we no longer require expensive computers:
A personal computer with an appropriate display system can
be just about as effective as a larger system for our
visualization techniques and interactive when outfitted with a
suitable computation accelerator, such as the one we
described. [Wolfe R.H. & Liu C.N. 1988 p.29]
[e] Cheaper computers are often more useful nowadays:
21 [a] Cost was a problem in producing the colour prints
for this dissertation:
Another practical issue in displaying the data is cost. Using
color provides an important extra dimension in displaying the
complex data sets obtained. Currently, however, the cost of
publishing two-color plates in some scientific journals
represents more than half the cost of the laboratory computer
that controls the experiment, stores the data, and displays the
results. Therefore, the use of color figures (which can best
present the results) might be hard to justify. [Long M.B.,
Lyons K. & Lam J.K. 1990 p.138]
[b] Colour printing is still technically difficult as well:
Showing complexity is hard work. Detailed micro/macro
designs are difficult to produce, imposing substantial costs for
data collection, illustration, custom computing, image
processing, production, and fine printing — expenses similar
to that of first-class cartography (which, in the main, can be
financed only by governments). [Tufte E.R. 1990 p.50]
[c] Using colour with computers can be frustrating:
It is nearly impossible to get the same colour on two different
devices so do not produce systems that depend on that.
[Hopgood F.R.A. 1991 p.9]
Workstations, minicomputers and image computers are
significantly more powerful and effective visualization tools
than supercomputers. It is a waste of supercomputer cycles to
use them to convert model data into new pictures. Specialized
graphics processors are more cost-effective than
supercomputers for specialized picture processing and/or
generation. Workstations should be placed on the desks of
each and every researcher to give them immediate access to
local graphics capabilities. [McCormick B.H. et al 1987 p.9]
[f] Many leading researchers predict the demise of
monolithic machines:
McCormick says, "The visualization problem is not peculiar to
supercomputers in any sense. In fact, I don't expect the bulk of
[the visualization initiative] to be tied to them.
Supercomputers are in many ways dinosaurs, a dying breed."
[Frenkel K.A. 1988 p.113]
[g] A much cheaper future lies on the horizon:
Visualization of anything will become the norm in computer
uses. Specialized data display managers will migrate to chip
sets for both faster response and ease of user interaction.
[Staudhammer J. 1991 pp.42-43]
Chapter 1: Envisioning Information
25
perceived differently by different people. The colour blind cannot see
the full trivariate range.
Visualization can achieve a great deal, even when limited to static
two-dimensional images (Wong W. 1972, White R.D. 1984, Wood D. 1985,
MacEachren A.M. 1987, Simkin D.K. 1987, Kennie T.J.M. & McLaren R.A. 1988,
Buttenfield B.P. & Ganter J.H. 1990, Freeman S. 1991, Hartmann J.L. 1991, McAbee J.L.
1991). This goes against many of the embryonic tenets of the field, but
it is questionable how much they are guided by what is possible,
rather than what is desirable. Why use the illusion of three dimensions
if it adds so little information to an image while causing so much
confusion? Perspective views are pretty and still somewhat novel, but
not especially useful unless it is three-dimensional geometry in which
you are particularly interested.
Animation, like perspective viewing, is also not as invaluable as has
been claimed. You cannot hold a moving picture in your mind as well
as you can hold a static image, and comparison of two dynamics is
difficult. Animation can tell a story. Visualization, more often, allows
you to find a story to tell (Prints XXIII, XXIV & XXV). Much more
22 [a] The next major hurdle to cross involves allowing
people to paint images as fast as they can see them:
The unhappy thing about all this, of course, is that whereas I
have the ability (and we all have the ability if we're sighted) to
take images in at a fantastic rate, I have no ability to create
images with the same facility. This is a one-way street. On the
other hand, I can create language and symbols at about the
same rate I can take them in, which means I can create speech
at about the same pace that I can listen to it. So it is not at all
unexpected that for most of us language seems to be the main
carrier of our thoughts because that is the thing we can hear
ourselves saying and were conscious of its use. [Huggins W.
1973 p.37]
[b] We have always, of course, been able to create images
in our own minds:
Aristotle was one early commentator who gave a crucial role
to mental images, claiming that "thought is impossible without
an image" and "memory, even the memory of concepts, does
not take place without an image." [Kosslyn S.M. 1983 p.5]
[c] And have for a long time sought ways of showing what
we can see to others:
The coordinate approach grew out of the analytic geometry
developed by Descartes, Fermat, and other French
mathematicians in the first half of the 17th century. Descartes
himself was convinced that "imagination or visualization, and
in particular the use of diagrams, has a crucial part to play in
scientific investigation" [103, p.28]. [Beniger J.R. & Robyn
D.L. 1978 p.2]
[d] And now can use images to view social structure:
The general consensus in the scientific visualization field is
that a broad commonality exists among the visual needs of all
numerically intensive sciences. While users have applied this
computational environment to fields as diverse as
computational fluid dynamics, molecular modelling,
geophysics, and meteorology, we are keenly awaiting its
application to fields with a shorter history in numerical
computing, such as econometrics and the social sciences. Will
users from these fields find this environment appropriate for
their needs? [Upson C., Faulhaber T., Kamins D., Laidlaw D.,
Schlegel D., Vroom J., Gurwitz R. & Dam A. van 1989 p.41]
23 [a] We have to be able to visualize spatial social
structure if we are to begin to comprehend it:
The observed variation pattern in spatial data is often
extremely complicated. Geographical maps have traditionally
Chapter 1: Envisioning Information
26
importantly, with both animation and perspective views, you are
limited to producing very simple pictures if you are to be able to
understand them. Both ideas are included in this work and they
produce nice illustrations, but until the viewer can easily control what
is viewed, through interactive visualization, their utility is limited.
The use of colour greatly augments what can be seen in a twodimensional image. However, use of colour is expensive, and
duplicating these prints was not easy21. Colour can also add difficulty,
and invoke unintended ideas (good and bad, hot and cold, near and
distant hues — often used, intentionally, as such in conventional
atlases). In this dissertation colour is not included to make the pictures
prettier. I have used colour to include extra information in the image
and to show how to display more complex data sets. Often it is not
used (as it can be) to simplify understanding of the images, but is
added as a final embellishment to elaborate on how the complexity
continues as other facets of the social structure are connected (Print
XXIV).
1.7 From Mind to Mind
served the need for visualization in attempts at description and
explanation of spatial patterns. A logical goal of the
information display (mapping) process is to produce, as
efficiently as possible, the most effective graphic
communicator of distributional information. Image processing
entails the assimilation, manipulation, and analysis of
information which is given in spatial pattern form such as a
map. [Muehrcke P. 1972 p.53]
[b] To see the structure we must first decide how to draw
[c] And we must use machines if we are to cope with the
mass of information that has been collected:
In the GIS environment, visualisation techniques are
recognised as an invaluable system component, aiding in the
interpretation of spatially related phenomena and complex
data analysis that takes the GIS a step beyond two
dimensional polygonal overlay analyses. Many of the GIS
vendors are including this capability in their systems to help
cope in our understanding of the "fire hose" of data being
produced by contemporary sources such as satellites. [Kennie
T.J.M. & McLaren R.A. 1988 p.737]
it:
Recently we have witnessed a heightened interest on the part
of specialists of various branches of science, in investigations
connected with the problem of the graphic representation of
social, economic, and other spaces. Various methods of image
transformation under the graphic constructs of anamorphozy
[transformed images] [3,4,7,9,12] are used in these cases.
Such images, in our opinion, are promising in city-building
analysis, especially during the use of "time" scales for them.
[Tikunov V.S. & Yudin S.A. 1987 p.203]
[d] We must look outside of our own disciplines for
inspiration:
Perhaps then, the differences between maps and other forms of
graphic information are not as great as they appear. All types
of graphic information are different solutions to a common
problem: our limited capacity to remember unprocessed
information. By removing the limitations of short term
memory, graphic information allows us to do kinds of
thinking which are difficult or impossible in other ways.
Chapter 1: Envisioning Information
All information about places, concise enough to be
edited manually, was stored in "Comma Separated
Value" (CSV) files. These files can be read by many
applications on several computer systems, in particular
by spreadsheets - allowing complex manipulation to be
easily accomplished. An example of beginning of a CSV
file containing information used to create a drawfile of
counties is:
"$.GIS.Area.Ward.County.Sheet",1,64,4
"County Topology and Statistics"
"Number","Name","Residents","Neighbours","Neighbour"
"1981"
1,"Greater London",6713130,6,0,29,22,43,26,11
29,"Kent",1467079,5,0,1,21,43,22
22,"Essex",1474126,6,0,12,42,1,26,29
43,"Surrey",1004332,8,21,45,1,29,0,24,10,11
26,"Essex",1474126,6,0,12,42,1,26,29
11,"Buckinghamshire",567979,7,43,26,34,9,38,1,10 . . .
The first line gives the filename, number of tables,
number of areal units and fixed variables. The second
line describes the file, the third has variable names and
the fourth holds temporal information. This header is
followed by the relevant numbers and text for each
place. Notice that the records can be in any order and of
variable length. They easily edited individually as this is a
text file.
A library of procedures was written to manipulate these
files. In particular to allow any other application to read
and write to them, taking advantage of an interpreted
language which allowed the procedures to, themselves,
invoke routines from the applications which had called
them.
Figure 3: Recording the Places
[Phillips R.J. 1989 p.25]
[e] And recognise what has already been achieved:
That very ancient merger of Geography, Geometry and
Graphics still exists and, if anything, with increasing vitality.
Many breakthroughs still lie ahead. The map is the
geographer's laboratory. [Warntz W.W. 1973 p.85]
27
The argument in this
chapter has developed
from the initial desire to
allow people to convey
what is in their mind, in a
form others can see, to the
point where individuals are
able to see and paint their
own information22 (Figure
3). It is as if we had all
been mute, and suddenly
were able, with the aid of a
machine, to make sounds
— what sounds should we
make? In the past we made
sounds by knocking sticks
together, so we get the
machines to imitate those
noises. But surely, we
think, there is more (Huggins
W. 1973, Evans D. 1973, Mills M.I.
Chapter 1: Envisioning Information
28
1981, Kosslyn S.M. 1983, Farah M.J. 1988)?
Our vision has a much higher bandwidth than our hearing, far greater
scope for communication. We can see thousands of stars, watch
sunsets, view landscapes, survey half a million people in a crowd.
Naturally we begin to paint things by getting them to look like
recognisable objects, chaotic functions to look like mountain ranges or
an island archipelago, flowing energy to appear as running water. In
this work pictures are often based on natural things which have two
dimensional structure, from honeycombs and cobwebs to crowds of
upturned faces and flocks of arrows (Prints XXVII & XXVIII).
Here visualization is used to make millions of figures understandable
without massacring their meaning, without reducing them to tables,
graphs, crude maps or models. If we are to understand the structure of
society we must find ways of envisaging it23. This dissertation
demonstrates how large amounts of simple information can be shown,
and then goes on to increase the potential of the graphics by
conveying increasingly complex information.
I have a collection of thoughts and prejudices about British social
history over the last two decades. I am going to convey them to you
through the medium of images, rather than trying to persuade you with
words, convince you with tables, or confuse you with equations. I am
going to use government sample and census figures, rather than
newspaper cuttings or extended interviews. What I am doing may
appear subjective, but it is no more so than any other method. The
images shown should be very different from anything you have seen
before and probably not instantly meaningful.
The pictures shown here are of things which cannot be easily (or
adequately) described, discussed or modelled, and yet many people
who see them expect to understand them in an instant, even when they
Chapter 1: Envisioning Information
29
may fail to understand the long complicated narratives which explain
them badly, or the intricate mathematical models which could
represent them inadequately . If you want to know the shape of Britain
you look at a map. You can then go on to investigate rivers and
mountains, lakes and bays. It is shown, through this dissertation, how
you can look at the shape of British society with visualization and then
it is possible to discuss the implications. However, to know the shape
you have to look at the picture (Print XXIX), you cannot just describe
it in words.
You learn to see, as you learn everything else, from experience. Here,
we gain some experience of what there is to be seen, using the first
generation of machines able to paint such pictures. The charcoal stick
has come a long way.
Chapter 2: People, Spaces and Places
30
Chapter 2: People, Spaces and Places
The practices through which social structure is both expressed and
reproduced cannot be divorced from the structuring of space and the use of
spatial structures. Previously structured space both constrains and enables
the reproduction of social practices and social structure.
The social becomes the spatial.
The spatial becomes the social.
[Pred A. 1986 p.198]
2.1 Which People
I am interested in the lives of the people of Britain over the last two
decades. This is because I am one of those people. I have been
counted as a birth in 1968, a child in two censuses, as a migrant by the
Health Service, as a claimant of unemployment benefit, as a voter in a
general election, and so on. As I have been counted, so have been
millions of others. Surely all these numbers can be turned into a
picture of the people in the country in which I grew up?
What is it about these people that we wish to understand? Trivially, it
is who these people were, how old they were , what they did.
Fundamentally we ask what was the relationship between these
people, what was the structure of the society in which they lived? You
cannot have grown up in Britain in these decades without having felt
the weight of this structure, how it affected your life, defined your
opportunities, and altered your destiny. Whether you were male or
female, where you went to school... you knew it made a difference,
but you did not know quite what difference it made.
More importantly you could not have known the effect of the social
Chapter 2: People, Spaces and Places
A program was written to perform
conventional automated cartography to
modern computer standards. The program
combined information from geometry and
lookup data files with CSV text-files
describing the topology and other attributes
of places, to produce the
desired map as a drawfile,
which could then be
Oxford
manipulated further. The
shire
shading and names of places
was given in the textfile and a set of simple
rules applied to annotate the areas. Names
were split at spaces, hyphens,
commas, before the syllable
"shire" and wherever an
underscore had been inserted.
The text was then scaled to fit
within a rectangle enclosing the place and
centred.
31
structure on everybody
else. It is the relationship
between the gains of some
people, and the losses of
others,
which
would
escape
any
single
individual's
perception.
They may be able to
imagine some of it, but not
to picture all of it. Here I
attempt to show the social
structure; not to explain or
to understand it to any
great extent, but merely to
see what there is to be
understood, and how its
most obvious workings are
organised. To illuminate
the situation.
Figure 4: Drawing the Maps
It is only by first seeing
what you wish to comprehend, that you can begin to understand why
and how it exists. Just to create an image of the most simple
manifestations of the structure of the society that these millions of
people make up, is a difficult undertaking (Figure 4). The story that is
told should treat everybody's part in it as equally important, as all their
24 [a] Great Britain or the United Kingdom?:
It should be noted that Great Britain (England, Wales, and
Scotland) is not the United Kingdom (which includes
Northern Ireland as well). But, given the fact that Ireland as a
whole constitutes a separate land mass, that it was historically
governed as a colony of Great Britain, that the division of
Northern and Southern Ireland occurred only in 1922, and that
Northern Ireland itself contains but six counties — for these
reasons, we restricted the study to the single land mass of
Great Britain for which the requisite data were readily
available. [Massey D.S. & Stephan G.E. 1977 p.352]
[b] Divisions, boundaries and borders are a help and a
hindrance:
Few demographers define areal populations by statistical
analysis before proceeding to analyse them demographically,
but many of the more local boundaries for enumeration
purposes (e.g. enumeration districts, census tracts) are
delineated with demographic considerations in mind, and areal
populations are aggregates of these small units. Moreover,
among larger populations the use of administrative or
geographic boundaries for population analysis is not always
demographically undesirable, as many such boundaries have
real demographic significance, and some, as for example
between U.S.S.R. and China, between East and West
Germany and between Bangladesh and Burma, are real
demographic divides. [Clarke J.I. 1975 pp.2-3]
Chapter 2: People, Spaces and Places
32
lives should, at least in the way that their history is told, have equal
value. This is a picture book which begins that story.
Why was British society chosen as the subject of this work? That we
should study all the people in this place, and only the people of this
place, was a choice made from both the practicalities of the exercise
and the experience of the writer. That the line chosen divides land
from water is convenient, but not the reason for its imposition (Print
XXX). The line around this country divides the experiences of most of
its people from those outside. The sea may present no great economic
or political barrier any longer but it is still a very strong social one.24
It is argued the the boundary of Britain is a much stronger social
divide than those lines drawn within the state, whether between
Scotland and England, or inner and outer London. The internal
differences are more often seen and measured as just that —
differences. The divisions over the water separate political systems
which are hard to compare, standards and styles of living which are
outside the experience of most of Britain's inhabitants. Some of the
sharpest divisions are the closest; separating Northern Ireland from
Britain, the United Kingdom from Europe, although these are much
less in magnitude than inequalities further afield. Eventually it may be
possible to undertake this kind of study across those lines. But it must
be done at the local level, not used as another excuse for
amalgamation — creating the average English, Scottish or Welsh man
and woman (Print XXXI). Before we can begin to understand world
society, it is prudent to delve into our own, to see just what we are
comparing others with.
Chapter 2: People, Spaces and Places
33
2.2 Why Study Places?
If we are interested in the relationships between people, why should
we wish to consider places? It is not due to the ease with which we
can collect information about people in a myriad of places that we
should do this (Print XXXII). It is because, just as your place in time
so obviously constrains and determines your life, so your place in
space limits and creates the possibilities in your world. It is not the
actual position in space, as it is not the actual position in time which
does this, but who else shares that place, who else shares your time
(Howe G.M. 1986d, Johnston R.J. 1986a, Agnew J.A. 1987, 1989, Agnew J.A. & Duncan
J.S. 1989).
However, times and places are fundamentally different things. As we
live we must all share the same moment in time, but, in existing, must
be spread over space into different places. This diffusion of settlement
in space, juxtaposed with the concentration in time, defines the
dimensions over which experiences can differ. The organisation of
people over space, and through time, is what makes place important.
Constrained by the limits of time, people are forced to live close to
where they work. In cities they work together, but live apart. We used
to live together in villages and work apart in fields; now we live apart
in suburbs and work together in offices. This spatial organisation
reflects the need for people to live together and the wishes of some to
be apart. As they are rewarded unequally at work (if they work), this
inequality is reflected in where, and how they live.
Thus neighbourhoods are created, areas where most of the people
have a comparable income, live in houses which are alike, have
similar backgrounds and, to some extent, a common future. When a
firm closes down only those neighbourhoods from which its
Chapter 2: People, Spaces and Places
34
employees came are directly affected. If the supply of labour is
spatially compact, so too will be the impact. Only pupils in the
neighbourhood of a certain state school will go to that school. People
downwind of a particular source of pollution will be most affected by
it.
Not everything is so spatially determined. If people in one place
suffer, so eventually will all others in some way. It is the spatial
reinforcement of these trends which makes them so obvious. People in
poor neighbourhoods are less likely to have their houses improved or
their schools maintained. Then, more subtly, as the relative power of
people in different places affects those places, so does the movement
between them.
Just as the pattern of commuting allows the neighbourhoods to exist,
the pattern of migration serves to exacerbate their differences (Print
XXXIII). As a few people in poor neighbourhoods do well, they move
to richer ones. More importantly, the vast majority of migration is
between similar places in the spatial social hierarchy, reinforcing and
perpetuating the existing differences.
The terms neighbourhood, community and locality define, here, a
group of people who live in close proximity. They do not necessarily
have to know or even recognize one another. What the study of human
geography has shown is that they will tend to have more in common
with each other than with outsiders25. This is because of what put
25 [a] Place is not everything, but:
To insist on the continuing importance of place, therefore, is
not to deny that processes beyond the locality have become
important determinants of what happens in places. But it is
still in places that lives are lived, economic and symbolic
interests are defined, information from local and extra-local
sources is interpreted and takes on meaning, and political
discussions are carried on. [Agnew J.A. 1987 p.2]
[b] Places can be as much social fabrication, as physical
reality:
Paradoxically, the early British attitudes that viewed all West
Indians as Jamaicans may be becoming true. The
Jamaicanisation stems more at this moment from an identity
forged by white racism than from a Marxist class
consciousness. [Peach C. 1984 p.228]
[c] When, for instance, looking at an area the size of a
London borough:
The point should be clear enough. People live in different
worlds even though they share the same locality: there is no
single community or quarter. What is pleasantly 'old' for one
person is decayed and broken for another. [Wright P. 1989
p.290]
Chapter 2: People, Spaces and Places
The entire ward geometry of Britain was
generalized using an algorithm which
recorded a pair of vertices every five
kilometers along the (original) length of
each boundary. The list of boundaries was
then stored with the following coded
information:
left ward, right ward, original length,
number of vertices,and (X,Y) vertices
pairs...
Inspite of
programming at the
binary level the file
was 1,099,648 bytes
in length.
35
them there, keeps them
there, and moves them
away — the forces that
sort people in space, the
institutions
of
social
structure.
Place is important to the
understanding of the social
structure
of
society
because it is through
places that that structure
is most directly visible.
Not only is it visible in
A lookup table was
our everyday lives, but
also constructed as a
some of its many facets
binary file giving ten different area codes for
can be made visible on
each ward. These were its district, county,
paper (Figure 5). So also
amalgamated office area, parliamentary
must the complex picture
constituency, travel to work area, local
of flows be portrayed; the
Figure 5: Storing the Geometry
flows of people which
together allow, maintain and reinforce the spatial picture.
2.3 What Are Spaces?
Spaces are constructed from the relationships between places. Just as
26 [a] A well known source of error is the ecological
fallacy:
Many of these inferences occur in descriptive studies, in
which it is very easy to confuse the characteristics of areas
with the characteristics of people who live there. [Openshaw
S. 1984 p.18]
cases, the sign of the relationship also changes! Even worse,
the effects of country-wide variations in size of areal units
within the same level of the administrative hierarchy certainly
provides variable levels of resolution and may also induce
severe biases. [Rhind D. 1975 p.9]
[b] We cannot study individuals when dealing with places:
[c] Analysis of individuals requires unaggregated
information:
At least as long ago as 1950, it was demonstrated that not only
the strengths of relationships between different variables
change at different levels of aggregation but that, in some
The only safeguard against false interpretation of statistical
maps is to be able to get back to individual unit data, located
at point accuracy in order to check on one's opinion. It is
Chapter 2: People, Spaces and Places
36
the individual attributes of people are not the main interest, so the
collective attributes of single places do not hold the key to our
understanding of society. Places are not things which can be rigidly
defined and have a meaning of their own. They are abstract collections
of people whose depiction can shed some light on the spatial social
structure of our lives. Thus we cannot talk about the situation of a
place in isolation, but we can talk about the context created by the
collection of all such places, making up a space. It is the divergence
and convergence, clustering and scattering of people between places
in spaces on which we should concentrate (Muehrcke P.C. 1972, Warntz W.W.
1976, Petchenik B.B. 1979, Borchert J.R. 1987, Beruchashvili N.L. 1987).
Aspatial views of society can capture many things. Gender
relationships within households may not express a national geography.
especially unsound to base policy decisions on statistical maps
unless these maps work to the lowest practicable level of
spatial, numerical and temporal generalization.
It must be constantly remembered that the spatial, numerical
and temporal filters are varying together in virtually all
statistical maps of change, and within the spatial filter itself
there is a further source of variation within each map
especially if one is using irregularly shaped and sized areal
units.
Statistical maps at high level of aggregation are at best
distorting mirrors held up to reality. The "simple" portrayal of
a distribution is a difficult enough problem, as has been
shown. A second layer of problems rests upon the first. ...
[Forbes J. 1984 p.100]
modifiable areal unit problem:
A fourth solution to the MAUP is much more speculative in
that it involves the abandonment of traditional statistics in
favour of other ways of representing the inherent information
in the data. What exactly these other ways are is still uncertain
but increasingly emphasis is being placed on the visualization
of data rather than on statistical analysis (Tukey, 1977; Sibley,
1988) and it may be that these techniques are less susceptible
to the MAUP. [Fotheringham A.S. 1989 pp.223-224]
[g] Use of cartograms might also be beneficial:
There are several cartographic solutions to the small number
problem: map classification, data suppression, proportional
symbol maps, and cartograms. [Kennedy S. 1989 p.191]
[d] We have to learn to work round these problems:
The dependence of the correlation coefficient upon the
territorial base is well known to statisticians and it is just one
example of the general yoking of statistical quantities to the
size of the collecting areas. Many attempts have been made,
without success, to solve the problem. But it is inherently
unsolvable because collecting areas or territories are man
made and therefore arbitrary rather than 'natural' units. The
same comment applies to time-series. Days, weeks, months
and quarters are equally arbitrary subdivisions of a year and
the smaller units go to make up the larger. [Cliff A.D. &
Haggett P. 1988 p.163]
[e] An alternative view is that:
It is not the areal units which are to blame. The difficulty is
that the method of analysis used was inappropriate. This
tautology is immediate. If the procedure used gives results
which depend on the areal units used then, ipso facto, the
procedure must be incorrect, and should be rejected a priori.
[Tobler W.R. 1989 p.115]
[f] The use of visualization may well help overcome the
[h] No amount of technical wizardry can substitute for the
careful collection of good quality information:
Despite the current focus on error induced during the analysis
of spatial data, it is far too easy to search for the classic
technological fix to problems in datasets. One thing the use of
GIS systems should provide us is the realisation that increased
attention to the source of data themselves will alleviate more
problems than all the combined technological toolkits and
error correction algorithms built into commercial GIS
packages. [Brusegard D. and Menger G. 1989 p.185]
Chapter 2: People, Spaces and Places
37
Those whose schooling was in the private institutions will never have
experienced the spatial inequality of the state education sector, in
which where you live determines how well you learn. Aspatial views
which, say, equate how people vote with their class or job may find
many single strong relationships, but will fail to put them all together
as a single image. Those whose schooling was privileged will have
grown up in privileged places and so on (Print XXXIV). The near
certainty with which we can so easily make these statements
illustrates, in itself, how strong the spatial structure is.
In two dimensions we can see the entire environment in which people
live as a whole, not only how they vote and work, but how everybody
around them votes and works, and how other things about them are
distributed — where they and their neighbours came from, how they
live, who they are. Perhaps it is because this spatial structure is so
strong, so well known, that we so often seek to find more ethereal
aspatial relationships. We should first take a look at the wood before
trying to classify the trees as if they were not part of it.
There are, however, some fundamental problems to be overcome in
trying to see the structure of society through its spatial apparitions26.
To begin with, there is the problem of drawing a line around a group
of people to be called a society, for which Britain was chosen for this
dissertation. Then, there is the question of how to cut up that space,
and what effects such divisions can have. Numerous lines have been
drawn across Britain defining communities and cities, regions and
villages (Prints XXXV, XXXVI, XXXVII). How the spaces of interest
can be rebuilt from the dissection of the nation is the central problem
in relating places to people.
27 Which collections of people should we study?:
In terms of size, the basic demographic unit (Cox, P.R. p.4) is
the individual person, while the largest is the whole human
species — a species, incidentally, which is clearly
distinguished from all other species — but where in the
continuum between these two extremes do we draw
boundaries or thresholds? A demographer would see no real
difficulty here as he does not demand that the population
should be distinctive areally, socially or in any other way.
Many of his populations are hypothetical. [Clarke J.I. 1975
p.1]
Chapter 2: People, Spaces and Places
38
2.4 Drawing Lines
Just as the line which surrounds Britain is dictated to us, so too are
most of those which divide its people27. Most seriously, for many
purposes these lines divide the country into areas which contain far
too many people to come anywhere near to the idea of the
28 [a] City sized units are sometimes, unfortunately,
referred to as being local. Defence of their creation is
interesting:
[Goddard J.B. & Coombes M.G. 1987 p.1]
It is a basic tenet of this work that the appropriate scale to
examine spatial variations in economic development in Britain
is not the major regions but the local labour market area —
that is the area where the vast majority of people live and
work, where they can change jobs without changing house.
While location with respect to the overall national territory
will obviously be important (in the sense that the development
potential in otherwise similar areas in core and peripheral
regions may be different) it can be argued that insufficient
attention has been given in the past to more localized
characteristics of places which in certain instances may enable
an area to either overcome or fail to capitalize upon its
broader regional situation. [Goddard J.B. 1983 p.21]
Hence, an examination of the evidence on economic
prosperity and power reveals a clear North-South divide: the
bulk of the most prosperous LLMAs are located south of a
line from the Severn to Lincolnshire, with particular
concentration of the best performing LLMAs in the South
East outside London and on the neighbouring fringes of East
Anglia and the South West. [Green A. 1988 p.184]
[b] Numerous arguments have been presented claiming
that functional regions are natural, for instance:
[f] And other organisations have become exasperated over
the images created and the claims that some comparisons
cannot be made:
[d] The great variations in functional region size make
some patterns more likely to appear than others:
[e] Other researchers have noticed some of these problems:
The CURDS use of relative indices biases their account by
emphasizing the growth of rural areas. [Savage M. 1989
p.255]
The concept of functionally defined areas is not an easy one to
grasp, because most people have strong attachments only to a
very localized area such as a particular neighbourhood or even
a single street. Possibly the best way of appreciating these
ideas on the meaning of "places" is in terms of being asked
where one lives by people who live a long way away. If
someone from the North East goes on holiday or a business
trip to the South East and is asked where he comes from, he is
not likely to venture "Osborne Avenue" or even "Jesmond"
but is much more likely to reply "Newcastle upon Tyne", of
which that locality is a part. Similarly, a sixth-form student
being interviewed for a place in the geography department at
Newcastle University is much more likely to say he comes
from "London" rather than from "Islington" or even "Bexley".
The same applies to a person who comes from a dormitory
settlement on the outskirts of a city, in that such an individual
will probably volunteer the name of that city. This is the scale
of the "places" with which this book largely deals. [Champion
A.G., Green A.E., Owen D.W., Ellin D.J. & Coombes M.G.
1987 p.6]
Alongside the image of a booming high-tech economy and
stockbroker belt commuter village, relatively little attention is
paid to the other face of the South East — the unemployed,
low pay and poverty — on the opposite side of the 'SouthSouth divide' (Seeds, 1987). Conversely, one commentator has
claimed that if a North-South divide exists... 'Harrogate should
be on the South coast, not three hours north of Watford' (Rice,
1987).
Despite the existence of some pronounced inter-LLMA
variations at the intra-regional scale, the majority of those
challenging the existence of a North-South divide focus their
attention on micro-scale variations at the intra-urban scale, or
on inequalities between deprived inner city areas and
prosperous market towns. As noted in an earlier subsection,
scale is of crucial importance in examining the evidence for a
North-South divide, and it is significant that contrasts between
conditions prevailing in selected inner city wards and
prosperous market towns involve non-comparable areal units.
[Green A. 1988 pp.191-193]
[c] A problem of scales haunts the use of functional
regions:
[g] Advocates of a functional regions perspective have
made great claims:
Many commentators have been pointed to the existence in the
North of prosperous places and areas of deprivation within the
South. However, there has been much confusion on this issue,
largely because of inconsistencies in the scale of analysis
adopted. The most common fallacy has been to compare inner
city problems — which are characteristically localised
problems of residential deprivation, some in generally
prosperous labour market areas — with the problems of job
shortfalls that characterise the labour market as a whole.
The geographical units adopted for this study are the 280
Local Labour Market Areas (LLMAs) of the Functional
Regions framework (Coombes et al. 1982). This urbancentred functional approach divides the country up into a set
of real places that are relatively independent and on which the
quality of life of the local inhabitants largely depends.
[Champion T. & Green A. 1989 p.63]
Chapter 2: People, Spaces and Places
The hierarchy of areal units in Britain used in this thesis
is shown here. Arrows indicate which units nest into
which. Dashed connection imply a very similar
geography. All of these scales cover the whole of Britain:
17 Level II European Regions
64 Counties and Metropolitan Areas
97 Family Practitioner Committee Areas
121 Postcode Areas
116 Local Education Authorities
136 "Functional" City Areas
280 Local Labour Market Areas
322 Travel-to-work Areas
459 Local Government Districts
39
neighbourhood, locality or
community
outlined
above. At a ridiculously
aggregated scale Britain is
often divided into ten or
eleven
regions
and
numerous
statistics
periodically published for
average people in these
areas — for example, the
average Yorkshire and
Humberside person.
633 Parliamentary Constituencies
852 Amalgamated Office Areas
Other large areas often
used include the seventeen
planning regions, sixty
four
counties,
four
hundred and fifty nine
2,649 Postcode Districts
8,604 Postcode Sectors
10,444 1981 census wards and
Scottish part postcode sectors
10,519 1981 nomis wards
10,756 1987 nomis wards
129,211 1981 Enumeration Districts
125,476 1971 Enumeration Districts
Figure 6: The Areal Hierarchy
[h] We must always remember that it is people who
function — not the space which contains them:
Enumeration-district and grid-square data do provide a useful
basis for elementary descriptions of areas, either
cartographically (Dewdney and Rhind, 1975) or statistically
(Webber, 1977). Nevertheless they must remain descriptions
of what Chapman (1977, page 55) terms "areal aggregates"
and not of objects or true entities per se. Essentially Chapman
argues that objects of interest should have the properties of
systems. In primitive terms this means that the 'whole' should
be greater than the sum of the parts, or else we are dealing
merely with an aggregate. This property of 'wholeness' of an
object is reflected in its relative self-containment of activity.
Boundaries around the object are characterised as zones of
greater impermeability between the object and the outside
world. Finally the key characteristic of a true object is the
existence of some control mechanism within the object. Hence
objects 'respond' to stimuli and therefore can be said to
'behave'. Mere aggregates do not behave. [Coombes M.G.,
Dixon J.S., Goddard J.B., Openshaw S. and Taylor P.J. 1978
p.1181]
29 [a] New calls for a place perspective are not
for more functional regions:
I want to be clear at the outset that most recent
arguments for a place-based geography are not
calls for the revival of regional geography based on either the
immutable physical landscapes of fixed regions at a scale
intermediate between locality and national unit or descriptive
inventories of regional characteristics regarded as if they are
independent of social order. The conception of place involved
in these approaches could not be expected to excite much
interest in social science. [Agnew J.A. 1989 p.9]
[b] Other researchers also disagree with the use of such
areas in analysing social structure:
However, within such areas there can be significant changes in
population and, for the purposes of this study, FRs do not
produce enough local detail nor are 1971 Census statistics
readily available for these areas: as a result, they are not
appropriate for use as an areal base in local study. [McKee C.
1989 p.6]
[c] An area slightly larger than Greater London is often
classed as a single labour market:
The multivariate classification confirms that Greater London
is best not taken as a single unit because it divides sharply
between Inner and Outer London. [Craig J. 1980 p.23]
Chapter 2: People, Spaces and Places
40
districts (Print XXXVIII), six hundred and thirty three mainland
parliamentary constituencies (Print XXXIX), and so on (Figure 6).
These areas define regions the size of small towns, often of a hundred
thousand people or more. The spatial social structure hardly manifests
itself here; for in these areas, to sustain such areas, are people from all
parts of the social structure. Towns and cities are made up of the rich
and poor. There are not rich and poor towns, not in the sense that there
are rich and poor people.
What is more, the boundaries of these many areas rarely coincide,
neither with each other in space nor themselves in time. So we are left
with most of our geographical knowledge being mixed into a plethora
of ephemeral places. The shapes and sizes of which do as much to
alter the appearances of what is happening, as do the numbers which
are gathered about the people within them; even though they may be
very accurate measures. The average for a large area destroys
knowledge of the variations and defines people who do not exist; a bit
of everyone and all of no one (Nystuen J. 1965, Clarke J.I. 1975, Rhind D. 1975a,
Coombes M.G., Dixon J.S., Goddard J.B., Openshaw S. & Taylor P.J. 1978, Fotheringham
A.S. 1989, Tobler W.R. 1989a).
One way to try to overcome this confusion is to draw another set of
lines, but this time defined not for the convenience of collection, to
administer government or distribute services, but on some functional
grounds28. Zones have been created which try to encompass the areas
within which people live and work, or between which they tend to
migrate. Although such exercises might usefully tell us something
about the patterns of commuting and migration, they do not serve well
for seeing social structure any better than the existing divisions — for
several reasons.
Primarily, they have tended to create even fewer places than have
commonly been used and so the gross aggregation of communities is
Chapter 2: People, Spaces and Places
41
sustained, again creating the illusion that there is little spatial division,
but worse when it is claimed that there is some rationality behind
these lines. Whereas administratively defined areas tend at least to
collect the same numbers of people within their boundaries, these
functionally defined entities exhibit some of the greatest variations in
population of any set of boundaries in use. Finally, the very nature of
these areas, created from flows of people, dispenses with the spatial
aspect of society by creating a set of (semi-autonomous) places which
can be studied on their own, ranked and (supposedly) profitably listed
in tables. Functional regions are an aspatial concept, an attempt to take
out the effect of movement and the relationship between places, where
the real differences are to be found29.
There has been too much drawing of lines. Now is the time to begin
rubbing them out, to see the real divisions, not the ones imposed by
the cartographer's pen, administrator's necessity or geographer's
algorithm.
[d] Although other classifications split the capital in two:
The fact that Greater London has been assessed as falling in
two separate travel-to-work-areas but one LLMA starkly
illustrates this point. Not since the Great Plague of 1665 has a
cordon sanitaire been imposed at London's periphery,
blocking entry and exit! (Defoe, 1727). [Merrett S. & Sharp C.
1991 p.290]
30 [a] Advocates of grid square mapping have occasionally
confused orders of magnitude:
Enormous variations in base population are typical of grid
squares but also occur in EDs ... [Rhind D. 1983 p.159]
[b] Small scale density mapping is not a particularly new
idea:
A time series of such proximity measures (for 1570, 1700,
1801 and 1851) were shown diagrammatically in the 1851
Census Report. A large circle represented the area of England
and Wales and inscribed within each were sets of six-sided
polygons whose size was proportional to the density of
population over the country as a whole, and to the average
distance apart and 'average amount of ground to each person'.
Variations in density were shown in a detailed dot-type map
— the areal unit used is not explained but it is very small and
the overall effect is not dissimilar from the maps produced
120 years later when computerised cartography led to the
production of enumeration district and 1 km grid square maps.
The measures of proximity mentioned in the preceding
quotation were also given for each of the nine divisions
(analogous to regions) of England and Wales. [Craig J. 1987
p.34]
[c] It is telling that grid maps show more empty land than
populated kilometre squares:
Hence, the maps in People in Britain (CRU/OPCS/GRO(S)
1980) are the first reliable maps of unpopulated areas in
Britain! The second consequence is that related to data
suppression: because of their fixed size, many more grid
squares than EDs are suppressed (section 2.9.6) in census data
and as a consequence mapping of the grid-square data is not
possible for a large percentage of the rural areas of the
country. [Rhind D. 1983 p.181]
[d] But the protagonists of the technique see other reasons
why it can be so unpopular:
But much the most devastating disadvantage that grid squares
have for these purposes is that most people do not yet like
them. They are certainly unfamiliar as a means of data
referencing and presentation. At the time of writing
(November 1974), for instance, only one group had any 1971
Census grid square data for England or Wales. More than this
unfamiliarity though, is the deep-seated, if irrational, feeling
that they are distinctly 'unnatural', while 'anything which
wiggles' is 'natural'. [Rhind D. 1975 p.3]
31 [a] The approach taken here has been tentatively
suggested in the past:
There is however no theoretical reason why the ward or parish
should not be the basic unit in a study of the country as a
whole. There are some practical problems of separating the
wood from the trees in that the amount of detailed information
that is available is large. However it is still possible to
summarise it even though some of the techniques which are
helpful with a smaller amount of data (mapping, for example,)
Chapter 2: People, Spaces and Places
42
2.5 Picturing Points
To show the real spatial distribution of people we need information
about places which are at least as small as the communities we
envisage these spaces to contain. If you think of the number of
neighbourhoods in a town of two hundred thousand people, you
should be able to count at least ten, if not twenty, distinct estates.
Places ranging in size from as few as one hundred to as many as
twenty thousand people are what we know as neighbourhoods. It is
with places such as these that we can build comprehensive pictures of
the national spatial structure. Even the smallest, conventionally used
places are usually too large for our purposes (Print XL).
may be impractical. [Craig J. 1980 p.14]
[b] Many may consider my approach impractical:
Anyone at this stage writing their own software of this type is
therefore foolhardy. All of these maps, however (with the
exception of those by OPCS) deal with only up to about 1,000
or so zones on any one sheet of paper. [Rhind D., Mounsey H.
& Shepherd J. 1984 p.65]
[c] It is the availability of a high resolution printer which
has made such fine scale mapping possible:
All the information presented on a map is to be read by eye
from paper. The resolving power of the eye enables it to
differentiate to 1/10mm where provoked to do so. Clearly,
therefore, conciseness is of the essence and high resolution
graphics are a common denominator of cartography. There are
tricks, too, of colors, lines and point symbols that suggest that
there is some subtlety within the cartographic language. Here
the capabilities of the high resolution plotter in a computer
system offer interesting potential to extend the language as
opposed only to mimicking it. [Bickmore D.P. 1975 p.331]
[d] The realization that this technical equipment could be
used for more than just mimicking manual methods was also
crucial:
Nevertheless, by the early 1980s it had become possible, given
adequate investment in software and hardware, to replace
traditional mapmaking with digital technology. Although the
proportion of maps created using digital methods is still
relatively small, the technology has advanced to the point
where it is impossible to determine by cursory examination
which approach has been used to generate the final product.
The critical point was probably passed in about 1980. The
hardware developments and multi-year investments in
software have advanced digital technology to the point where
its constraints are no more restrictive than those of pen and
paper.
One of the surprising aspects of these developments, and of
applications of digital technology in many other areas as well,
is the degree to which perfect emulation of the conventional
product appears to be regarded as a legitimate objective. Word
processing technology has been developed to the point where
it is a more cost-effective way to generate the conventional
typed text. Surprisingly, it has not been used to explore
alternative methods of presentation to the same extent. Yet
neither word processing nor digital cartography are
constrained by the same limitations that produced the printed
page or analogue map. [Goodchild M.F. 1988 p.316]
[e] It is the commercial software, which is of poor quality
— that is now coming into direct conflict with visualization:
A resulting phenomenon is the "application expert", the man
or woman who knows all the ins, outs and quirks of ARC/
INFO, and who is crucial within the organisation for getting
questions — more or less — answered, even to the extent of
writing "user-friendly" macro systems. Although this will not
change much over a short period, in the long term there is not
much future to it. Especially in the field of visualization,
developments have already set in to a much more modular
approach, in which techniques like filtering or enhancing can
be applied independently to GIS applications. [Hartmann J.L.
1991 p.411]
32 [a] There is good reason for using such a fine spatial
scale:
Moreover, analyses should be carried out at the scale of the
operation of the processes whose manifestations are being
observed (Rhind et al 1984); without this, results are very
likely to be biased. If the scale at which a process is occurring
is unknown, it is best to at least examine the data in the most
disaggregated form available. There is, in addition, good
reason for believing much of the development — through
pervasion — is locally varied. It is localised conditions which
dictate whether demographic and socio-economic change
exist, and the exact form it takes; therefore an emphasis on
local-scale study is necessary to avoid making overgeneralisations, particularly in areas of high population
Chapter 2: People, Spaces and Places
43
The boundaries of such small places may well be arbitrarily defined.
That is not a great problem. It is the relationship between the places in
which we are interested. We should look at what they show
collectively, not their individual characteristics — there are far too
many of these to examine each one, anyway. With enough small
places we can create methods which are robust to the effects of the
arbitrary lines drawn on the map, devising techniques by which we
can paint realistic pictures of social spaces (Petermann A. 1852, Wright J.K.
(ed.) 1938, Clarke J.I. & Rhind D.W. 1975, Rhind D. 1975b, 1983b, Census Research Unit
1980).
The most important factor about the localities that we use is that they
vary little in the number of people which they contain, for again we
density where large-area study will not reveal local scale
change. [McKee C.H. 1989 p.49]
[b] Visualization raises the limits:
[b] Other advantages also accrue when working with
enumeration districts:
More generally, these problems of heterogeneous
geographical units and the prospect of massive ecological
fallacies are common to much census analysis. It is obvious
that they may be reduced — but not eliminated — by
concentrating on the analysis of EDs. [Openshaw S. 1983
p.247]
[c] But some problems remain:
The problem of object location will be less troublesome if the
subareas are small. If it is possible to use small subareas, the
number of possible locations for an object is small and the
generated patterns, though still indeterminate representations,
will be more concise than otherwise. In any case, analysis
must proceed using grouped data and it may be wasteful to
attempt to use sample subareas of very small size.
Aesthetically, there may be justification for proceeding with
small areas, but the effect of size limitations on probabilities
(attributes) may become more difficult to handle. [Getis A.
1974 p.85]
33 [a] It would be interesting to discover how much detail
we can profitably appreciate in an image:
Of all the research reported in this review, the one factor that
constantly changes and has the potential to be a deciding
factor and influence is the number of statistical units
contained in the test maps. For example, the number ranges
from approximately 1000, (maps produced by the U.S. Bureau
of the Census), to only nine fairly large areas (Cartstensen
1982:66). Perhaps a limit exists pertaining to the number of
statistical units on a two variable choropleth map that the map
reader can process easily. [Halliday S.M. 1987 p.37]
Significantly more complexity can be comprehended through
Visualization in Scientific Computing techniques than through
classical ones. [McCormick B.H. et al 1987 p.vii]
[c] Again, generalization is often not necessary:
We believe that it is possible to push the resolving power of
the eye to the point where this detail can be understood
without complicating the issue by statistical generalization.
[Bickmore D.P. 1975 p.344]
[d] But problems can arise from using many small areas:
Amalgamations may also be necessary when mapping at the
enumeration-district level, depending upon the size of the
study area. With very large areas, for example in excess of
five hundred enumeration districts, the amount of printed
shading that can be applied to each unit is limited if the size of
the computer output is to remain within reasonable bounds.
Some smaller enumeration districts may receive only one or
two printed symbols, which renders detailed inspection of
individual units virtually impossible. [Kirby A.M. and Tarn D.
1976 p.510]
[e] In particular, cartograms greatly reduce the misleading
impressions created by small populations:
It is also obvious that mapping at high geographical resolution
maximizes the chance of encountering small groups of people
in each area: indeed, this is the rationale for high resolution,
since in dealing with census data we are interested in people
rather than areas. With small groups of people, however,
percentages which are not based on land area ('percentage of
the population which is male' c.f. 'density' of the population
per km2) are frequently very unstable: the highest and lowest
Chapter 2: People, Spaces and Places
44
require equal representation. Here we find that the second traditional
response of geography in its rejection of arbitrary administrative
divisions fails. At the same time as "functional" regions were being
created, other researchers were turning their hand to placing lattices
over the land area of the British Isles and counting attributes of the
number of people in each small square. This method of division was
called grid mapping, and one kilometre squares were most often used.
Grid mapping is no less arbitrary a practice than any other technique,
although the practicalities of its execution are the simplest. That it
creates a stable set of units over time is a trivial defence of the
method; any set of lines you draw and leave on a map is "stable".
What makes this type of spatial division stand out as more flawed than
any other is the huge inequalities in populations it creates between
areas, at the added expense of creating so many areas30. Unlike any
other geographical division, the more places that are added in this one,
the greater the inequalities become, with most spatial units containing
no people at all.
What we require to cover Britain then, are roughly ten thousand to
one hundred thousand localities. Fortunately, the lowest tier of
administrative geography gives us the first set — wards31 (see
Appendix F), and that of census geography the second — enumeration
districts32. There are other practical alternatives, for example postcode
sectors, on which much market research is based, number over eight
thousand (Prints XLI, XLII & XLIII). But in general the absolute
values, and consequently the most visually striking areas, on a
map may therefore commonly be based upon very few people.
Most mapping of detailed census data has ignored this effect
which is rampant in use of ED and 1 km2 grid-square data.
Indeed, many authors have been even more misleading: where
the population is small and also clustered in one part or
another of the area being mapped, they have none the less
shaded the whole area and greatly extended the misleading
effect of small populations. [Rhind D. 1983 p.183]
34 [a] Making the point more strongly:
Therefore, and this point is played down by Giddens, place is
not just locale, as setting for activity and social interaction, but
also location. The reproduction and transformation of social
relations must take place somewhere. [Agnew J.A. 1987 p.27]
[b] Place and space are evolving concepts:
New Zealand is not on the other side of the world from
England. Mentally shrinking the oceans to one-tenth their size
places England closer to New Zealand than to Hungary. The
return to the seas "shrank the earth" and turned the land inside
out. [Bunge W.W. 1973 p.280]
Chapter 2: People, Spaces and Places
45
wealth of information that is available at the time of the census for
enumeration districts (and for wards during the decade), makes these
places the natural choices from which to paint spaces33.
2.6 Population Space
How should these pictures of spaces of people in places show us
social structure? What should they look like, how should they reflect
society? To answer these questions we must have some idea of what it
is about the space we want to see — the nature of society.
People in space create a near continuum, especially if we view the
distances between them in a relative sense. The spatial nature of our
society is such that nearby places usually exhibit very similar
characteristics in their populations, but occasionally they diverge
widely. To see these patterns we have to stretch and squeeze the space
of physical geography into becoming the landscape of human
experience, opening up the cities, exorcising the empty space from the
image (Prints XLIV to XLVII, Bachi R. 1975, Tukey J.W. 1976, Tikunov V.S.
1987).
The spatial nature of the society we live in holds more than divisions
and continuity, trends and correlations. It is patterned. Social patterns
[c] It is as much the movement between places, as the
people within them, that interests us:
Place, therefore, refers to discrete if 'elastic' areas in which
people can identify. The "paths" and "projects" of everyday
life, to use the language of time-geography, provide the
practical "glue" for place in these three senses (Pred 1984).
[Agnew J.A. 1987 p.28]
35 [a] Remember, it is people, not land, we are looking at:
Cities do not only exist in the physical property of the cities,
they also exist where the citizens of the city move. We are so
property bound that we think a man lives at the address of his
bed, even if he ever visits it. The cities could be defined as
their people as apposed to the property of the city. Using such
a definition, the city has moved if the people evacuate it, say
on a weekend holiday. [Bunge W.W. 1973 p.293]
[b] But many aspects of the places, in which people live,
have an influence over their behaviour:
... the "factors" causing political behaviour cannot just be
added up in linear fashion (census class, census age, census
ethnicity, etc.) to constitute an adequate explanation. To the
contrary, it is how these factors "come together", take on
meaning for people, and determine political outcomes that
constitutes a satisfactory political analysis. In other words it is
in places that causes produce the reasons that produce political
Chapter 2: People, Spaces and Places
46
of power, control, deprivation and monotony are all reflected in spatial
mosaics. Rings of the wealthy, holes of the poor, lines of accessibility,
enclaves of distinction. However, none of this would be seen if we did
not seek to see it. We must know something of what we are looking
for before we can know how to look.
Pictures of spatial social structure should have the power to reflect the
complex tapestry and delicate lace-work of the relations between
people through the places in which they live and the spaces they
create. All social organization must take place somewhere, and
aspects of that somewhere strongly shape what structures are
formed.34
2.7 Adding Time
Finally, space and place is not enough. As this chapter was introduced
by arguing that the constraints of space are as important as those of
time, so it should end, re-introducing time into the picture. This
detailed spatial structure is something which takes time to form, and is
deformed in time (Pred A. 1984, Taylor P.J. 1991).
Neighbourhoods are temporal phenomena, the cohort in time is like
the community in space. People who live at the same time will live
comparable lives of comparable quality. Far more so for people who
live at the same time and in the same places. These three dimensional
spacetime pockets of existence are the ultimate level of containment in
this work. They can be delimited by neighbourhoods and generations,
measured in a few hundred people and a few dozen years.
behaviour. Many of these causes, as I have argued emanate
from other places beyond the confines of the locality. But it is
in the locality and through the choices of local populations
that the various causes of political behaviour, from the shifting
economic position of the US automobile industry, in the case
of Detroit, to Castro's Revolution in Cuba, in the case of
Miami, work to structure political expression. [Agnew J.A.
1987 p.213]
[c] And we have still to consider time properly:
A geography of regions within the modern world-system must
Chapter 2: People, Spaces and Places
47
The history and geography of modern Britain, which tells the story of
all its people, can only do so successfully, through all the sources we
have, based on the concept of ever changing communities35.
Definition in time is, however, much less clear cut than definition in
space. Communities merge into one another, evolve and disappear.
The idea of rigid boundaries is even more ridiculous when applied to
time than when applied to space. Here an infinity of units must be
used to create a spacetime continuum, to reflect the temporal spatial
structure of society.
A fixed three-dimensional image of an evolving, finely organised
society is the final suggestion of this work, relating the times through
which people live their lives to the places in which they live them. For
now, creating the two-dimensional spaces to reflect simple twodimensional instances of a complex structure is enough of a challenge,
and the focus of the next chapter.
locate its regions in both space and time. [Taylor P.J. 1991
p.28]
48
Chapter 3: Artificial Reality
People ask me, "What's so good about artificial reality?" And I say, "What's
so good about reality?"
— Myron Krueger, quoted in New York, August 6, 1990
[Haggerty M. 1991]
3.1 Imagining Reality
Reality requires resemblance to the original, artificial implies made
by art — not natural. Artificial reality is a manufactured version of the
original, created through imaginative skill to show more about reality
than is directly visible. If we were to paint things just as we saw them,
our purpose would be merely to store their likeness. Instead we wish
to investigate their being. To bring out more than the mere surface
details of reality we must create images which might not look directly
like it — but tell us far more about it and what lies beneath it (Print
XLVIII). Almost everything we study is either too small or too large to
be studied as it really is. We create models of our world through
which to understand it. When these models are expressed in picture
form, an artificial reality is created.
Chemical models of molecules are a pioneering example. Not only do
they represent great magnifications of reality to a size we can see; but,
more importantly, they distort, simplify and elaborate the object, to
enhance our understanding. The atoms making up the molecule are
drawn as planets, brought close together, and linked with rods to
imply connection. Unnecessary detail is omitted, and different
elements are colour coded to aid interpretation. Highly complex
molecular modelling is now part of the forefront of visualization on
the scientific computing agenda.
Chapter 3: Artificial Reality
49
Transforming reality is an ancient occupation. From charting the
Heavens to depicting anatomy, we represent things not as they are, but
as we think they should be understood. That we create artificial
realities is partly of necessity — reality being too large, small, or
chaotic — but it is mainly to expedite understanding36. In
visualization we enhance reality. The most important decision in so
doing concerns what view of space to adopt as the basis for our
pictures (Print XLIX, Bunge W.W. 1966, Post J.B. 1973, Denes A. 1979, Krueger
M.W. 1983).
Artificial reality is a way of understanding reality. We create it
because we can, and our capability to do so is enormously expanded
by visualization through the computer. The contents of our
imaginations are made visible to feed back into our and others' minds,
in great loops of creativity. The ability to share so vividly our thoughts
will tax our abilities to accept and cope with each other's artificial
realities37. Through seeing how others see, we all change our views.
36 [a] To understand reality we must first transform it:
Graphics is a very simple language. Its laws become selfevident when we recognize that the image is transformable,
that it must be reordered, and that its transformations represent
a visual form of information-processing. [Bertin J. 1981
p.183]
[b] Transformation of space is the essence of geographic
understanding:
Stated in simplest terms, a creative, productive person is one
who can take the ordinary sensory data available to all of us
and process them in new ways. It is this dual process of
gathering data and transforming these data creatively the
geography students should aim to master. The first step is to
gain expertise with the techniques of the field. whereas writers
need words, mathematicians need numbers, and artists need
visual perceptions as primary raw materials of their respective
fields, geographers need all three. It is in that uniquely
geographical technique, the map, that words, numbers and
graphic forms are moulded into a hybrid view of
environmental relations.
But knowledge of geographical techniques (including maps)
alone provides little more than the starting point for creative
thought. The second and most important step in being creative
involves transcending these basic raw materials of the field
and intuitively seeing possibilities for transforming these
ordinary geographical data into new creations. Here, again,
the immense transformational power of cartographic
abstraction provides the geographer with a special means
toward environmental understanding. [Muehrcke P. 1981 p.6]
[c] The transformation can produce an artificial reality:
One tendency, which is not necessarily desirable, is the use of
displays to reproduce the real world. The technical evolution
of television suggests that other displays will undoubtedly
progress toward greater and greater realism. But it is probably
an error to try to automatically guide aesthetic displays in this
direction. An abstract conceptual or symbolic space may be
more effective than a completely faithful rendering of a real
environment. What is important is that the displayed space
appear sufficiently compelling so that the participant suspends
belief and accepts the experience as real, even if the world it
portrays is not. [Krueger M.W. 1983 p.79]
37 [a] How should we view the physical world?:
The map is not some inferior but more convenient substitute
for a globe. Map projections are not simply choices of lesser
evils among distorting possibilities. On the contrary, the map
allows the geographer to twist space into the condition he
wishes. For purposes of finding lines of constant compass
direction, the Mercator projection is far superior to the actual
surface of the earth. The earth itself lacks the spatial property
of having such lines being straight lines. [Bunge W. 1966
p.238]
Chapter 3: Artificial Reality
50
3.2 Abstract Spaces
Viewed from a few hundred metres above the surface of the earth
people appear like ants, milling around aimlessly. Another few
hundred metres and we cannot see the people, only the buildings they
constructed, the land they cleared and the roads and bridges which
connect and separate these things (Print L). A few kilometers above
the earth and all this evidence of human activity has disappeared; we
are left with only the flares of oil wells and the line of some ancient
Chinese wall to indicate that any people occupy the land below38.
[b] Distortion can be used to great advantage:
Second, the distorting features are perceived not only
negatively as an impurity, which interferes with the true form
of the invariant object; they are also seen positively as the
effect of a condition that overlays the true shape of the object.
The effect is understood as the logical consequence of the
object's position in space relative to the observer. [Arnheim R.
1970 p.51]
[c] We distort both to simplify and illuminate:
The map is our experimental tool. It allows us to twist space
into desired shape. What projection will yield the "uniform
surface" we need so that we can meaningfully test geographic
theory? This geographic mapping is crucial before we can see
the world clearly, so that "all the spatial shimmer is taken
away and the underlying symmetry focuses for pertinent
analysis." [Warntz W.W. 1973 p.55]
[d] Effective maps are not necessarily realistic:
One of the most often cited applications of graphic imagery is
the use of maps. We found that the most effective maps may
not be the most realistic, but are those which actually "distort"
reality by eliminating information and by visually clarifying
the topological and functional connections among
geographical entities. (e.g., a pocket subway map). [Mills M.I.
1981 p.115]
[e] Here, we create and stress distortion:
The Distortion series by Agnes Dene (see fig. 26) is a series of
projections for representing the earth. The series stresses
distortion, rather than trying to minimize it. The projections
were developed geometrically. Through her book, (Denes
1979) we know what Denes intended to point out. That is that
distortion is an unavoidable part of the process in which
concepts are transformed into substance. Denes description of
her research method is comparable to descriptions of the
scientific process. [Varanka D.E. 1987 pp.66-70]
38 [a] Recognition of the fundamental difficulty of
showing the activities of people on the Earth has a long
history:
Such being the points which it is desirable to compass in a
pictorial representation of a given crop, say wheat, several
modes of obtaining the relative power, in this respect, of
different sections, States, or counties, suggest themselves to
the mind. More than one of these might, perhaps, be
advantageously adopted, were it practicable in the
construction of such a map to take the township as the unit of
treatment; but as this is wholly impracticable in dealing with
so large a field as the United States, the county — embracing,
as counties generally do with us, town and county,
manufacturing, commercial, and agricultural populations alike
— being the lowest unit of treatment that can be taken for the
purpose, the reflections and the tentative computations of the
Superintendent satisfied him that no one simple ratio could be
found which would not, in many cases, grossly exaggerate,
and in other cases as unjustly disparage, the importance of the
crop to the county and the county to the crop. [Walker F.A.
1870 p.367]
[b] Thematic mapping brought about a conflict in
cartographic history:
The second major revolution in cartography identified by
Robinson (1976) was the emergence of "thematic" mapping.
Again, technical advances facilitated expansion of this new
revolution. Robinson included computers, along with further
advances in printing, plastic drafting materials, and new
photographic films, as the significant advances. This second
revolution, which began more than a century prior to the
invention of computers, also derived from a desire to view
geographical reality from a different perspective. Whereas in
the first case we progressed from an abstract theological basis
of maps to a spatial planimetrically accurate one, with
thematic maps we had almost the reverse. Emphasis was on
communicating relationships and spatial problem solving,
often at the expense of precise locational specification.
[MacEachren A.M. 1987 p.100]
Chapter 3: Artificial Reality
51
The creation of artificial spatial realities is necessary to our sense of
self importance. We think that what we do is central to this world, that
the thin slices of concrete we have placed upon its soil is a great
achievement, that our impact is of crucial importance to its future.
When we map our world we are mapping it for ourselves; to navigate
it, control it, and understand it as it relates to us. The objective view of
the earth, the blue-green blob seen from the moon, tells us nothing of
the world of people, who might as well not be there for all we can see.
To see ourselves on this planet we had to create first the abstract
spaces upon which our paths could be drawn39 (Print LI).
[c] The thematic / topographic debate continues today:
Other theoretical foundations and methodological approaches
are essential here, based on studies of the specific
geographical relationships between the phenomena being
mapped. "The fetish of geometric accuracy", writes K.A.
Salishchev, "which is becoming increasingly clear in respect
to topographic maps and maps in general that are intended for
cartometric work, turns out to be unwarranted and even
senseless for maps intended for other purposes, particularly
thematic maps" [21, pp.123-124]. [Suvorov A.K. 1987 p.259]
39 [a] Many thematic creations were extremely
misleading:
This choropleth method of mapping numbers using the area of
the collecting unit as part of the symbol is widespread, and as
a method I have no disagreement with it. It is the misuse of
the method that is bad. Choropleth maps are appropriate only
for those numbers that include area. I expect that many of you
are so accustomed to seeing this method used to map any kind
of number that you will not agree with me that some uses
produce error. Beware that your response isn't more emotional
than reasoned. Arguing that this method of mapping is
sanctioned by custom is not persuasive. Until quite recently it
was accepted custom for a doctor to bleed, purge, and puke
his patient. But, even though some survived, it is no longer
held that this once customary treatment is laudable. I suggest
that simple mechanical accuracy in maps is not enough. Map
makers should provide psychologic, or call it aesthetic,
accuracy as well. . . .
This map could not be much more deceiving than it is even if
a conscious effort were made to make it so. [Williams R.L.
1976 p.216]
[b] One researcher developed a technique akin to the
cartogram, but which did not transform space:
It should be stressed that use of GRP [a method developed by
the author] to represent a population Pi of each region i is
justified only if the reader is aware that no account is taken of
the different territorial size of each region and that this way of
graphical presentation is extremely variable, with changes in
the division of the territory into alternative systems of regions:
consider, for instance, three alternative maps of the territorial
distribution of the population of the U.S.A., one by counties,
one by states, and one by large geographic divisions. If the
GRP scale is kept fixed on the three maps, their general aspect
will change markedly: the number of symbols will decrease
from the first to the third while the size of the symbols will
increase. However, the total blackness of the GRP will remain
almost constant. [Bachi R. 1968 p.198]
[c] The use of cartograms hides areas with small
populations and thus low denominators:
It is the contention of this author that many, many of the
census maps for small areas of Britain (such as EDs) are
highly misleading because the ratio used (e.g. percentage
values) are based upon small denominators and have been
variably influenced by adjustment. Even without adjustment,
percentages based on small populations often reach extreme
values and, when mapped, these may dominate the map.
[Rhind D. 1983 p.185]
[d] Basic geography may even be better learnt using
cartograms:
Through classroom experimentation, Fuson "has proven that
geographic locations are easier to learn when map clutter is
eliminated." Fuson also suggested using cartograms as
teaching aids for place-names before attempting to tackle
atlases which tend to show too much detail. [Phillips D.J.
1977 p.5]
[e] The use of cartograms allows absolute measures to be
simply shown:
Absolute numbers should never be mapped by this means
when using a standard set of boundaries. To do so is grossly
misleading since large areas will automatically tend to be
black. Two solutions exist:
— to standardize the data, most commonly by converting the
variable to a percentage or other ratio form;
— to transform the map base, such that the basic areas are
enlarged or reduced in size so as to represent the total
numbers of people therein, then to map, say, absolute numbers
of retired people on this new base map (see figure 6.5); [Rhind
D. 1983 pp.187]
Chapter 3: Artificial Reality
52
The Mercator Projection maintains all compass
directions as straight lines and was therefore
extremely useful in an age of maritime navigation. It
distorts areas considerably, for instance, Greenland is
very much magnified while Africa is relatively
compressed. The shape of the areas is also altered,
but this is inevitable to some extent in flat
representations of the surface of the Earth.
It is surpising that centuries after its inception this
image, and other images like it, should still present the
accepted view of the world. A series of lines, dividing
land from water, which medieval explorers used to find
their way across the oceans.
Figure 7: The Mercator Projection
Early maps of the world were
centred on the religious
capitals,
the
land
was
magnified where most people
were known to live and most
detail could be drawn. There
were maps of kingdoms and
empires,
spaces
which
contained land, the land which
contained people. The world
was flat, as you could not sail
around it. Rivers were drawn
as wide barriers, mountains
enlarged
as
impassable
obstacles. Toady roads are
drawn far wider than their real
width.
Map projections were first deliberately devised to aid navigation,
straight lines on the map maintained their compass orientation40
(Figure 7). The shape of the world changed again, and suddenly it was
full of oceans and seas where once the land had crowded to fit in the
names of places and courses of rivers. The shape of the physical world
had not changed but the world of man had, just as it has in the pictures
drawn here (Print LII, Tobler W.R. 1966, 1977, Monmonier M.S. 1977a, Williams
R.L. 1976, MacLane S. 1980, Stooke P.J. 1985). Trade and conquest by sea
became paramount and the images changed to reflect this.
40 [a] Projection is the most important decision in "how to
map":
An expertly chosen map projection helps to focus and amplify
the geographic message of a map (Figure 1). [Hsu M.L. 1981
p.152]
[b] Tobler was one of the first to argue that distortion
could produce realism:
As map projections, the transformations used in this chapter
do not conform to the traditional geographic emphasis on the
preservation of spherical surface area. Maps prepared using
these transformations, however, from many points of view are
more realistic than the conventional maps used in geography.
[Tobler W.R. 1961 pp.162-163]
[c] The map can be anything we make it:
We need to recognize unequivocally that the map is a socially
constituted image and our definition of the artifact itself
should reflect that recognition. This is entirely lacking in
works such as Robinson and Petchenik's The Nature of Maps
or Keates' Understanding Maps and in the voluminous
literature on cartographic communication and cognition. They
represent a still largely positivist way of cartographic
thinking. [Harley J.B. 1990 p.6]
Chapter 3: Artificial Reality
53
As all the lands were overrun by the former seafaring people, their
area had to be subdivided and their actual size grew in importance.
The shapes on the maps changed again as land area was maintained at
the expense of compass direction. Oceans, now easily traversed,
shrunk as they were cut out of the atlas, and the parcels of farm land to
be settled, fought over and traded, were clearly depicted. The centres
of the maps moved from Mecca and Jerusalem to Venice and London,
and the names were of dominions rather than provinces41 .
This century is a time of air travel and world wars, of twenty four
hour money markets and starvation on continental scales. The shape
of the world has changed again, but to no single accepted projection.
There are numerous one world projections, from those which claim
the peoples of the world are best represented by their land areas, to
those in which distance represents the shortest paths of ballistic
missiles.
The shape of our world has always been an artificial reality of the
times, whatever religious or scientific accuracy was claimed for it42.
41 [a] Writing on the historical development of
cartography, Harley complains:
[b] But spatial accuracy is beginning to be disregarded
today:
On other maps, towns occupy spaces on the map — even
allowing for cartographic convention — far in excess of their
sizes on the ground. Castle signs, too, signifying feudal rank
and military might, are sometimes larger than signs for
villages, despite the lesser area they occupied on the ground.
[Harley J.B. 1988 pp.292-294]
Thus I would like to suggest that the "new" view encouraged
by the current interest in "GIS" is the recognition that the
spatial relationships between the objects on your map matter
as much or more than their actual coordinates, particularly
when using computers. [Gold C.M. 1989 p.21]
[c] Cartograms often look very much like old maps:
[b] A few pages on, however:
Maps as an impersonal type of knowledge tend to 'desocialise'
the territory they represent. They foster the notion of a
socially empty space. The abstract quality of the map,
embodied as much in the lines of a fifteenth century Ptolemaic
projection as in the contemporary images of computer
cartography, lessens the burden of conscience about people in
the landscape. Decisions about the exercise of power are
removed from the realm of immediate face-to-face contacts.
[Harley J.B. 1988 p.303]
42 [a] Ancient maps look very disorganised to us:
In ancient times and the middle ages, maps were highly
subjective. No impersonal codes and conventions. No uniform
scale, orientation or even distances. [Hagen C.B. 1982 p.326]
As an aside, it is interesting to note that even if the earth were
a disk (as some ancients believed) and not sphere-like, the
suggested transformations still would be of value. The maps
obtained here as transformations also are reminiscent of maps
produced in the middle ages. Other equally unusual maps can
be considered transformations, ... [Tobler W.R. 1961 pp.140141]
[d] Cartograms can also have a propaganda value:
Chimerical cartography was effectively employed in the
propagation of ideas by the Nazi geopoliticians. Dr. K.
Frenzel, addressing the German Cartographic Society in
Berlin, October 22, 1938, declared: "Every map has a
suggestive force! Man is an ocular creature. He reacts to that
which he sees and can take in at a glance." [Boggs S.W. 1947
p.433]
Chapter 3: Artificial Reality
54
We shape our world for our own purposes, to see where we are
ourselves and to see what each other has. We are still imagining the
abstract spaces to draw ourselves upon. They have never been
naturally given.
3.3 Area Cartograms
Cartograms are maps in which the particular distortion chosen is made
explicit43. Area cartograms are drawn so that areas representing places
on the paper are in proportion to a specific aspect of those places
(Tobler W.R. 1961, 1963b, Forster F. 1972, Wilkie R.W. 1976). The aspect most
commonly chosen has been total human population, another age might
have chosen only adult men. Population cartograms give another
shape to the world, reminiscent of ancient medieval projections, so
obviously population centred. By choosing to draw the surface of the
earth as a population cartogram, we give all its people equal
representation in the image (Prints LIII, LIV & LV). In the process we
lose much that is familiar, but then, we do not learn through
familiarity44.
[e] Your point of view very much affects whether you see
propaganda, distortion, or realism:
A second map in Der Krieg 1939/40 in Karten uses similar
techniques in its aim to persuade the Americans that Britain
was the real threat to the Monroe Doctrine. It shows the entire
western hemisphere and once again, demarcates states with
strong political ties to the British Empire in yellow, with the
smaller Caribbean countries outlined in large solid circles. As
a result, the mass of yellow accounts for an area larger than
the USA and Mexico combined, which obviously distorts their
true proportions. This impressionistic map clearly
communicates the threat of British imperialism [Murray J.S.
1987 p.241]
transformation of income space in Tacoma, to test whether
shopping centres were in fact regularly spaced in relation to
demand. A second, less sharply focussed strand in the use of
cartograms can be identified. This strand is concerned mainly
with conveying a clear visual impression of a static spatial
distribution, with the cartogram providing a base on which
related information can be depicted, generally by means of
choropleth mapping. [Holmes J.H. 1974 p.218]
44 [a] Even for unfamiliar locations, cartograms simplify:
Long classroom experimentation by the author has proven (at
least to him) that geographic locations are easier to learn when
map "clutter" is eliminated. [Fuson R. 1970 p.xi]
43 [a] A simple definition of a cartogram is that:
[b] And in the new electronic information age:
A cartogram is a combination map and graph. [Wilkie R.W.
1976 p.1]
[b] Some well known examples include:
Perhaps the two most commonly quoted cartograms (or
transformed maps) are Edgar Kant's logarithmic map,
designed at Hagerstrand's behest, to depict relative migration
distances from a specific origin, and Arthur Getis' map
Videotex is a special form of TV image, and behavioural
research on cartography and videotex is very limited. In one
of the few published studies, Mills (1981) has argued that the
most effective maps on videotex may be those that distort
spatial reality. He also argues that as cognitive capacities such
as visual memory ability vary from individual to individual,
some viewers may be much more able to learn from maps
than others. [Taylor D.R.F. 1985 p.31]
Chapter 3: Artificial Reality
55
Population cartograms far outnumber any other kind. They were first
drawn around a hundred years ago, and largely ignored until the last
couple of decades. In the 1960s algorithms were developed to
construct them by machine, as their manual creation has always been
immensely tedious. Most used today are still generated manually,
although interesting mechanical means were also developed for their
production. Much effort went into this pioneering work, because much
was hoped of the media. What can be done today is largely a
realization of what people were trying to achieve in the past45.
The depiction of electoral geography is a frequent use of population
cartograms. Here the population base is often the electorate. On any
traditional map of an urbanized country, the majority of political
constituencies are literally not visible to the naked eye (Prints LVI &
LVII). The problem is particularly acute in countries such as Canada
and Australia, but still fundamental in all other regions. The argument
is not that the conventional map distorts the message, it is that it
cannot even contain half of it. Numerous insets, and insets within
45 [a] Research on cartograms has been lead by the work
of Waldo Tobler:
Besides offering new types of maps and atlases it seems to be
indispensably necessary to develop new methods of use and
evaluation of cartographic representations. In pattern
recognition not only cartography is interested, but also
geosciences. The efforts of W. TOBLER from my point of
view can be regarded as a promising beginning in elaborating
suitable methods of analysing spatial pattern and transforming
cartographic figures for new considerations. [Kretschmer I.
1978 p.36]
[b] A mystical uniform plain was being sought:
Geography, like other mathematized sciences before, is
searching for the correct coordinate system and point of
origin. Tobler is our Copernicus. The Geographic Projection,
the one we still seek , the one much more important than the
infinite projections mastered, is the Uniform Plain, which is
the geographic equivalent of "other things equal" assumptions
in other sciences. Once the space is properly projected, the
patterns (our primitives are the dimensions) both probalistic
and extremum (with the function to be minimized some
concept of nearest) should emerge and be more testable.
Somehow the patterns and the coordinate system should be
related functionally. [Bunge W. 1968 p.31]
[c] And several computer algorithms were written to find
it:
The original computer algorithm was constructed by Waldo
Tobler, which generates pseudo-continuous cartograms
(Figure 2.7) according to partial differential equations (Tobler,
1963). Tobler's algorithm fixes a planimetrically correct base
map to an underlying continuous surface, which is then
projected onto a distorted plane, which represents the variable
transformation (Dougenik, et al., 1983).
The Tobler algorithm is regarded as imaginative but highly
inaccurate, slow due to the number of iterations required by
the algorithm, and guilty of producing an over generalized end
product (Figure 2.8). This led Nicholas Chrisman to write a
competing algorithm which uses a different distorted plane
approach. In this scheme, each region or polygon has an
amount of "force" applied to it based on the variable's value
being mapped (Dougenik, et al., 1983). The implementation of
the Chrisman algorithm (Figure 2.9) currently exists as part of
the mainframe GIS package ODYSSEY (Corson-Rikert,
1983). [Torguson J.S. 1990 p.20]
Chapter 3: Artificial Reality
56
insets, or dynamic zooming using a computer could be employed to
try and see what is going on, but they cannot form what is required —
a single gestalt image, a unique impression (Print LVIII).
Medical epidemiology is another large and rapidly expanding area
which employs cartograms. What is the point of drawing diagrams of
the incidence of disease using map projections which literally hide the
cases you are trying to map? These cartograms are most useful in
searching for structure in the incidence of a disease which is thought
to strike the population at random. Where is the illness most prevalent,
and how is it related to other features of the social landscape?
Population cartograms will gain in popularity as they become easier to
employ and better understood in general46. For visualizing the spatial
distributions of social structure there is no alternative, if we wish to
see the detail of substance. A traditional map can take many
projections, so too can a population cartogram. An infinite number of
correct population cartograms can be constructed for any aspect of
any set of places. This is both an asset, as it allows us to choose to
what other properties we wish our abstract space to conform, and a
46 [a] Cartograms blatantly proclaim the distribution of
population:
First, the projective distortions not only permit the discovery
of the prototype inherent within them; they call for it actively.
[Arnheim R. 1970 p.51]
[b] They allow us to entre an artificial reality and gain new
knowledge:
The distortions of geographic space are wilder than any other
science. Einstein's simple spatial bending is child's play
compared to the weird house of mirrors geographers must
straighten. Point to your home. Now go "straight" home. Do
you follow your arm through the wall? How crooked your
"straight" path is as you wend and wind your way "straight"
down the geodesic time path to your house. What sort of
mapping is necessary to show all these "straight" paths from
all points as straight? In chapter two it was emphasized that
not even the order of space is maintained. Tobler proved to us
that space can repeat. It fascinates the curious to notice how
the refraction of light seems to bend a hand thrust into water.
In geography, thrusting your hand into the proper space can
cause it to reappear in 15 places simultaneously if the space is
folded under itself five times. In such an Alice in Wonderland
World it is a miracle that geographers have discovered any
underlying order whatsoever. How much progress would the
astronomers have made if they had had to make their
observations through the wildly swirling lens through which
we must peer? Nor does the mapping of the isotropic surface
end with the transformation of such regions as Iowa. The
entire globe itself has to be reprojected. What Tobler calls "the
geometry of geography" meaning the curvature of the
geodesic surfaces after Gauss, is hardly confined to sheets of
paper. These projected globes will show little resemblance to
current desk top models which seem to have won so many
geographers to their hearts as "distortion free". Since the
notion of reprojecting a "perfect" globe makes Tobler's point
so clear, I suggest to the profession that we refer to them as
Tobler Globes in his honor. [Bunge W. 1966 pp.242-243]
Chapter 3: Artificial Reality
57
hindrance, as the superficial appearance of the same population
cartogram will vary from one author to another47.
3.4 The Nature of Space
The challenge is to construct a graphical representation of real space
which portrays sections of the community in places as areas in
proportion to their populations. This is usually achieved through an
iterative transformation of the conventional Euclidean geometry and
topology of the area, slowly stretching some parts, while squashing
others, until the places' sizes are in proportion to their populations,
instead of being in proportion to their land area.
This process can be tempered by deciding that the topology of the
space should be preserved throughout the transformation. In other
words, that these places which were neighbouring should remain so
after transformation, and that those which were not so, should not
become neighbours. Thus we are aiming to create a topologically and
geometrically correct contiguous area cartogram48. Even so, it is still
possible to create a multitude of these for any given area (Golay M.J.E.
1969, Sen A.K. 1975, 1976, Coombes M.G. 1978).
47 [a] Not all writers favour the use of cartograms:
Exhibit 193 represents a method of showing data for states by
drawing the area of the state on the map proportional to the
numbers represented. Such maps, however, are not superior to
the dot or bar maps just described (Exhibits 183 to 189,
inclusive) for showing distributions of size. In many cases the
method illustrated in Exhibit 193 would result in the states
being so distorted that little if any resemblance of their true
shapes would remain, and even their relative positions would
be inaccurate. It is much more difficult to compare the
irregular areas on such maps than it is to compare either
circles or bars. [Riggleman J.R. 1936 pp.179-180]
[b] Some dismiss them, but offer no alternative:
There are three basic misuses of area in mapping census-type
numbers: 1. Failure to compensate for differences in the areas
of the collecting units. 2. Including map area in the symbol
when land area is not part of the number being mapped. 3.
Using a base map that distorts area. There is an ever
increasing number of canned computer mapping programs.
These make it easy for anyone with a handful of numbers to
produce a map, but seldom do the instructions for the
program's use include warnings on their possible misuse.
Choropleth-type maps of census-type numbers are one of the
most dangerous traps for the unwary. [Williams R.L. 1976
p.213]
48 [a] Continuous area cartograms are not necessarily as
challenging to create as some would claim:
The most valuable and constructionally challenging type of
cartogram is the contiguous cartogram (Figure 2.6b), where
proximal relationships and contiguity are maintained. Note
that some shape properties have been sacrificed, but the
topology still gives an appearance most like a real map. There
is no generalization of the number of vertices which occur in
the latter cartogram type. No boundary generalization
theoretically takes place. The map like quality of the
contiguous cartogram is cause for its high appeal and
desireability. Its creation presents the highest challenge to
cartographers using both conventional and computer-assisted
techniques. Contiguous cartograms are the most difficult and
time consuming to construct (Muehrke, 1978), though a welldesigned cartogram of any type requires much forethought in
its inception and execution. [Torguson J.S. 1990 pp.17-19]
Chapter 3: Artificial Reality
58
Further constraints can be added. The most common are that the outer
boundary of the area be preserved and that the lengths of interior
boundaries be minimised, so creating a cartogram, the shape of which
looks familiar and whose interior is least convoluted. While it is
possible to achieve both these aims simultaneously, happily now
producing a unique solution, they are somewhat contradictory.
Maintenance of the original perimeter dramatically restricts
simplification of the internal boundaries. A population cartogram of
Britain was produced which precisely preserved the original coastline,
but a confused internal structure resulted (see Print XLIX). Here, I
concentrate on creating the simplest population cartogram, only
roughly following the physical outline of Britain, so that the patterns
are depicted with the least visual distortion and the greatest interal
detail49 (Print LIX).
[b] For instance, when producing cartograms:
The assumption of continuity of a distribution is often not
warranted. The data are often in the form of discrete locations,
as on a population dot map, or grouped into areal units, such
as census tracts, or refer to areal units rather than infinitesimal
locations, as land values which refer to specific parcels of
land. In these cases an analytic solution is usually not feasible
and rule of thumb approximations are useful. [Tobler W.R.
1961 p.155-156]
[c] Preserving contiguity completely often produces
confusing twisted images:
One other striking feature of map (C) compared with map (A)
is that many of the registration districts have become very
long and thin. This is a common feature of isodemographic
maps, especially near the margins, and arises from the need to
pack the units properly while at the same time preserving the
abuttments. [Cliff A.D. & Haggett P. 1988 p.60]
49 [a] The circular cartogram can be seen as a
development of the proportional circle and dot maps:
In any urbanised country, however, it is quite impracticable to
achieve a happy combination of map scale, dot value and dot
dimension which will give a satisfactory representation of
both country and town areas. The use of different sized dots
can go a little way towards the solution of this problem, but
recourse must always be made to the employment of
proportional symbols, or overprinting of population totals for
major urban areas. [Dixon O.M. 1972 p.20]
[b] It is interesting that proportional circles have to be
rearranged in conventional mapping:
It is generally unsatisfactory to use small, fixed-size symbols
for such mapping, so proportional symbols (possibly shaded)
are often used. Here luck also plays a critical role — to get
such a computer map satisfactory on the first run is to be
extremely fortunate. Iteration must therefore be expected —
figure 6.2 illustrates an example from a second iteration in
which certain of the centre points of areas were edited to
minimize symbol overlaps. Of course, the human cartographer
is much better than the computer in planning the map
production, but to draw such a map manually would take very
much longer than the twenty seconds which it took to compute
and even the twenty minutes it took to draw. [Rhind D. 1983
p.182]
[c] An analogue simulation was used to produce the
cartograms shown here:
I feel that at least in our initial period of theoretical research,
we will find analogue computers of great use. For identical
reasons, I predict we will seek to solve our intractable formal
theory by approximate methods and the use of computers, i.e.,
we will beat our problems to death with machines.
Mathematicians will detest the lack of elegance, but notice
that while the mathematics of such a strategy may be gauche
and muscular, the theory itself can be completely lithe and
uncluttered. It is not uncommon in the history of science that
the scientific theory is simple while the mathematics required
to operate the theory gracefully, remains behind and must
catch up later. [Bunge W. 1966 pp.284-285]
[d] Many past hurdles have also been overcome towards
the possibility of producing linear cartograms:
Unfortunately, cartographers have serious technical problems
to resolve before this powerful technique becomes generally
operational. And, whatever the form of the transformation,
problems arise first in specifying meaningful transformations
and secondly in preserving desired properties such as
boundary, shape and continuity relations. The search for
permissible and meaningful transformations, the degree of
control attainable with each, and the reversibility of the
transformation processes provide important topics for future
research. [Muehrcke P. 1972 p.46]
Chapter 3: Artificial Reality
59
The algorithm which was developed to create the area
cartograms worked by repeatedly applying a series of
forces to the circles
representing the
The Forces
places. Circles attract
those they are
Acting
topologically adjacent
Repulsion
upon
to; the strength of this
a single
attraction being
circle.
greater the larger the
distance is between
them and the longer
their common
boundary. They repel
Attraction
The practicalities of the
situation — designing an
algorithm which could be
successfully implemented
with the equipment and
knowledge that we have
today — led to a further
compromise in this work.
those with which
The shapes of internal
they overlap, with a
The Effect
places were made circular
strength proportional
of Friction
to the overlap.
(Figure 8), and hence as
Friction is applied to
Each
prevent
circle moves
simple to gauge as possible.
unsatisfactory local
towards the other
solutions being
Strictly
speaking
the
at a rate equal to a
quarter of the distance
contiguity and topological
A More Complex
between them.
Situation
constraints
were
now
to Resolve
settled on too soon.
broken, but in practice the
Overlaps can
The repulsion factor
must always be
remain if the
vast majority of places still
slightly greater than
forces simply
the attraction or else
balance.
bordered
their
former
where, for example,
each of four zones are neighbours
after
all connected to the
other three, an overlap transformation.
Various
will always remain.
methods could be employed
Figure 8: The Algorithm at Work
to make the cartogram, once
created, appear continuous
again — building Thiessen polygons around the circle centres is
simplest (see Print CLXXII).
Thus we can now create a space of places, the areas of which are in
proportion to their populations, and which maintain, as far as possible,
their original topology. Such a cartogram is particularly useful for
visualization as it presents a much clearer image than one which
would have to twist and wind to satisfy strictly all the conditions —
all of the time.
Chapter 3: Artificial Reality
60
3.5 Producing Illusions
What places should now be chosen, out of which to build these
abstract spaces? How will the choice of which hierarchy and division
of areas to use alter the image (Prints LX & LXI)? Cartograms from
the same population-count of all the major administrative divisions of
Britain have been constructed and many are shown here. The answer
to the question of robustness is that the choice of areal unit does not
substantially alter the final shape of these images — a reassuring
outcome. In fact all thoughtfully constructed cartograms of Britain
tend towards the same rough structure, which loosely implies that an
ideal solution exists. There is a sense of aesthetic acceptance to be
realised also. The following papers document various attempts to
control or automate the process: Hunter J.M. & Young J.C. 1968, Hunter J.M. &
Meade M.S. 1971, Skoda L. & Robertson J.C. 1972, Tobler W.R. 1973a, 1986c, Olson J.M.
1976b, Kadmon N. & Shlomi E. 1978, Eastman J.R., Nelson W. & Shields G. 1981,
Dougenik J.A., Niemeyer D.R. & Chrisman N.R. 1983, Nelson B. & McGregor B. 1983, Cuff
D.J., Pawling J.W. & Blair E.T. 1984, Selvin S., Merrill D., Sacks S., Wong L., Bedell L. &
Schulman J. 1984, Dougenik J.A., Chrisman N.R. & Niemeyer D.R. 1985, Kelly J. 1987,
Cauvin C., Schneider C. & Cherrier G. 1989, Torguson J.S. 1990.
This degree of autonomy in the shape of the cartogram, from the
influences of the areal division which was used to create it, was only
achieved by choosing a careful definition and measure of contiguity.
Two places were said to be contiguous if they shared a common
border or were linked by a major tunnel, road or rail bridge. The
measure of contiguity was not absolute, but estimated as the
proportion of the perimeter of an area made up by the border in
question, or the length of estuary coastline which a bridge, for
instance, rendered traversable (see Print L).
Chapter 3: Artificial Reality
61
The following set of equations show how the damping
factor K of 0.25 was derived for the cartogram algorithm.
x is position, v is velocity and An used as an ansatz:
The algorithm for creating
the cartograms began with
each place at its Euclidean
vn+1 = K(vn - xn)
location, represented as a
xn+1 = xn + vn+1
circle whose area was in
proportion to its population.
xn+1 = xn + K((xn - xn-1) - xn)
Overlapping circles repelled
each other while circles were
xn+1 - xn + Kxn-1 = 0
attracted to their neighbours
n
n+2
n+1
A - A + KA = 0
in relation to the strength of
n
2
their contiguity measure.
(A - A + K)A = 0
Places which bordered the
by factorising we
n
can see that either: A = 0
sea expressed a degree of
inertia because part of their
2
or: A - A + K = 0
perimeter, being coastline,
did not make up a common
To avoid oscillation the solutions to A must be real,
therefore the discriminant must be positive: 1-4K >= 0
border, and this helped to
Thus the largest non-oscillating K is 0.25.
maintain
prominent
Figure 9: Deriving a Constant
peninsulas
and
other
landmarks. Thus, although the exact shape of the coastline was
sacrificed, many of its key locational features were retained.
The sustained combination of all these forces in parallel (Figure 9)
created the new pictures of Britain used in this dissertation50. An
50 [a] An example has been produced which satisfies the
following criteria:
Though the example is very simple, there are still an infinite
number of solutions, but some seem more appropriate than
others (see Fig. 6.6). Preservation of the internal topology is
one condition which might be applied; preservation of the
shape of the boundary is another, etc. [Tobler W.R. 1961
pp.156-157]
[b] The cartograms used in this work are pseudocontinuous:
There are two types of contiguous cartograms. The first is the
pseudo-continuous cartogram (Figure 2.6a). Pseudocontinuous cartograms depict regions like a continuous map,
but are endowed with the "pseudo" label (after Muehrke,
1978) due to the generalization of the polygon's topological
structure. This can be contrasted with the contiguous
cartogram, where the topology has been retained (Figure
2.6b). [Torguson J.S. 1990 p.17]
[c] A chaotic environment was initiated to produce these
cartograms:
It is impossible in principle to predict in the general case
whether the forces tending towards chaos or the forces tending
towards quiescence will ultimately dominate the dynamics of
the system of whether, for that matter, neither one will ever
dominate. Indeed, for many such systems, the conflicting pulls
towards order and chaos seem to provide an essential tension
which keeps the ongoing dynamics on an indefinitely
extended transient, far from equilibrium. [Langton C.G. 1986
p.129]
Chapter 3: Artificial Reality
62
algorithm was used where the solution evolved towards the desired
goal (see Appendix A), releasing and tightening constraints to allow
the conditions to be attained, and to ensure that the final pictures
looked acceptable.
3.6 Population Space
The very shape and layout of the cartogram is of interest even before
we begin to use it to depict other information. The population
cartogram tells us a lot about the human geography of places — how
they are related to each other in a new and intriguingly unfamiliar
way51 (Prints LXII & LXIII, Warantz W. 1975, Tobler W.R. 1976a, Wilkie R.W.
1977, Finamore P.M. 1982, Härö E.S. 1989, Löytönen M. 1991).
The population of Britain is more drastically dominated by London
than most human geographers would imagine. Greater London itself
contains over an eighth of the population. Combined with those areas
under London's immediate influence in population space, we can
count nearly half the people of the island. This structure is repeated
[d] Here, the density of people is used to curve the space
they live in:
So gravity can be explained by assuming that matter curves
space. But why should matter do this? Why should matter
curve space?
One explanation is that space curvature is what matter is.
William K. Clifford first proposed this theory in an 1870
paper called "On the Space Theory of Matter": [Rucker R.
1984 p.82]
51 [a] Some people thought it would not be possible to
produce such pictures as are shown here:
Extreme shape distortions associated with these sharp density
gradients present such severe problems that it is doubtful
whether isodemographic maps could be used for testing
locational characteristics when both urban and rural
populations are shown on the one map. One quails at the
problems of presenting Australia's population in similar detail,
with 63 percent of Australians being located in the ten largest
cities. [Holmes J.H. 1974 p.218]
[b] Cartograms can be useful for many purposes:
The SMRs so calculated have been portrayed on a specially
prepared demographic base map (Fig 7.17). The demographic
base map is designed to relate the SMRs to the local
populations at risk to death from cancer and to complement
the geographical map which related SMRs to the areas within
which such populations reside. The advantage of the
demographic map is that main centres of population such as
London, Birmingham, Glasgow or Liverpool assume
increased proportions while geographically large areas with
numerically small populations, such as Dyfed in Wales or
Inverness in Scotland, are reduced in area relative to the UK
as a whole. Disadvantages of the demographic map include its
somewhat unfamiliar shapes, distortion of reality and loss of
continuity. [Howe G.M. 1986 p.131]
[c] These cartograms are the first to successfully show the
human geography of Britain at this fine scale:
This is attractive at first sight but has several disadvantages.
Distances within census tracts in the DEMP are not
necessarily population-adjusted; the resulting display bears no
relationship to geographical space and cannot be used in
conjunction with, for example, maps of land use and, finally,
the algorithm has never been successfully applied to U.K.
census tract boundaries that are highly irregular. [Alexander
F.E., Ricketts T.J., Williams J. & Cartwright R.A. 1991 p.159]
Chapter 3: Artificial Reality
63
recursively — Glasgow making up more than half of Scotland and
dramatically influencing the geography of the rest of that country. The
areas of influence of the other great cities are clearly shown, as is how
they compare and combine, are divided and divide space up amongst
themselves (Print LXIV). The separation of Wales into North and
South, and Scotland from England, highlights divisions which are well
known, but missing from conventional depictions.
To make the reading of the cartogram simpler, and to learn more
about population space, we can transform the major networks of
infrastructure, which service the population and along which they
move, to lie upon the space. The layout and purpose of the mainline
railway network is clear on the cartogram (See Print XLV). It provides
a series of arteries attempting to reach all areas equitably, in
accordance to their populations.
The road network of Britain is much more complex, and only the
motorways and designated main routes are shown in the illustrations
drawn here. Again the even spread across the country can be noted
(See Print XLVII). Intriguingly though, the network is most sparse in
population space where it is most concentrated on the ordinary map —
in London. It is no wonder that congestion is greatest where there are
least roads per head of population. Here the physical practicalities and
human desires combine, so that both versions of reality are useful in
understanding the situation.
3.7 Stretching Spacetime
The difficulty of constructing these area cartograms is due to the fact
that they are two-dimensional entities (Prints LXV & LXVI). Onedimensional cartograms are simplicity itself to produce. Imagine a
Chapter 3: Artificial Reality
64
Many different types of area cartogram can be imagined.
Here are some world scale examples -
one-dimensional, temporal
cartogram of the population
1950
2000
One dimensional: -3000 1000 1600 1800 1900
of the world from when the
World population
over time:
Length in proportion to population between the dates
species began until the
One-and-a-half dimensional: World Population over time
present
day.
Such
a
by Continent:
Asia
America
cartogram would consist of a
Europe
Africa
Australasia
single line, with dates
Two-dimensional: World population over space by
marked along its length
continent:
(Figure 10). The distance
between any two dates
would be in proportion to the
Two-and-a-halfnumber of people living
dimensional: World Population with income as height:
between those times. Thus,
the time line would be very
compact at the beginning,
having its years widely
spread towards the end.
More importantly, it is
unambiguously the only
Figure 10: Many-dimensional
solution to the problem. The
Cartograms
number of dimensions of a
cartogram can vary, limited in type only by the imagination. A halfway house can be envisaged of a one and a half-dimensional
cartogram, where some information independent of time is depicted
vertically up from the time-line cartogram, for example, the
proportion of the population living in the various continents. Such a
cartogram would be just as simple to construct and while appearing
two-dimensional the information is of one dimension (place) within
another (time).
The term linear has already been reserved in the literature on
cartograms to mean something other than one-dimensional. A linear
cartogram is one where the distances between places is deliberately
Chapter 3: Artificial Reality
65
altered for a given reason52, the most well known of these being to fit
place names on, and so simplify, a map of a city's underground
system. Another well known option is to make the distances between
places proportional to the time or cost required to travel between
them. This can only be achieved for the, say, shortest travel time
distances between all places, when the two-dimensional space in
which the linear cartogram resides, is itself warped in the third
dimension.
What happens when we go beyond the two spatial dimensions and
also attempt to incorporate time? At one level an analogue to the one
and a half-dimensional cartogram can be made. The linear cartogram
where distance is made proportional to travel time can be projected as
a surface above an area cartogram. Thus a two and a half-dimensional
linear area cartogram is created as a surface of travel time above
population space (Angel S. & Hyman G.M. 1972, 1976).
Even simple two-dimensional population space changes in time (Print
LXVII), dramatically so over long periods. A series of area cartograms
has been constructed for this dissertation of the British electorate by
52 [a] One definition of a linear cartogram is that:
A linear cartogram operates like an area cartogram but instead
of varying areas with values it is the map distances which
vary with values. Its construction is analogous to the
azimuthal equidistant projection, but rather than physical
distance varying with map distance — cost of effort of travel
are used. [Lai P.C. 1983 p.33]
[b] The linear cartogram problem and the need for its
creation has been appreciated for some time:
In transportation systems analysis, it is convenient to
represent the transportation system mathematically by means
of a network. For many purposes, such as the problem of
estimating travel flows in the network, the mathematical
properties of nodes and links are sufficient for the analysis.
For other purposes the map image representation of the
network may offer important visual clues. The field of
interactive computer graphics, for example, explores the
coupling of human intuition, aided by visual clues, with the
computational power of computers. Transportation networks
are abstracted from the locations of streets and intersections,
and the graphic representation of the network resembles a
road map. Such maps are traditionally produced using
Euclidean geometry as the basis for the representation of the
spatial relationships. The choice of Euclidean geometry has
obvious advantages in the study of many physical phenomena,
but it does not always represent accurately the properties of
networks that are of interest in the study of transportation
phenomena. For example, it is well known that travel time
through a network is a more important determinant of
behaviour than travel distance. Travel speeds vary markedly
from link to link in transportation systems, and these
variations result in a space that is non-Euclidean in
experience. The problem is to represent these non-Euclidean
properties graphically, in a way that will aid analysis
processes that depend on visual information. [Clark J.W. 1977
p.195]
[c] Disease mapping could benefit from linear, as well as
area, cartograms:
The analysis thus indicates the importance of viewing the
relationships between places in terms of the metric (such as
time or cost, for example) which is most appropriate for the
problem being tackled. The plotting of places in terms of
accessibility metrics like time and cost distances is
particularly valuable when communicable diseases are being
studied and may frequently provide a fresh perspective on the
disease patterns occurring. [Cliff A.D. & Haggett P. 1988
p.267]
Chapter 3: Artificial Reality
66
parliamentary constituency from 1955 to 1987 (see Appendix B). The
ten images show the gradual deformation of the space as the electorate
grows nationally, the South East swelling in particular while the inner
cities shrink. The fact that the definition and number of places
changed also over this period, was easily incorporated.
A true three-dimensional volume cartogram of population spacetime is
difficult to imagine. Such an image would have to be based upon the
axiom of giving each life equal representation rather than each area.
As lives have temporal extent they would have to be drawn as life
lines. It is hard to imagine what further constraints would be
employed in constructing such spaces. Obviously volume should be in
proportion to individual lives, and contiguous places in space should
touch each other, as should places connected with themselves, both
forwards and backwards in time. If we then choose to minimise the
area of internal boundaries, which are now planes rather than lines, we
will warp time into space and vice versa53. A place which many
people left will slip back in time, a place growing in size pushes
forward. What are we creating and how can we understand it — let
alone view it?
53 [a] Even more bizarre transformations are conceivable:
Turning to the map of the world, how far from London can
you extend your military power before you will lose one
hundred thousand men? This "circle" obviously moves much
farther over sea than into Europe where military resistance,
say from the French, would make such a move very expensive
per geographic mile. A map of the earth with hundredthousand-man-lost circles centred on England in 1850
establishes the most distant place, not as New Zealand, but
somewhere around Moscow. The paths of least deaths at right
angles to the circles draw another set of real longitudes and
latitudes. Moscow is antipode, the opposite side of the earth,
the "down under" from London. To conquer the world is to
conquer Moscow, not Auckland, and this is why the British
kept moving toward Moscow from the Crimea the walls of
Peking, the Khyber Pass, from Vladivostock. All "paths" from
London lead to Moscow, and thus the mysticism of
Mackinder's geopolitic is explained. [Bunge W.W. 1973
p.286]
[b] Geo-political information often needs to be
transformed:
It would seem that here, since many of the relationships which
are displayed are not most significantly geographical, but
rather operational in nature, and that a schematic map of some
sort would have been much more effective. It is not miles or
kilometers across the surface of the earth in which these geopolitical factors are arranged, but rather an interesting — but
complicated — series of topological relationships. [McCleary
G.F. 1988 p.148]
[c] We are limited only by our imaginations:
In the opinion of this author, the value of such maps can be
great for geographers and other behavioural scientists — a
value which seems limited only by the imagination of the
scholars whose tools they should be. [Lewis P.F. 1969 p.406]
[d] But we must remember that others have to understand
what we produce:
As a rule, the novel, dramatic character of cartograms may
deceive unwary map readers. Great care and skill must be
exercised when dealing with this particular type of map. The
advantages of cartograms are substantial enough, however,
that geographers would do well to gain sufficient
sophistication to handle these maps effectively. [Muehrcke P.
1981 p.27]
Chapter 3: Artificial Reality
67
The computer algorithm employed here could be adapted to create all
the variants mentioned above. The elusive formulae that people have
searched for in the past, to achieve these transformations in a single
step, are no longer required.
The nature, creation and use of spaces above two dimensions is the
subject of the last part of this dissertation. For now we see how the
unusual, but understandable, two dimensional population spaces can
be gainfully employed in the visualization of spatial social structure.
68
Chapter 4: Honeycomb Structure
Detail cumulates into larger coherent structures; those thousands of tiny
windows, when seen at a distance, gray into surfaces to form a whole
building. Simplicity of reading derives from the context of detailed and
complex information, properly arranged. A most unconventional design
strategy is revealed: to clarify, add detail.
[Tufte E.R. 1990 p.37]
4.1 Viewing Society
Britain is an island, a small piece of land upon which, at the end of the
twentieth century, over fifty million people live. Why study this single
percentage of the world’s population and why study it now? Because
it is convenient, it is understandable and it is timely to do so.
The British state is a convenient unit of analysis; consistent national
statistics are collected on many subjects at regular intervals about all
its constituent elements — often to be mapped54. A single political
body is created from areas covering all its territory, and its territory is
divided into areas which are themselves subdivided into units of local
representation for political election. Its boundary is well defined, and
encompasses almost all the movements of people who live within the
state borders.
54 [a] Although statistics may be collected to be mapped:
The graphic portrayal of census data has always been a
decentralised and in many respects an adhoc affair. After the
superb maps produced by Petermann (partly for the
government) after the 1841 and 1851 censuses, little ‘official’
mapping was done until that carried out after the 1961 Census,
by what is now the Department of the Environment (DOE). A
tradition grew up that individual geographers mapped those
elements of the census in which they were interested and in
1968 one of the Transactions of the Institute of British
Geographers consisted of a set of twelve maps of variables
from the 1961 data. This 7-year delay in map availability was
very similar to that after the 1841 census. [Rhind D. 1975 p.9]
[b] The practical problems of doing so continue:
The statistician who compiles data about the present aims to
provide primary data for constructing a very large number of
conceivable comprehensive pictures. Among the mass of
figures collected for this purpose are concealed an infinitely
large number of latent pictures. To transform these latent
pictures into actual ones, to present them in such a way that
they even suggest the basic structure of the other images still
concealed among the mass of figures — this is one of the most
important tasks of the field called census cartography. [Szegö
J. 1987 p.149]
Chapter 4: Honeycomb Structure
69
The British state is an understandable social entity. The definitions of
people, work, places, political parties and groups are simple. Its
history and geography are well documented and easily accessible. It
covers too little land area and too many people to divide easily into
smaller wholes. The country can be travelled from one end to the
other by land, in a day; but only a few of its people can be met in a
lifetime. Britain is understandable because we live in it; we make up
its social landscape. If we cannot see the nature of our spatial
relationship with each other, then what will we be able to see55 (Foley
D.L. 1953, Shepherd J., Westaway J. & Lee T. 1974, Bunge W.W. 1975, Evans I.S., Catterall
J.W. & Rhind D.W. 1975, Massey D.S., Tedrow L.M. & Stephan G.E. 1980, Census
Division, OPCS 1981)?
It is timely to look at the people of Britain now; not only because it
has only recently become possible to look at them in the way we are
going to, but also because of the moment in history which we will be
viewing. After over a century of decline, what makes the British
unique is their position, as a people, who have fallen further on the
world scale than any other group in recent times. Now they are finally
beginning to recognise their position and to take stock of their plight.
How better to do it than by painting pictures of the social landscape
they live in and make up?
55 [a] There are many reasons for mapping census data:
[b] People have been using and mapping census statistics
for many years:
Mapping census data is potentially an extremely effective way
of communicating information for tens, hundreds or even
thousands of different areas through the medium of one
(sometimes small) piece of paper. The extreme example of
census mapping is probably the national maps in People in
Britain, the census atlas based on 1971 grid-square data: these
maps contained between 50,000 and 150,000 areas on each
A4 size page. It is important to emphasize at the outset,
however, that all such ‘statistical’ maps are a complement to,
rather than a substitute for, statistical tables. Moreover, as we
shall see, there are many different ways of mapping census
data, some of which give misleading results. Mapping, then,
can be extremely informative but it can also mislead. [Rhind
D. 1983 p.171]
For a number of years the American Statistical Association
has provided for annual meetings of specialists in the use of
census tract statistics. These meetings have brought under one
roof investigators and administrators concerned with such
matters as “studies of disease, city planning, marketing
analysis, labor market studies, civil defence, church planning,
studies of juvenile delinquency, housing problems” and
“retailing”— to cite a list presented in a recent publication on
census tracts (United States Bureau of the Census, 1958, pp.45). Political scientists have found use for census tract statistics
and methods of urban analysis applied thereto in their studies
of voting (e.g., Gosnell and Schmidt, 1936), and even
psychologists have found the classification of urban areas by
elaborate techniques an interesting exercise (Tryon, 1955).
Foley (1953) gives a lengthy list of census tract studies.
[Duncan O.D., Cuzzort R.P., & Duncan B. 1961 p.13]
Chapter 4: Honeycomb Structure
70
4.2 Who the People Are
The people of Britain are a diverse collection. This small section seeks
only to describe the basic geography of their most simple attributes,
gender and age for example (Print LXVIII), and in doing so to begin to
explore how the detailed
The small area statistics of the 1971, total population
census, consisted of 480 figures for each of 125,476
mosaic of life in Britain can
enumeration districts, some sixty million numbers.
Originally stored in eight character wide slots the file was be uncovered (Figure 11).
half a giga-byte in size, far too large to be easily stored
and repeatedly accessed. A simple form of run length
encoding was customised to compress the file and still
allow the records of individual enumeration districts to be
read instantly. The counts for each cell were stored
sequentially as either a run of zeros, half-bytes (0-15),
bytes (0-255) or half-words (0-65535). The sophistication
of the algorithm was, in deciding whether of not it was
profitable to "drop down" an order of magnitude in the
form of storage used. This was achieved by looking
through the list both forwards and backwards. The
following simplified heuristic was employed:
Define: yesterday, today and tomorrow as the magnitude of the
previous, present and future cell to be encoded.
Then, if the opportunity to lower the magnitude of storage arises
(today<yesterday) continue at the present order while
tomorrow>=yesterday.
With a few other caveats, this rule compresses the file to
just 5% of its former length: 29,861,010 bytes. The more
sparse first section of the 10% population census
file containing 368 cells by 125,462 enumeration districts
(14 missing) is compressed to a file of just 11,314,567
bytes in size. These figures are better than those
achieved by the standard Lempel-Ziv compression
algorithm, but more importantly, the file can be read and
decoded faster than any other configuration (including
the original flat form, due to disc speed restrictions).
Figure 11: Storing the Census
56 [a] There were slight discrepancies between the
detailed 1981 census statistics and preliminary report:
Population present — preliminary Report figures. The
population present in an area on census night is
straightforward to count. The preliminary figures were
prepared by the enumerators, collated manually and published
within three months of census day. The count for England and
Wales was 49.01 million. [Population Statistics Division,
OPCS 1983 p.21]
[b] Those who produced enumeration district statistics
never assumed they could be mapped nationally:
Thus at the ED level, 10 per cent SAS tables are subject to
large errors and will generally need to be aggregated to much
The pictures drawn to
accompany this chapter are
based
on
population
cartograms of one hundred
and
thirty
thousand
enumeration districts56. This
resolution was chosen as the
finest that is possible —
closest to the local scale and
individual realities of life.
Great regional patterns can
still be seen in the images,
but only where they really
exist, not as fabrications of
the boundaries chosen. We
have
collected
more
information on people in the
higher area levels to ensure small variability in the cell
values. The statistics are presented at ED level, primarily to
allow flexible aggregation. [Denham J.C. & Rhind D. 1983
p.80]
[c] The 1971 census figures contained some particularly
obvious inconsistencies:
The census user can, however, experience problems as a result
of adjustment, particularly if he calculates ratios from adjusted
figures. He may, for example, find that the percentage figure
for a set of categories — age groups or occupation groups —
may add up to more or less than 100 per cent of the total
population. [Dewdney J.C. 1983 pp.10-11]
Chapter 4: Honeycomb Structure
71
last twenty years, than over the previous twenty thousand. Is it not
surprising that radically new techniques are required to view the social
landscape57? Conventional choropleth maps at the level of ten
thousand wards have also been included to show how they contrast
with the message of the cartograms (Print LXIX).
Gender is the least ambiguous attribute we give people. A picture was
originally painted of Britain, where each street block is coloured either
black, for over-average proportions of females, or white, for underaverage. The picture not only showed the random variation in this
statistic, indicated by the speckled nature of the image, but also
suggests simple patterns of slight over and under representation. There
are more women in the middle of cities and along the South coast. A
similar two colour technique is used later to show the distribution of
Irish born (see Print LXXI).
To see distinctly the distribution of these proportions, a relative scale
of measurement is adopted — above or below the median level of
females rather than above or below fifty percent. Thus half the area of
Britain, on the population cartogram, is shaded black, the other white.
In this example the picture would hardly alter if an absolute scale were
57 [a] The essential problem of lack of space in mapping
persists:
The main problem in cartography is the counterpart of these
very properties. In a diagram, the geographic component AB...
only utilizes a single dimension of the plane. The other
dimension remains available for transcribing n characteristics.
In a map the component AB... constructs a network that
utilizes the entire plane, in fact, accounting for the map’s
effectiveness. But the y dimension of the plane is no longer
available for the representation of the characteristics, so we
must choose between two solutions:
-either construct one map per characteristic. In this case the
map answers two types of question: Where is a given
characteristic? What is there at a given place?
-or superimpose all the characteristics on the same map. But
then the question: where is a given characteristic? no longer
has a visual answer. Should this question indeed have an
answer? This is the basic problem in cartography with n
characteristics, that is, “thematic” or more precisely,
“polythematic” cartography. [Bertin J. 1981 p.140]
[b] It is the deluge of new digital information which has
lead to the recent need for visualization:
While computers are quite adept at the minutiae of
computation, the human is far more capable, in dealing with
global information, to work as a “Gestalt” recognizer. Hence
we may think of the human-computer symbiosis as a process
whereby the machine fashions and places mosaics of
information (the pixels) so that the human can form an
understanding of the vista being worked. It is the human brain
that will then visualize the process — “see” the drift of a
complex calculation. The computer is used to aid in
visualization of anything. Webster’s describes visualization
as: 1. formation of mental visual images, or 2. the act or
process of interpreting in visual terms or of putting into visible
form. [Staudhammer J. 1987 p.24]
[c] The census small area statistics provided a wealth of
information, initially incomprehensible:
Quite simply there is far too much information to allow
policy-makers,
planners,
geographers,
politicians,
schoolchildren, and others interested in census data for a
particular area to be able to identify easily patterns of
characteristics or features of interest from SAS data without
processing and condensing it in some way. [Openshaw S.
1983 p.243]
Chapter 4: Honeycomb Structure
72
used. Absolute scales would require different legends for all pictures.
The images would also vary greatly in their levels of saturation,
merely because of the use of arbitrary, incomparable measures.
Here, levels above and below the median for Britain, or in some cases
groups bounded by quartile levels, are used to shade the images, to
treat all variables most simply, consistently and comparatively58. The
division into two levels, quartiles and beyond, can be extended until
continuous shading is achieved. This has not been used here, as it is
difficult to shade, or see, such small areas continuously. More
complex colouring schemes are developed later; but, as the images
stand, continuous impressions are gained through the dithered patterns
created by so many tiny discrete shades.
It is interesting to note that shading a great many small areas by four
levels of colour reveals far more information on the complexity of the
structure, than would by gained by showing larger areas with more
detailed schemes. The interesting divisions are often local, and rarely
correspond to administrative boundaries.
The main influence upon the patterns shown by the distribution of
gender is age. Women tend to live longer, so where the population is
generally older it is likely to contain more women. The distribution of
the elderly, in every neighbourhood in Britain can also be shown, in
58 [a] The quartile technique has been adopted by other
researchers:
[b] It is interesting to note that here we are mapping those
things which are said to affort the nature of mapping itself:
The modified quartile classification was developed only after
consideration of the purpose for which the maps would be
interpreted. It was anticipated that many different questions
about socioeconomic conditions would be addressed to each
choropleth map. The potential for answering such questions is
maximised when the map pattern is balanced, that is when the
area occupied by each symbol is approximately equal. For
example, a map with five symbols is balanced when each
symbol occupies approximately one-fifth of the pattern. As a
map pattern is determined by classification, a balanced map
pattern is most likely to be obtained when an equal number of
LGAs is allocated to each class. [Massey J.S., O’Shea J.B. &
Poliness J.S. 1984 p.286]
To discover these rules, we have to read between the lines of
technical procedures or of the map’s topographic content.
They are related to values, such as those of ethnicity, politics,
religion, or social class, and they are also embedded in the
map-producing society at large. [Harley J.B. 1989 p.5]
[c] Divisions of class and race can be seen through
location; they are not aspatial as some suggest:
Of course, there are problems other than those raised by the
north-south divide facing Mrs Thatcher, the government and
the country: other divisions between people of different class,
race and sex that are actually more fundamental than those of
location. [Lewis J. & Townsend A. 1989 p.4-5]
Chapter 4: Honeycomb Structure
73
the same way as the distribution of women was depicted59. The two
maps could be compared, but shortly we will see how both variables
can be shown on a single map, with yet a third variable, the
distribution of the young, introduced through the use of colour.
Variables, such as age, can then be further subdivided to show yet
more detail (or the lack of it — Print LXX).
A traditional means of showing this information is to draw numerous
population pyramids, but these fail to convey the distribution of age
and sex structure across more than a few large geographical areas
(Lawton R. 1968a, Dewdney J.C. 1968, Bureau of the Census 1970, Applied Urbanetics INC
1971, Lycan R. 1980, Warnes A.M. & Law C.M. 1984). Pyramids are, in addition,
not easily visually comparable. What profitable use can be made of
them is examined in Chapter Eight. It must be stressed here, however,
that the use of large areas would naturally dilute the interesting
structures by gross aggregation. Shading and colour is the most
effective way to see local patterns across the structure of a national
population.
4.3 Disparate Origins
Where the people who make up the social landscape of Britain came
from in the past is perhaps as well known as it will ever be. Where the
people who are alive today originate from is less well studied (but see
Pocock D.C.D. 1960, Coates B.E. 1968, Allen J.P. & Turner E.J. 1988, Gibson A. 1988,
Diamond I. & Clarke S. 1989, King R. & Shuttleworth I. 1989, Miles R. 1989,
Ward R. 1989).
59 [a] The processes ordering the distribution of the sexes
are closely interrelated:
[b] The use of many small units can be repeatedly justified
from the errors that manifest when they are not employed:
Retirement areas (predominantly coastal areas) are peculiar
not only because their proportion of elderly population is high
but also because they are associated with high female sexratios (Clarke, 1960). This is partially due to the longevity of
women and the relatively rich female employment
opportunities generated in these areas by tourism and service
provision for the elderly. [Kennett S.R. 1983 p.227]
An odd consequence of the redrawing of county boundaries in
1974 was that Lancashire became an area of concentration of
the elderly. [Warnes A.M. & Law C.M. 1984 p.40]
Chapter 4: Honeycomb Structure
74
Migration is one of the major themes in this thesis. Static pictures of
migration are best provided by looking at the distribution of people
across the country who were born in a particular place. Shading every
street in Britain by the proportion of its population whose birthplace
was in Ireland (North and South) shows the scattering of people who,
in the course of their lifetime, flowed from that one island to live in
this60. We see immediately how strongly the Irish immigrants are
concentrated in particular localities (Print LXXI).
Migration is about mixing. The picture fails if it does not convey the
colourful mixtures of people that result from their movement. Colour,
resulting from the mixing of light, gives the clearest images of the
kaleidoscope of people’s differing origins. Unfortunately not more
than three primary colours can be clearly distinguished from a
mixture. Red, blue and yellow are the most easily combined and
separated by the eye. They can be used to paint the basic picture of
people mixing in Britain, representing proportions of those in each
street born in the three nations of England, Scotland and Wales
respectively (Print LXXII).
The countries naturally contain mostly people born within their
boundaries. England is red, Scotland blue and Wales yellow. Mixing
takes place between them, thus the Scottish border is purple, the
Welsh orange. Disproportionate numbers of Welsh and Scottish
60 [a] Monitoring migration is an age old preoccupation of
the British:
Only four pieces of information were collected about each
person in the 1841 Census. That one of these was birthplace is
indicative of how essential this item was and still is. [Craig J.
1987 p.33]
this table reveals some concentration in ED9 and an underrepresentation in ED10, it does not suggest that the Irish in
Tow Law are heavily segregated. However, if we consider the
proportion of Irish living in each street, a different pattern
emerges, as table 11.2 shows. [Norris P. 1983 p.313]
[c] Here we examine the distribution of all three major
groups of immigrants simultaneously:
[b] Even the enumeration district level may be too coarse
to see some spatial distributions:
A specific and simple illustration of some of the advantages of
microdata can be given by considering the distribution of the
Irish-born in Tow Law, County Durham in 1871. They
constituted some 10 per cent of the town’s population,
distributed amongst the five EDs as shown in table 11.1. while
The emphasis of immigrant community research by British
geographers and other social scientists during the past twenty
years has been overwhelmingly on the Afro-Caribbean and
Asian groups at the expense of those of longer standing and
greater numbers, but perhaps of less visibility. [King R. &
Shuttleworth I. 1989 p.64]
Chapter 4: Honeycomb Structure
75
people mix in London, where there is a dearth of English born,
colouring the capital green (blue and yellow). Closer inspection shows
just how intricate the pattern of mixing is. The white areas on the
picture of the distribution of British-born are made up of streets where
there are shortfalls of all the indigenous nationalities. Here immigrant
populations are most densely settled in the social landscape.
As the indigenous population was divided into three, so too can
people born overseas be subdivided into broad geographical categories
(Print LXXIII). Here we use red for Asian, blue for Irish and yellow
for African and Caribbean61. This image is dominated by black areas,
with high proportions of all three immigrant groups (black represents
the mixing on paper of all three colours, as white represents their
absence).
Tints, tinges and trends of colour in the image graphically show how
the mixing varies. The East side of the West Midlands is more Asian,
the West more Irish in the backgrounds of its people. Those from
Africa and the Caribbean settled in greater numbers in the South than
61 [a] It must be remembered that we are mapping place of
birth, not colour of skin:
For example, of the 322 670 persons born in India living in
Britain in 1971, between one-fifth and one-third (66 139-104
362) may have been Whites born in India (Peach and
Winchester, 1974, p.391). [Peach C. 1982 p.24]
[b] Without mapping we are more prone to make mistakes:
The study of birthplace characteristics identified two major
types of immigrant group. The first composed of the Irish, the
Other Commonwealth and Other Migrants have similar
patterns to the British born. For the latter two types of
immigrant, although the differences between regions and
between urban zones are small compared to the British born,
there are much larger increases in all areas. The second cluster
of immigrant types — the Indian sub continent, Africa and
West Indies — have markedly different patterns with respect
to the British born, especially with respect to intra urban
variations. [Spence N., Gillespie A., Goddard J., Kennett S.,
Pinch S. & Williams A. 1982 p.277-278]
[c] The less aggregation the better:
Further shortcomings exist in census data relating to ethnicity.
Dissimilar birthplace groups are frequently aggregated into a
single category: for example, all those born in the American
New Commonwealth (chiefly the Caribbean) are usually
grouped together in the published statistics. More seriously,
several cross-tabulations in both 1971 and 1981 SAS group all
New Commonwealth-born together. Prandy (1980) has
demonstrated that the ‘social distance’ between Asian and
West Indian groups living in Britain can be as great as that
between either of these groups and the British-born. [Ballard
B. & Norris P. 1983 p.105]
[d] In the geography of migration student population
should be kept in mind:
A third factor contributing to the large inflow into the South
East is that students make up about 15 per cent of all
immigrants and London is popular with overseas students as a
place of study. The first and third of these factors go some
way to explaining the larger than average outflow from the
region. Outside the South East, the West Midlands and East
Anglia were the most attractive areas for immigrants, relative
to their populations. The relatively least attractive place for
immigrants were the North of England and Northern Ireland
(though it must be remembered that the figures take no
account of immigrants from the Republic of Ireland). [Davis
N. & Walker C. 1975 p.5]
Chapter 4: Honeycomb Structure
76
the North of Britain, and so on. These are simple pictures; each block
of streets (forming an enumeration district) is just a coloured dot, but
already reveals, in a picture, details of the diversity of our society
which a search of the literature (see Bibliography) and conventional
image (Print LXXIV) failed to find.
4.4 Lost Opportunities
Now that we know something of who these people are and where they
come from, we want to know what they are doing here. Gaining our
information principally from what the British state collects, we are
interested in how people are employed. Much of the geographical
nature of this is largely determined by the first two questions
addressed above. Children do not officially work full-time, the elderly
are usually retired, men get more work than women, immigrant areas
have often become (if they were not already) those places with the
worst prospects of work. So pattern builds upon pattern and we dissect
the body of information, before we can rebuild it to a better
understood whole.
The simple distribution of the proportion of the population
unemployed shows strong connections with aspects of those
distributions mentioned above. Employment and unemployment is
another major theme to run through this work. If we delve further we
can compare the proportions of economically inactive people (mostly
housewives, the retired, students and children) sometimes called
dependant, with the unemployed and working populations using a
three colour scheme (Print LXXV).
Red is used now for the unemployed, blue for those working and
yellow for the dependants. The picture can show many variations and
Chapter 4: Honeycomb Structure
77
a complex geographical pattern. Orange areas are those with high
proportions of unemployed and dependant people (the Welsh valleys),
green indicates many working and dependant people living in the
same places (the Home Counties) and purple shows blocks where high
numbers of people are working while many others are simultaneously
unemployed (parts of London for instance where there are relatively
few dependants). Without this sophistication of colouring we might
not have realised that such areas could exist at all.
Traditional maps fail altogether to portray distributions such as
unemployment (Print LXXVI). They suggest a massive divide between
the north and south by emphasizing the fate of those living in rural
areas — Scottish crofters against London stockbrokers. In fact there
are more unemployed people, and stronger concentrations of them, in
the South than people living in the North East of England. What
appear to be black-spots on the maps are great regions of joblessness
in population space. Finally, it is the steepness of the slopes between
the places of prosperity and poverty that is our concern. The depressed
areas abut on the most fortunate62.
When we subdivide those who work (and did work) into what they do
as a living, we do not find such close spatial affinity. Again, we are
limited to three categories, by the ability of our eyes and flexibility of
62 [a] It may, perhaps, be surprising to learn that in the
1980s:
Although the percentage officially unemployed in Greater
London is a little smaller than average for Britain the city
holds the largest concentration of unemployed in the
industrialised world, and the real total is at least 150,000
larger than the total of over 400,000 admitted by the
Government. [Townsend P. with Corrigan P. & Kowarzik U.
1987 p.29]
[c] Inequality is the crucial ingredient of deprivation:
We shall hold that the most severe deprivation exists where
the scores of disadvantage are high, where they affect the
largest number of people, and where there is the most crass
contrast between these areas and the advantaged periphery.
[Begg I. & Eversley D. 1986 p.55]
[d] Other conurbations also exhibit sharp divisions:
[b] London is clearly a sharply divided city:
Eversley and Begg’s (1985) nation-wide study of deprivation
indices for urban areas, undertaken for this research
programme, shows that on a wide range of indicators there is a
steep gradient in conditions between inner, outer, and fringe
areas of London. [Buck N., Gordon I., Young K., Ermish J. &
Mills L. 1986 p.12]
The difference between inner Birmingham and the West
Midlands southern fringe is 3.80 — the steepest in the
country. Less than 10 miles separate some of the worst
conditions in the country from some of the best. [Begg I. &
Eversley D. 1986 p.75]
Chapter 4: Honeycomb Structure
78
our imaginations, but three is enough to form a strong impression of
the essence of social spatial structure.
4.5 Work, Industry and Home
Occupations can be divided into three broad groups according to how
much people are paid. These groups correspond closely with the
general nature of the work (Figure 12). The group commanding the
highest income are professionals: managers, employers or landowners, often university educated. The middle section are termed
intermediate in this dissertation and include foremen, technicians,
skilled labours and those in
The occupation groupings used in this dissertation are
defined by OPCS (1981, pp.24-29). The three
well-paid white-collar jobs.
combinations chosen (using the New Earnings surveys
of 1971 and 1981) were of socio-economic groups:
The lowest paid group are
1: Managers in central and local government.
the supervised, made up of
2: Managers in industry and commerce.
3: Professional workers - self employed.
unskilled
workers,
4: Professional workers - employees.
agricultural labours and
13: Farmers - employers and managers.
5: Ancillary worker, artists, foremen and supervisors. those poorly-paid in service
8: Foremen and supervisors - manual.
jobs. Colour blocks of
9: Skilled manual workers.
12: Own account workers (other than professional). streets, by the proportion of
14: Farmers - own account.
the households whose heads
6: Junior non-manual workers.
belong to these categories:
7: Personal service workers.
10: Semi-skilled manual workers.
blue, yellow, and red
11: Unskilled manual workers.
15: Agricultural workers.
respectively, and you can see
The industrial groups were taken from the following
one of the most basic
amalgamations of 1980 Standard Industrial Classification
based codes, referred to as "Broad Industrial groups":
divisions of the social
Code
Description
NOMIS Class landscape — the geography
1: Agriculture, forestry and fishing
0
of class (Print LXXVII,
2: Energy and water supply
1
3: Manufacturing industries
2-4
Humpherys G. 1968, Goldthorpe J.H.
4: Construction
5
5: Distribution, hotels/catering; repairs
6
& Hope K. 1974, Leete R. & Fox J.
6: Transport/communication, banking, finance 7-8
7: Public administration and defence
91
1977, Boston G. 1980, Beacham R.
8: Other service industries
92-98
1984, Congdon P. 1989).
Figure 12: Working Definitions
Chapter 4: Honeycomb Structure
79
The colours do not mix. The blue (professional) lace-work threads its
way around the city suburbs, strongest in the South63. The red
(supervised) masses mark out the centres of major settlements, while
the yellow (intermediate) patches show the distribution of relatively
well-paid workers between the two: the coalfields of Wales and the
North, for example (this is before most of the miners lost their jobs).
London is a city split between the most and least rewarded workers,
with little room in between.
Smoothing the picture (see Print CLXXVII) makes it easier to form
some generalizations from these pictures. It can be justified, in this
case, because the information on occupation is only available from a
sample of one tenth of the population. Smoothing evenly over
population space on the cartograms averages people with their nearest
neighbours. But the technique must be used sparingly if it is not to
provide false conclusions. It should also be remembered that it is only
the use of the population cartogram which allows the most poorly-paid
63 [a] The constitution and aggregation of classes is a
contentious issue:
[c] The grouping used here is a similar to one used by
Hamnett:
As BRAVEMAN, 1974, has pointed out, however, ‘The
traditional distinctions between “manual” and “white-collar”
labour, which are so thoughtlessly and widely used in the
literature on this subject, represent echoes of a past situation
which has virtually ceased to have any meaning in the modern
world of work’ (p.325). [Hamnett C. 1986 p.393]
It makes little sense to aggregate such divergent groups and
tendencies together and in the analyses which follow, SEGs
12 and 14 are treated separately from SEGs 8 and 9, on the
grounds that they have more in common with SEGs 1, 2, 3, 4,
5 and 13 than they do with 8 and 9. Similarly, SEG 6 is
analysed together with SEGs 7, 10 and 15 on the grounds of
skill levels, renumeration and intercensal comparability. If this
is not done, any comparison over time, let alone a sensible and
meaningful comparison, is indeed virtually impossible.
[Hamnett C. 1986]
[b] There are many ways in which people can be grouped:
Nevertheless, we have avoided lumping together both
intermediate non-manual workers and skilled manual workers
into a ‘new middle-class’. Although some commentators
claim to have perceived either the ‘embourgeoisement’ of
skilled manual workers through rising incomes, or else the
‘proletarianization’ of the white-collar workers through
increased trade union affiliation and ‘militancy’ in the labour
market, the bulk of evidence indicates that there are
substantial and persistent differences in material rewards,
status and life styles between manual and non-manual workers
(Roberts et al., 1977; Westergaard and Resler, 1975). We
have, nevertheless, avoided the mistake of creating a blanket
non-manual category by distinguishing the professional
worker from the intermediate clerical strata. [Pinch S. &
Williams A. 1983 p.138]
[d] Social group affects many aspects of the quality and
stability of life:
Insurance coverage varies greatly by socio-economic group
(SEG), with 23 per cent of the professional and managerial
SEGs benefitting from it, while only 2 per cent of semi- and
unskilled manual SEGs are covered; in the 45-64 age group,
over 31 per cent of the professional and managerial SEGs are
covered. [Curtis S. & Mohan J. 1989 p.187]
[e] You will not find class structure at the city scale:
In only one of the largest cities (Liverpool) did the proportion
of semi- and unskilled exceed the national average by more
than 4 per cent. If concentrations of the most disadvantaged
have occurred as a result of selective decentralization then it
would appear to exist at a more localized level within cities.
[Goddard J.B. 1983 p.12-13]
Chapter 4: Honeycomb Structure
In some prints in this dissertation the pixel-maps have
been smoothed by several passes of a binomial filter. In
one dimension it can be written as (1/4, 1/2, 1/4) and
dissipates the intensity of a pixel with a value of 1 by the
following intensities after the first five passes:
( 1,
4
1
( , 1,
16 4
1,
2
3,
8
80
third of the population to
appear in the picture (Print
LXXVIII, Figure 13).
1)
4
1, 1 )
4 16
There is more to what
people do than their general
( 1 , 3 , 15 , 5 , 15 , 3 , 1 )
64 32 64 16 64 32 64
activity and occupation.
( 1 , 1 , 7 , 23 , 35 , 23 , 7 , 1 , 1 )
256 32 64 128 128 128 64 32 256
What are they doing it for?
( 1 , 5 , 45 , 5 , 95 , 29 , 95 , 5 , 45 , 5 , 1 ) They are doing it to grow
1024 512 1024 128 512 128 512 128 1024 512 1024
things, make things, sell
The two dimensional version of this filter is given by the
following matrix (after Tobler W.R., 1969):
things and think about
1 , 1, 1
16 8 16
things. They are doing it for
1, 1 , 1
8 4 8
industry. People not only
1 , 1, 1
16 8 16
gain employment because of
After approximately ten passes this filter is equivalent to
who they are and what they
the effect of a normal kernel with variance n/4 (where n
is the number of passes). This is one of the simplest and
can do (Print LXXIX), but
most elegant forms of spatial smoothing. It is also,
interestingly, reversible (although this is only practical
also because of the area of
after one or two passes). Its inverse may presumably
also be used to sharpen an image.
the economy they can do it
in. Like everything else,
Figure 13: Two-dimensional
Smoothing
industry is geographically
distributed, that distribution being important to the fundamental
geology of the social landscape (see Appendix E).
{ }
Industry can be divided in many ways, for example into primary,
secondary and tertiary, or into public and private service sectors and
the remainder. Its distributions can then be painted (see Print XXIII).
These are now the distributions of where people work, rather than
where they live, and this is an important point I address later in
Chapter Six. Instead of mixing colours, a geology type classification
has been adopted here, showing which industry has a majority of the
workforce in each area. This has the advantage of further possible
subdivision into dozens of industrial classifications, using subtle
shades of the basic hues, while also showing how other forms of
Chapter 4: Honeycomb Structure
81
colouring can be gainfully employed. The picture produced shows the
clear divisions between the sectors and the strong geographical
patterns, which underly many of the images already presented, and
those still to be seen.
Finally, the kind of work people do affects the sort of home they will
have. Price can indicate the quality of a house, as well as, perhaps, the
inflated and depressed states of local markets. It must be remembered
that examining housing that is for sale only illustrates the distribution
of privately owned provision of homes. Average housing price has
been estimated and plotted for wards from a sample of building
societies’ sales (Print LXXX). The geographic patterns of yearly
inflation and local housing sector structures are investigated in
Chapters Five and Eight. For now, the close correspondence, and
important differences, between this picture and the others are all
presented for consideration64.
4.6 How People Vote
We have seen something of who people in Britain today are, where
they come from, what they do, and for which industries and what
rewards it is done. The next theme in this thesis is concerned with
what they do about it — how they vote. The British state regularly
asks its inhabitants for their opinion on its government, through
elections of candidates representing political parties standing for
particular issues. As the choice is usually only between two or three
regular parties this expression is extremely limited.
64 [a] If we cannot decide which aspect is most important,
why not look at several?:
[b] The distribution of housing is intricately connected to
many of the other patterns shown here:
Social class in the sense of status of individuals in the labour
market, may today be as well reflected by position in the
housing market as by necessarily imprecise occupational
labels. [Buck N., Gordon I., Young K., Ermish J. & Mills L.
1986 p.101]
The results of this study strongly support the argument
(Cheshire, 1979) that inner-city unemployment is not so much
a problem of the performance of the city labour market as a
whole, but a feature of the other sifting mechanisms in
society, mainly the housing market, that concentrate people
who are at a competitive disadvantage in society into
relatively restricted areas. [Frost M. & Spence N. 1981 p.100]
Chapter 4: Honeycomb Structure
82
Geographically such a system produces areas which either support the
party, and generally the order of the day, or the main opposition, or a
third major alternative65. During the 1980s these were the
Conservative, Labour and Liberal parties, which adopted the colours
blue, red and yellow to represent themselves. In the early 1980s the
Liberal party allied with a newly created Social Democratic party
which survived for a decade. Now mostly merged, they are together
called the Liberal Democrats. The term Liberal is used wherever
possible in this work to avoid confusion. Other parties, such as the
Nationalists, Unionists and Greens show interesting, but not
particularly influential, geographical distributions.
Study of the human geography of voting is thus well suited to our
means of visualization (Prints LXXXI & LXXXII). Cartograms give
people the equal representation their votes are worth, and the
graduated three colour scheme encompasses most eventualities.
65 [a] An atlas of British election results was recently
criticised as:
The publication is entirely in black and white which is a little
disappointing in view of the colour association with the major
political parties. All the thematic maps use the area shading
technique. This is a conventional technique for such maps but
has the weakness that it places greatest emphasis on areas of
sparsest population. The effect is doubly unfortunate in that
political tendencies often relate closely to population density.
Thus supporters of the Labour party might feel aggrieved by
the visual diminution of their achievements. A demographic
rather than a topographic base would overcome this problem
but would almost certainly confuse the general public.
Nevertheless simple bar graphs could have been used to
complement the maps and avoid any mis-interpretation.
[Beard R. 1989 p.172]
[b] There are two basic electoral distributions to consider,
who wins, and how people vote:
Finally it should be noted that what polls attempt to measure is
the distribution of party support among the electorate.
Extrapolating from this to the distribution of seats in the
House of Commons is a tricky business which is becoming
trickier. In February 1974, for example, the party which won
most votes (the Conservatives) did not win most seats. In
1987 ITN seriously underestimated the likely Conservative
majority in the House of Commons despite the fact that its
exit poll, conducted by Harris, got the Conservative lead over
Labour in terms of vote share almost exactly right. [Denver D.
1989 pp.106-107]
[c] Political bias can be in either direction:
The January 1910 election illustrates this situation very
clearly. The Irish Nationalists won 82 seats, all but one being
located in Ireland. This extreme ‘peripheral’ concentration is
reflected in the U-shaped voter proportion distribution (Figure
4(a)) with its very large variance. Thus with 1.9% of the vote
the Irish Nationalists were able to secure 12.2% of the seats to
enjoy the positive bias of 10.3%. The more recent experience
of the Liberals has been a sharp contrast to this situation.
[Gudgin G. & Taylor P.J. 1973 p.18]
[d] The local distribution of class is almost identical to that
of local voting:
Since 1945, occupational class has been widely seen as the
main social basis underlying electoral politics in Britain. A
pattern of ‘class alignment’ was clearly apparent in the 1950s
and 1960s. [Dunleavy P. 1983 p.32]
[e] The importance of class to voting is widely
acknowledged:
The dominant alignment, or cleavage, in British electoral
politics is class — employed loosely as a shorthand term for
position in the division of labour. [Johnston R.J. 1986 p.574]
[f] The distribution of occupational class is the best
predictor for the political composition of an area:
But at the level of explaining why particular areas or
constituencies vote the way they do, knowing the mix of
occupational classes in the local area continues to be as
valuable as ever in explaining or predicting election results.
[Dunleavy P. 1983 pp.37-38]
Chapter 4: Honeycomb Structure
83
Although only the winning candidate holds a seat in parliament, the
degree of support and nature of the opposition are also relevant both
now and to indicate possible future trends (Prints LXXXIII & LXXXIV,
Hollingsworth T.H. 1964, 1966, Roberts M.C. & Rumage K.W. 1965, Prescott J.R.V. 1969,
Johnston R.J. 1979, Madgwick P.J. & Balsom D. 1980, Dunleavy P. 1983, King A. 1986b,
Swaddle K. & Heath A. 1989, Upton G.J.G. 1991b).
The votes in national (general) elections are only reported for very
large areas containing sixty or seventy thousand electors. Whilst being
pertinent events, and the only complete record of the people’s (who
vote — Print LXXXV) actual wishes for government, the fine detail of
local opinion (at which we know everything else about our social
landscape) is lost.
Local elections follow a complicated system of timing and are not all
simple, one candidate, decisions. They do, however, give us
information, at the relatively fine level of about three thousand
electors, nationally. County council and Scottish regional elections,
however, are based upon a different, very poorly defined, geography.
Therefore we must rely upon the results of District elections to see the
spatial mosaic (Rowley G. 1965, Clark D.M. 1977, Eagles M. & Erfle S. 1989, Rallings
C. & Thrasher M. 1988, 1989, 1990a, 1990b, Bowler S. 1991).
The national picture of voting emphasises the divisions seen earlier in
the social landscape66. In general people in areas of high
unemployment, recent immigration and older industries vote Labour,
66 [a] Some claim that local elections are too different to
be considered along with the national elections:
[c] Constituencies may well represent too high a level of
aggregation:
Because of its significant effects there is little need to justify
the analysis of constituency level voting, but the same cannot
be said for the study of other levels of voting. [Miller W.L.,
Raab G. & Britto K. 1974 p.391]
Nevertheless, many remain sceptical that there is indeed a
geographical component to the vote or, indeed, to any other
kind of behaviour. Some critics of these approaches ground
their attack in variants of the ‘ecological facility’. McAllister,
in particular, doubts that the processes at work can operate at
the levels of aggregation that are sometimes chosen (see the
Johnston and McAllister debates in this journal). [Bowler S.
1991 p.92]
[b] But others state:
‘You can no more take politics out of government than you
can keep sex out of procreation.’ [Gyford J., Leach S. and
Game C. 1989 p.1]
Chapter 4: Honeycomb Structure
84
whilst the rest of the country is dominated by the Conservative party,
closely followed by the Liberals (Print LXXXVI). The geography of
political party support accentuates the differences between people in
areas, for it is through these parties that people are allowed to register
their support or condemnation of the social system within which they
are placed.
4.7 The Social Landscape
The social landscape of Britain has now been built up and presented
through a dozen colour images. This is the landscape which people
know, the landscape which is made up of, and determines, many
aspects of their lives. It is the landscape of neighbourhoods,
communities, blocks, streets, groups, ghettoes, villages, suburbs,
housing estates, life chances and constraints. It is the landscape of age,
work, class, immigration and race. It is the landscape of social
existence, political power and economic opportunity — the human
geography of Britain.
There is, however, much more that could be studied using these
methods, adding to the montage. The geography of health — people’s
life expectancies, disease and disability; the geography of welfare —
the payment of benefits, the provision of services; the geography of
privilege — the distribution of ownership of property and shares in
[d] Many writers have suggested that constituencies are so
large that they blend together the diverse patterns which
actually exist:
To the extent that there are processes of political persuasion at
work these are much more likely to be at a very local scale
rather than that of constituency [Agnew J.A. 1987 p.99]
[f] Local elections provide us with a more precise picture:
Third, it is essential that any analysis should be conducted at
the smallest electoral level possible. There is no excuse for
using constituency figures if returns are also shown for
subdivisions of electorates, or for individual polling booths;
the figures related to smaller areas will make it possible to
draw the boundaries of voting regions with much greater
precision. [Prescott J.R.V. 1969 p.381]
[e] Constituencies cannot constitute communities:
[g] The whole is formed from its parts:
Despite the fact that both cohesion measures consistently
appear to make a statistically significant contribution to the
explanation of turnout levels, it must be acknowledged that
the magnitude of their impact on turnout is disappointingly
small. This may be because constituencies are too large and
internally differentiated adequately to represent communities;
consequently,
communal
influences
are
probably
underestimated using constituency level data. [Eagles M. &
Erfle S. 1989 p.125]
Taken together it is hoped that this book demonstrates the
truth in the words that for political behaviour, “It is the local
reality that determines the total picture, and not the reverse”
(Granata 1980: 512). [Agnew J.A. 1987 p.6]
[h] But these small localities have been largely ignored:
Much work remains to be done. Local elections have received
relatively little attention.... [Busteed M.A. 1975 p.54]
Chapter 4: Honeycomb Structure
85
industry; the geography of income and wealth.
The fundamental mountains and valleys of the social landscape of
Britain would not be gravely altered by the addition of such
material67. Here and there a new aspect would come to light, a
different impression form. Overall, though, as with the incremental
addition of the information already gathered, further insight is far
more likely to reinforce our impression of a divided land. We know
where most ill-health would be (the old and poor), where the social
security benefits would be paid, where the rich would be found and
the owners of industry concentrated. As you draw more and more of
these pictures, you begin to recognise the same, familiar features over
and over again in the social landscape68. So much is so strongly inter-
67 [a] The image we gain of the social landscape depends
far more on how we draw it than on what it contains:
Therefore, rather than think in terms of a simple division
between South and periphery, it may be necessary to think of
a threefold division into periphery, South and Midlands, and
the South East excepting London. Even this division may be
too simplistic. [Pinch S. & Williams A. 1983 p.155]
[b] Some of the patterns found here were previously
gleaned from more conventional maps:
In terms of geographical patterns there is in 1981 a clear
distinction between the places at the two extremes (Figure
7.1A). Most notable is the ring of most privileged LLMAs
around London, extending to the South Coast and forming a
virtually complete arc on the other flank; the only exception
being along both sides of the Thames estuary. There are also
significant clusters of better-off LLMAs further westwards
along the South Coast and in southern parts of the West
Midlands. The prosperity of the South Coast can be gauged in
terms of the fact that south of a line between the Severn
Estuary and Lincolnshire there are only three representatives
of the lowest quintile on this indicator, namely Corby,
Spalding and Wisbech, and, of Britain’s bottom 112 places,
the South accounts for only 11, all but one of which are
located on the margins of the region in East Anglia and the
East Midlands (Figure 7.1A). [Champion A.G., Green A.E.,
Owen D.W., Ellin D.J. & Coombes M.G. 1987 p.91-93]
[c] But the impression presented by conventional mapping
often needs to be corrected:
Conclusion: there are an awful lot of poor areas in the South
East (especially inner London); and there are a lot of very
affluent suburbs around the North’s big industrial towns.
Which shouldn’t come as a surprise — but may act as a
corrective to the blinkered south-easterner’s view of a vast
industrial wasteland north of Oxford. [King A. 1986 p.18]
[d] Diversity and variation are everywhere, yet an order
prevails:
In short, a picture of local variation is revealed, here in terms
of the advantages enjoyed by some successful northern areas,
but more generally reflected in the wide diversity that exists
across Britain in many criteria (eg Fothergill & Vincent,
1985). [Goddard J.B. & Coombes M.G. 1987 p.13]
[e] The concentration of deprivation is greatest in the
conurbations of population, but in the more populated South
of Britain; on a conventional map, this can be overlooked:
Rather, it is claimed that the concept of a North-South divide
in Britain is valid despite the existence of local variations
because it is demonstrably the case that the concentration of
relative socioeconomic deprivation and disadvantage is
significantly greater in the ‘North’ than in the ‘South’ (Martin,
1987b, p.573). [Green A. 1988 p.194]
[f] Which divisions matter most, the sharp lines in cities, or
the shallow slopes between regions?
And as is well known, the degree of spatial inequality in the
socio-economy is scale-dependent; measured differences tend
to increase as the geographical coverage of the areal divisions
employed decreases. The debate is not just over the existence
or significance of local disparities, which can be found
everywhere: the issue is also that these local disparities map
out and form part of a broader ‘north-south’ geography of
socio-economic inequality, and that this regional divide has
become an increasingly prominent feature of British society.
[Martin R. 1989 p.22]
Chapter 4: Honeycomb Structure
86
related that it becomes the exceptions — which point to how things
can be different — which are worth looking for (White D. 1985, Goddard
J.B. & Coombes M.G. 1987, SEEDS 1987, Town and Country Planning Association 1987,
Wilsher P. & Cassidy J. 1987, Green A.E. 1988, Owens J.R. & Wade L.L. 1988, Lewis J. &
Townsend A. 1989b, Martin R. 1989).
This part of the thesis now moves on to ask: now that we can see the
landscape, how is it changing? Then, what movement keeps it alive
and what alters it? These are questions which increasingly stretch the
limits of the visualization methodology. In the final part of the work
the problem of envisioning the history and geography simultaneously
will be addressed, leading us to the integration of all this information
to create a consolidated image of British social history and geography
at the end of the twentieth century. Time and space intermingle. The
pictures I have presented up to now show the situation at a single
point in time, but those of immigration and birthplace also tell of a
different past.
The three themes of migration, work and voting will be integrated as
the thesis is developed. Migration — where people come from and
where they are going; work, what people do, when they can do it, and
voting — what people want, and when, how and where they get it.
Human geography must integrate the study of the populations which it
first, so finely, spatially separates.
68 [a] The pattern is most consistent when based upon
smal scale populations:
The Victorians were more concerned with patterns found at a
smaller spatial scale, and this is also reviving today: as we will
see, despite the interest in the north-south divide, some writers
see spatial differentiation taking place on a much smaller
spatial scale between localities. [Savage M. 1989 p.248]
[b] A picture of “London and the rest” is clearly seen:
In considering the changing social structure of the principal
cities of the nation the basic distinction is between London
and the rest. Only in the London core does the proportion of
the economically active population in managerial and
professional occupations (17.7%) and in the intermediate nonmanual occupations (22.7%) exceed the national average for
this zone. At the other extreme only in the capital does the
proportion in the core of unskilled manual occupations fall
below the national average. [Spence N., Gillespie A., Goddard
J., Kennett S., Pinch S. & Williams A. 1982 p.275]
[c] But it is the divisions within regions which matter to
people:
This, then, is the South-South divide. It is a divide which
appears in employment opportunities, in wage packets, in each
job’s content and potentialities. It reappears in the car park
and the bus queue, in the green of the garden and the size of
the room. Each part of the divide has its own daily timetable
and its own life cycle. [SEEDS 1987 p.10]
87
Chapter 5: Transforming the Mosaic
Yet within any town as in the region as a whole there is a pattern. The poor
housing, schools and levels of unemployment will tend to be concentrated
in certain districts — as they are concentrated in inner city areas of the large
conurbations of this country. At the level of the region, too, there is a
pattern, increasingly clear and changing.
[SEEDS 1987 p.6]
5.1 Still Images of Change
Counts, measures, votes and all the other figures we use to build a
picture of our social landscape are collected regularly because it is
recognised that the picture changes. People’s positions and aspirations
alter. Compared to the static picture, much less research has been
addressed to looking at change. This may be partly the result of the
change being generally slow, but much is due to the difficulties of
displaying change69. These difficulties range from simple problems of
gathering the information (temporal discontinuities), to the technical
difficulties of processing it (what type of change is to be seen), to
finally, the imaginative hurdles that have to be crossed in portraying it
(creating still images of change).
69 [a] It can be claimed that in abstract terms:
Our maps are in one sense diagrams of geographic systems
and their evolution. Many of them are — or were —
cartographically communicated theories about global or
regional geographic systems of resources and settlement.
Many time series of maps are in one sense statements of
theories, in cartographic language, about geographic
development processes, about the functioning and the past and
future evolution of some global or regional geographic
system. Interpretations of the map patterns involve logical
interpolation or extrapolation from mapped observations, in
both space and time. Distinctively geographic models are also
cartographic
generalizations.
As
four-dimensional
descriptions of the geographic evolution of resource and
settlement systems, time series of maps are a fundamental
element of geographical explanation. [Borchert J.R. 1987
p.388]
[b] But there is a paradox inherent in trying to show
change between censuses:
Change in the context of the census occurs from one census to
another through the necessity to illustrate the conditions
present at the time of the census; therefore there is a distinct
conflict between maintaining consistency from one census to
another and the attempt to mirror relevant current conditions.
The paradox is that the greater the real change which occurs
between the censuses, then the greater the change that is
required in terms of census questions, variable definitions,
classifications, and geographical bases in order to reflect the
changed conditions and hence the greater the difficulty in
comparing the censuses in order to measure such change.
[McKee C. 1989 p.3]
Chapter 5: Transforming the Mosaic
88
Change can be looked at in different ways; absolute change (of so
many people), relative change (of a certain percent), or relative
expected deviation (higher or lower than the average change). The
first two measures give an indication of what is happening to the
country as a whole, the third removes that factor and allows
concentration on variations from place to place (Rhind D., Evans I.S. &
Dewdney J.C. 1977, Norris P. & Mounsey H.M. 1983, Brusegard D. & Menger G. 1989,
McKee C. 1989c, Savage M. 1989, O.P.C.S. 1991).
Change is important. Change shows how the landscape came to be,
where it is going, and how much it can alter. It is all too easy to think
that so strong a spatial structure as we know exists today will prevail
tomorrow. Change can show the spread of division, the concentration
of inequality and the instability of a situation.
5.2 Forming the Structure
In past research, geography often stopped at the first hurdle when
gathering information about change; coping with temporal
discontinuities. Temporal discontinuities occur when units of
population have their spatial boundaries altered. Temporal
discontinuity is continuously re-occurring, and is itself one aspect of
change70. As people move, so do the collating boundaries move
70 [a] Colour has been used, in this dissertation to show
the time dimension in an illustration of population change
between 1961 and 1991 (see Print CLV):
Perhaps the greatest potential is the time dimension. Maps are
static views, and require corresponding approaches to data
collection and map production. Electronic communication
allows instantaneous distribution from one, continuously
updated authority. The implications of this technical change
extend not only to the way the data are viewed, but also to the
way they are collected, and the infrastructure which has grown
up around data collection. The decennial census, for example,
was a solution to the problems of data collection and
dissemination of the mid-19th century. Yet we have only the
vaguest notions of how to exploit the new technology in this
area; our perceptual systems are so geared to conventional
display that we find it difficult, for example, to conceive of
color being used to show the time dimension, or of how to
structure and store time-dependent data in an efficient manner.
[Goodchild M.F. 1988 p.318]
[b] Many minor nuances must be included when
calculating the change between censuses:
Perhaps most fundamentally, the 1981 Census was taken on
the night of 5 April (before Easter and out of term for higher
education institutions) and the 1971 Census was taken on 25
April (after Easter and in term for higher educational
institutions). In towns where the number of holiday-makers
and students cause seasonal fluctuations in the size of the
population, this three-week difference is likely to have some
impact on the results obtained. [Norris P. & Mounsey H.M.
1983 p.276]
[c] Obtaining change information for high resolution
mapping is difficult:
Finally, OPCS have not treated dealing with change at all
seriously, at least compared to their treatment of the standard
‘snapshot’ census data. Additionally (and perhaps
understandably in view of the problems of change statistics),
little importance has been attached to local (less than district)
level analyses. [McKee C.H. 1989 p.432]
Chapter 5: Transforming the Mosaic
89
around them, eternally attempting to encompass them adequately . We
need to encompass these changes within our pictures (see
Appendix B).
Practically every person in Britain is counted in the census every ten
years. The simplest single number to be gathered from this is the total
change in population, an increase of however many thousand between
1971 and 1981. How is this simple loss or gain of people distributed
over our landscape? During this period Britain undertook its greatest
ever redistribution of administrative boundaries — everything altered.
Very few figures collected before the mid 1970s could be directly
compared with those that came after (Massey D.S. & Stephan G.E. 1977, Rees
P.H. 1977, Craig J. 1988, McKee C. 1989a, 1989b, 1989d).
Geographers often addressed the problem of change with the crude
solution of aggregation71. The method is to find a set of large areas
either side of the time period whose summed figures can be directly
compared. This solution causes a great deal of information to be lost;
local patterns can no longer be seen, large areas arbitrarily appear
uniformly good or bad when the more truthful picture is very different
(see Print LXVII).
To see national patterns or regional or city-sized processes it is better
not to use national, regional or city-sized spatial units. Rather, show
71 [a] A policy of aggregating areas can prevent a proper
study of geographical change:
The largest tract in the region, in terms of 1971 and 1981 EDs,
occurred in the district of Bracknell in Berkshire, with 98
1981 EDs and 60 1971 EDs combining to form this
comparable ‘small’ area (as defined by OPCS). This tract is
therefore a good example of an area in which great change is
taking place, but which — as a consequence — permits the
least local study in the region of this change, due to the large
size of the tract. [McKee C. 1989 p.4]
[b] Constituency boundaries are regularly redrawn:
The very poor correspondence between constituency and local
government boundaries has arisen partly because the present
constituency boundaries, which were first used in the
February 1974 general election, were based on the ‘old’ set of
local government areas. Normally constituency boundaries
would coincide with counties and standard regions. However
the current situation will continue until the next boundary
revision is implemented. [Population Statistics Division,
OPCS 1979 p.19]
[c] The approach adopted in this work could cope with the
fine scale enumeration district study of census change over
two decades:
Presumably, without comparable small areas and their related
and comparable statistics, measuring local change between the
1981 and 1991 Censuses will necessitate the aggregation of
EDs into comparable small areas between 1981 and 1991 by
the user, where datasets permit. Comparison of change
between the 1971, 1981 and 1991 Censuses to cover the 2
decades of change will, therefore, only be possible on a
‘supertract’ basis, by aggregating tracts comparable between
1971 and 1981 to areas comparable with the 1991 Census.
This will reduce the amount of local detail considerably and,
in areas of great change, such as the largest 1971/1981 tract in
the region (made up of 98 1981 EDs) will certainly not
facilitate a better understanding of change. [McKee C. 1989
p.19]
Chapter 5: Transforming the Mosaic
90
The 1971and 1981 census geographies were linked at
the enumeration district (ED) level. The majority of ED
boundaries had not changed or were nearly identical, but
in some places substantial alterations had occurred, for
instance: where a new town had been built or old estate
pulled down. The use of the "census tracts" designed by
OPCS has been found to be far from adequate by
McKee (1989). An alternative, far more flexible solution
was devised. Only enumeration district centroids were
known for each set of roughly 130,000 points. Two 2D
tree data structures were built and the closest 1981
district to each 1971 found, and visa versa. Thus every
ED in each set was connected to at least one in the other
- but could be connected to any number, if required.
Counts could then be compared.
9:9
the eye the finely detailed
picture, and let the mind
decide how much pattern
does or does not exist. Only
then can the decision be
made whether to smooth the
picture further . There are
also many means other than
indiscriminate geographical
amalgamation which can be
used to generalize an
image72.
1:1
1:1
1:1
1:1
1:3
How, though, do we create
1981 ED
2:1
these fine images of local
change from two sources
Figure 14: Linking the Censuses
based upon small, but
differing, areas? What I do is to recognise that any change in
boundaries has only a very local effect (Figure 14). People are moved
from one side of the line to the other. There is no need to abolish the
line, simply to realize that a few people have been moved. A detailed
image, where nothing but the boundaries has really changed, will
simply appear a constant, slightly speckled shade. The eye interprets
the fine dithering that will have been created by misplacement as a
colour, not a pattern. The problem has been reduced away.
1971 ED
72 [a] The desire to aggregate as the time scale is extended
must also be resisted:
Quite clearly the analysis of social change must incorporate a
time dimension and quite often involves a complex interplay
between several processes and between different levels of
aggregation. Techniques to permit such complicated analysis
are increasingly being developed and demographic research
has become much richer through intelligent use of these
approaches. [Hobcraft J. & Joshi H. 1989 p.11]
[b] Change can be very different at different scales:
It will be apparent ... that the relative significance of the
different types of population change varies with the scale of
analysis. Although decelerating increase is the most frequent
type at regional, county and district level, accelerating
decrease is just as common at county and district level but
also occurs in three regions. In addition, the diversity of types
increases with the reduction in scale of analysis, so that the
pattern at district level is very much more complicated than at
county and regional level; in particular the percentage
frequency of types of population increase is much greater at
district level than regional level. [Mounsey H. & Clarke J.I.
1981 p.6-7]
[c] Textual description of change can also be very elusive
as a result of generalization:
The inadequately described have moved almost exclusively
into the council sector. [Hamnett C. 1987 p.548]
Chapter 5: Transforming the Mosaic
91
The following tables show how often a one to one link is
achieved, and how it is unnecessary to combine up to 98
EDs in places (as is done with census tracts), when this
approach is taken.
It is not always as simple as
this. Between 1971 and
1981 we were fortunate
Links Between 1971 and 1981 Enumeration Districts
that the total population,
1971 EDs to each in 1981
1981 EDs to each in 1971
and its internal distribution,
0 0
0 216
1 105,946
1 100,436 changed little. Advantage
2 20,550
2 20,841
3 2,347
3 3,085
can be taken of this fact.
4 287
4 582
5 49
5 163
For
instance,
the
6 20
6 63
underlying
cartogram
7 4
7 34
8 5
8 18
which was used to portray
9 0
9 12
10 1
10 9
it did not perceptibly
11 2
11- 18 17
change its shape. Over long
Total 1981 EDs 129211
Total 1971 EDs 125476
periods,
which
are
Note: 216 1971 enumeration districts had identical grid
examined later in this
references of 600km East by 400km North and thus,
lying in the the North Sea, were not linked to the 1981
chapter, the underlying
set. These were probably used to record the population
aboard ship.
base map changes with
Figure 15: How Closely Connected?
time.
Secondly,
most
spatial units, of any size,
will usually correspond to just one or two predecessors. Occasionally
the relationships will be more complicated (Figure 15).
5.3 Structure Transformed
The most basic changes of population have been simply painted by
making each block white where population fell, and black where it
increased. The white holes of the major conurbations can be easily
distinguished, as can the black rings of built up areas around them.
Chapter 5: Transforming the Mosaic
92
Importantly, the truth of this generalization can be ascertained from
just how clearly this pattern stands out73.
There are no woodlands and fields on the population cartogram, just
the people-lands of inner cities, suburbs, small towns and villages —
all in proportion to their populations. Some of these have been
growing and some declining. Everywhere there has been great
variation, from street to street, suburb to suburb. As I progressively
smooth the image, averaging each cell of one hundred people with
eight hundred of their neighbours, then two thousand, five thousand,
eight thousand... we see a more and more generalized image of the
process of population redistribution. Information that is perhaps more
clear, but less real.
The changing distributions of the sexes and ages can similarly be
depicted (see Print VII). For the distribution of the sexes to alter,
people must be born, die or move. Age obviously changes
continuously with time, as well as irregularly over space (as people
move). These two attributes are, however, interrelated, for as people
age, men die earlier, and so changes in the proportions of the elderly
are reflected by changes in the geography of the sexes. Similarly,
more children will be born and brought up where there are more
women. Again one influences the other. We could struggle to see
these influences on three separate maps, one of the elderly, one of
gender and one of children. How much better to show these
73 [a] It should be possible to see the regional pattern
through the local picture:
With the exception of the South East, in all regions containing
a metropolitan county the balance of migration both in 1971
and in 1981 was outward; and in all the remaining regions it
was inward. [Brant J. 1984 p.28]
[c] Ward boundaries can be redrawn as often as every three
years:
The most extreme examples are a new ward in the Isle of
Dogs with a zero population in 1971 but 5,400 in 1981; and a
ward in Bracknell district with a population of 3 in 1971 but
8,700 in 1981. [Craig J. 1988 p.9]
[d] Changing the scale changes the picture:
[b] Again, contradictions are found in Inner London’s
geography:
In central London in particular, however, things are not quite
so clear-cut as groups of tracts with high percentage
population increases lie side-by-side with groups of tracts
which recorded high population decreases. [McKee C.H. 1989
p.198]
The City of London was the only London borough to increase
in population during the 1970s yet it is precisely this district in
which a number of tracts experienced some of the greatest
decreases in population in the region during this period.
[McKee C.H. 1989 p.201]
Chapter 5: Transforming the Mosaic
Observed change (O) can be measured in many ways
between two times (T) and many places (i). For instance
T -T
Oi = i2 i1
Ti1
T2 - T1
T +T
( 1 2)
2
or
Expected change can be calculated by:
n
E = 1n
i=1
Then deviation
from expected is
derived as:
Ti2 - Ti1
Ti1
93
interrelated changes in a
single image by three colour
shading. Now, though, rates
of change rather than
proportions of people are
under scrutiny. What kind of
change should we show?
Change is primarily a
product of the time over
which it is measured. There
Given six categories of housing and the national average
prices (P) and proportions (W) of these, it is possible to
are no coastlines in time to
estimate from the local distribution of prices (p), the
delimit a period neatly, as
average housing price (h), as either an arithmetic or
geometric mean:
we can a space. Using the
state census, we can claim to
6
6
Wi p
Wi p
i
1
) have no choice but to use
ln (
h = P
P
P
Pi
i
decennial intervals with no
h = e i=1
i 1
intervening
information.
How to cope with more
Figure 16: Measuring the Changes
fruitful situations is the
subject of much of the rest of this thesis. For now, we only have two
measures in time, and thus a single change to portray (Figure 16).
E - Oi
Di =
E
Aspects of the changes discussed above can themselves be examined
more closely. Change in the spatial distribution of children, of
different age groups, is examined. This image, which presents such a
jumbled picture, tells us that there has been little uniform progression
in these five year bands over time. The confusion is caused by families
moving; very few remain in the same block for ten years. Unravelling
the effects of migration is difficult. The next chapter examines it in
detail. For now, note that even pictures which show no structure are
showing something. Until you look at a picture you can only guess
what you may or may not be able to see.
Chapter 5: Transforming the Mosaic
94
The last chapter introduced the major theme of migration to this thesis
by looking at where people in Britain were born. Here we look at how
those pictures are changing (Print LXXXVII). This could be done by
seeing where those migrants have moved to, or from, but here we
show how the proportion of lifetime migrants has altered in areas over
the ten years between censuses. High levels of colour in the image
indicates that either the proportion of lifetime migrants in that area has
risen, or has not declined as much as elsewhere. Of those born in
Britain, what is most striking is the return of the Welsh to the Valleys
(or their unwillingness to leave them). Then there is the infusion of
English born into the rest of Wales and highland Scotland74. The
decline of all three national birthplace groups generally shows areas
where immigrants have been moving in.
The picture of change in the proportions of those born in (all) Ireland,
Asia and Africa depicts some interesting features (Print LXXXVIII).
The rings of movement of lifetime international migrants out of the
centre of London are distinctive75. This group of people had a major
impact upon the changing social landscape of Britain in the 1970s as
74 [a] The following is the first of three quotations which
show how many times the same phenomenon can be
independently recognised if it is clear enough:
The lowest mobility rate within a region was found in Wales
(65); all other such rates were between 70 and 80. [Brant J.
1984 p.24]
[b] It is interesting to see how the numbers alter, but the
picture remains the same:
The five districts with the lowest mobility rates per 1,000 total
population are all in South Wales and include Afan (50) and
Rhondda (56). Since many districts in this area also had
particularly low rates in 1971 there seem to be local influences
which reduce mobility as compared to other parts of the
country. There may be a greater reluctance to move,
associated with a strong community and family spirit, as well
as problems in finding local employment and in obtaining
accommodation. [Devis T. 1983 p.17-18]
[c] Slight spatial inconsistencies can still be found,
however:
Rees (1978) has depicted the reluctance of population in one
such community, the Upper Afan Valley in South Wales, to
resort to labour migration. Interestingly, the valley’s
neighbouring MELA, Rhondda, has the nation’s lowest per
capita migration inflows and outflows. [Kennett S.R. 1983
p.223]
75 [a] The calculation of some census change variables is
not totally reliable:
OPCS (1984) considered the change in the classifications of
country of birth (factor H, appendix 3) to have “relatively
little effect” on the measurement of change from 1971 to
1981. Even if this is so (and there are no means to prove it), a
number of other factors — as appendix 4 indicates — affect
the statistical comparability of these figures, one of which was
a change in the editing procedures (factor I, appendix 3) from
1971 to 1981. OPCS (1984) considered that, in general, the
effects of these would be lower than those caused by
differences in the population base. [McKee C. 1989 p.11]
[b] Changes in migration can cause changes in the
geography of voting:
For a long time this immigration was not regarded as being of
any political significance, but as the 1950s passed the level of
immigration and public unease increased. It was reflected at
the national level by some members of the Conservative Party,
and at the regional and local level by some Conservative Party
associations and individual candidates in local elections. The
most notable impact, however, was felt in the 1964 general
election in Birmingham and the West Midlands. [Busteed
M.A. 1975 p.49]
Chapter 5: Transforming the Mosaic
95
their moving influenced the basic age and sex distributions. They can
also be seen to be moving to escape or seize the changes of
opportunity that occurred (Craig J. 1980a, 1985b, 1987c, Mounsey H. & Clarke J.I.
1981b, Mounsey H.M. 1982a, 1982b, Peach C. 1982, 1984, Lawton R. 1986, Champion A.G.
& Congdon P. 1988).
5.4 Variable Employment
One of the most variable attributes of our social landscape is
employment, or the lack of it. The obvious extension of the above
methods has been performed to illustrate the transformation of
people’s lives from 1971 to 1981 which depended on where they live
(Prints LXXXIX). The West Midlands increased its share of the
unemployed and inactive by 1981; more people were working around
London, and so on. These images show strong patterns, but just as
indistinct images are not worthless, simple ones are not
necessarily true.
Employment is a feature of our landscape that changes seasonally, the
vegetation cover if you like. The single change over ten years hides
great swings in the fortunes of places between those dates.
Unemployment has been measured for areas, the size of towns, for
every month since 1978, a complex spacetime series. To show the
changes most simply we can paint a small image for each year,
showing the deviation in each area from expected levels for that place
and time. Such a series shows us how the spacetime pattern of
unemployment deviates from what we would imagine, given a simple
graph of time, and single cartogram of space (Prints XC & XCI).
[c] While in the geography of employment:
The Black population of Britain is locked into an allocative
system that seems bound to produce an increased polarization
of native and immigrant populations. The forces that drew
them into the economy are the same forces that are producing
an increased isolation of the Black population. They came to
fill gaps created by an upward mobility of the White
population in the employment structure and they settled in
gaps left in the urban structure by the outward geographical
mobility of the White population. [Peach C. 1982 p.40]
Chapter 5: Transforming the Mosaic
96
The series of cartograms has areas shaded dark to indicate higher than
expected levels of unemployment, moving towards white for lower
than expected levels. At the end of the 1970s a Celtic fringe of high
unemployment is apparent; by the end of the 1980s a very distinctive
ring of low unemployment has grown around London76. In between
are shown bad times for the people of the South, when unemployment
was high everywhere and thus they too were doing relatively badly.
The shading of these areas is as dependant on the limits of the time
period as it is on the spatial limits of Britain. What is more, only a few
years can be shown on a page (although at least years are a sensible
amalgamation of months for counting unemployment). However, the
beginnings of a picture of space and time is emerging (Frost M.E. &
Spence N.A. 1983, Green A.E. 1983, Pinch S. & Williams A. 1983, Buck N., Gordon I.,
Young K., Ermish J. & Mills L. 1986, Dunn R. 1987b, Balls E. 1991).
The geology of industry changes much more slowly than that of
employment, even though the latter follows changes in the former.
Detailed information on people working in industry in many places
has only been available towards the end of the 1980s and only then for
the years that the census of employment has covered. The change for
76 [a] The simple view of a North/South divide is
challenged by the spatial reality:
The use of unemployment as a convenient indicator of
economic health is not new in this area of work. However,
these studies have been given a new significance by two
recent developments. The first arises from the growing
realization that the simple division of the country into the
“peripheral” slow-growing assisted areas contrasted with the
more bouyant economics of the “core” areas of the South and
the West Midlands is no longer tenable, and is unlikely to
regain its former power in an era of rising national
unemployment and slow or negative economic growth. [Frost
M. & Spence N. 1981 p.7]
sharper decline in inner areas have come about without a
significant upward shift in London’s unemployment. [Buck
N., Gordon I., Young K., Ermish J. & Mills L. 1986 p.180]
[c] The ward level shows us what is affecting individual
people’s lives most precisely:
Analysis of trends over the 1980s points to a continuation of
wide differences between the least and most privileged wards.
Unemployment differentials have widened, even in the most
recent period when the average level has fallen. [Congdon P.
1989 p.489-490]
[d] The doughnut which is clearly and repeatedly seen in
the cartograms has been previously identified:
[b] Employment and unemployment levels are related, but
not directly:
While employment trends in London have thus been dissimilar
to those in other parts of the country, including the rest of
southern England, changes in unemployment have tended to
follow national trends rather closely, if with somewhat smaller
fluctuations. The long-term decline in employment in London
— which goes against the national trend — and the even
Particularly impressive is the “doughnut” of employment
growth located at a radius of 50-140km from central London,
which includes successful New and Expanded Towns such as
Milton Keynes and Basingstoke, and Winchester, a county
town which has gained from the growth of public services and
from being at the centre of a region of rapid economic growth.
[Champion A.G., Green A.E., Owen D.W., Ellin D.J. &
Coombes M.G. 1987 p.63]
Chapter 5: Transforming the Mosaic
97
wards is shown between the 1984 and 1987 censuses, and an
alternative colour scheme is used to depict which industries grew the
most and which showed most decline (see Prints XXIV & XXV). The
distribution of occupations, as might be expected, hardly altered at all
over the ten years to 1981 (Print XCII)
5.5 House Price Inflation
A large sample of house sale information has been collected for the
years 1983 to 1989 inclusive and has been converted for processing at
the ward level. The static picture has been shown before (see Print
LXXX), now inflation at every place in every year is shown. Here,
though, a different shading scale from that used above is adopted. We
wish to compare years as well as areas, so a fixed continuous shading
is employed, light to indicate rapidly increasing prices, dark for
slightly falling ones.
The picture is at first murky, a problem, perhaps, with the somewhat
unreliable figures in the first year (Print XCIII). With so few and such
different houses being sold by particular building societies in
particular wards in particular years, spurious changes can be found.
The mix of houses being sold in a ward is weighted by the national
mix before a housing price can be calculated for the area. One
outlying spot in ten thousand can easily occur. Far more notice should
be taken when a clump is seen, for this is very unlikely to have arisen
at random. Spatial and temporal smoothing could have been employed
here, but it is remarkable how well the structure of local housing can
be seen from the pictures of raw information.
Soon after 1983, a complex pattern begins to emerge of high inflation
in the Home Counties and London, slowly moving out in a rough ring
Chapter 5: Transforming the Mosaic
98
(Prints XCIV, XCV & XCVI). This picture mirrors the changes in
unemployment described above. The increases become greater and
greater, but a dark core begins to form in the centre (Print XCVII).
Suddenly the darkness envelopes and the house price slump of 1989 is
upon us77 (Print XCVIII). Only images could show how this began,
preserving the detail, rather than averaging (Saunders P. 1989, Merrett S. &
Sharp C. 1991, Coombes M.G., Champion A.G. & Monro M. 1991).
Still, when it has all finished, little has really changed. The static
pattern for 1989 looks much like 1983, although the prices have
trebled (Print XCIX). The images should be borne in mind, however,
in connection with other changes, and also in connection with how
they relate to changes about to be shown. Unemployment and inflation
are claimed to be the major preoccupations of those who are about to
vote.
5.6 Reshaping Votes
In considering voting in general elections, I have gone back in time
before the era covered by most of this work to see how the changes of
77 [a] Housing links the human geography of people to the
physical geography of land:
Patterns of housing tenure in the conurbation are therefore a
key element in the social geography of London and provide a
social link between the built environment (the physical
structure of London) and the social environment (the social
structure of London in its spatial context). [Shepherd J.,
Westaway J. and Lee T. 1974 p.32]
[b] There is a debate as to how much polarization and
segregation is due to the structure of housing provision:
By comparison of the standardised indices with the
unstandardised indices ... it may be concluded that most of the
segregation process operates through the housing market. The
deviations from 1.00 (excepting the other group) do not
exceed what one should expect from rounding errors. [Berge
E. 1988 p.977]
[c] Some of the reasons for the collapse in the London
housing market were foreseen:
Bramley and Paice (1987) have calculated that, even assuming
that potential buyers can raise a 95 per cent mortgage on three
times their income, one in three families living in the South
East cannot afford to enter owner-occupation. [Hamnett C.
1989 p.111]
[d] Many people are forced to live where they do — while
others can choose where to live:
A rational choice model is clearly oversimplified: the idea that
couples have a free choice between sectors is — in both
senses of the word — untenable: access to different types of
housing is determined by “constraint” as well as “choice”
(Rex and Moore, 1967). In Duesenberry’s words if
“economics is all about how people make choices, sociology
is all about why they don’t have any choices to make” (1960,
p.233). For many, the idea of a free choice in housing is a sick
joke, especially amongst the unemployed, those in insecure
jobs, and for many in high house-price areas. [Murphy M. J.
1989 p.101]
[e] It is also interesting to see where most social housing is
now required:
A much larger chunk of provision should be allocated to nonmetropolitan areas in the south of England, and to housing
associations and other agencies operating in these areas.
Relatively little additional provision is warranted in the
midlands and the north, areas which in the past have received
substantial provision. [Bramley G. 1991 p.73]
Chapter 5: Transforming the Mosaic
99
today compare with other years (see Appendix C). Ten general
elections have been held since 1955 (by-elections have not been
included, but they have never altered the government in power). What
we are interested in are changes which occur at elections, which
change governments and, as the political landscape always slowly
changes, we want to be in a position to guess where it is going (Cox
K.R. 1968, Gudgin G. & Taylor P.J. 1973, Miller W.L., Raab G. & Britto K. 1974, Busteed
M.A. 1975, Crewe I., Särlvik B. & Alt J. 1977, Burnham W.D. 1978, Taylor P.J. 1982, Urwin
D.W. 1982, Hurst M. 1986).
Images can be produced, showing which parliamentary constituencies
changed hands between each contest (see Prints CXXXVIII &
CXXXIX). These are most important in showing the geography of
political success. Only those seats which changed hands are coloured,
on the outside of their symbols by the former party, inside by the new
holders. Between three parties there are six possible colourings,
(between six different parties there are thirty). Here, the results of
those changes, and something of what lies behind them, can be seen
(Prints C, CI, & CII)
Showing the changing proportion of votes is more problematic. The
British electoral system is dominated by three major parties. It is the
swings between these which are of interest. The swing between three
choices is a two-dimensional object (just as the simple swing between
two choices, a basic change, is one-dimensional). A two-dimensional
change can be shown in various ways. These pictures (see Prints
CXLV & CXLVI) use arrows, the direction of which indicates the
direction of change and the arrow length indicates magnitude of
change. Pointing up was movement towards the Liberal party, left
towards Labour, and right towards the Conservatives. The advantage
of the parties having conventional directions (left, right and centre) as
well as colours (red, blue and yellow), has been fully exploited. The
Chapter 5: Transforming the Mosaic
100
arrows could be coloured by the existing shares of the vote, to show
what political complexion the change was from.
What can be seen from these images of electoral fortune are uniform
swings around the country with distinct variations depending on both
geographical and political position78. The divergence in political
behaviour grew stronger towards the more recent elections, as the
arrows in different parts of the country began to head in different
directions, taking the voting compositions of the seats and the pattern
of victories with them. The dramatically changing fortunes of the
liberal party are clearly shown (Denver D. 1989, Martis K.C. (ed.) 1989, Mishler
W., Hoskin M. & Fitzgerald R. 1989, Denver D. 1990, Pattie C. 1990, Johnston R.J. & Pattie
C.J. 1991, Sanders D. 1991).
It may not have been realised that these images encompass two
geographical redistributions of constituency boundaries, both
increasing the number of seats being contested. These changes are
incorporated in the graphics, the cartograms for which were based on
78 [a] The first election shown in the series analysed here
occurred in 1955:
It may be hard to believe nowadays, but during the 1955
general election campaign television news broadcasts made no
references whatsoever to the election because the broadcasting
authorities feared they would be in breach of laws regulating
the conducts of elections. [Denver D. 1989 p.50]
[b] A great change can be seen in the image of February,
1974:
The February 1974 election was one of the most peculiar, and
perhaps one of the most important, in British electoral history;
it will be discussed more fully in the following section. It was
called as a referendum on a specific policy issue for the first
time since Stanley Baldwin did so — also unsuccessfully —
over the tariff issue in 1923. As all know, the election not only
stimulated the highest voter turnout since 1951, but also a
mass exodus from both major parties — towards the Liberals
in England and the Scottish Nationalists north of the Tweed.
[Burnham W.D. 1978 p.280]
[c] The beginnings of transition were widely recognised at
the time:
The electoral change in February 1974 was therefore quite
exceptional, not simply in magnitude but also in direction: the
British pendulum stopped swinging. [Crewe I., Särlvik B. &
Alt J. 1977 p.132]
[d] The ratchet was also seen to slip in both 1959 and
1983:
The decline of loyalism within both major UK parties in the
1970s is well attested. Less obvious is the slip in support of
1959, linked to the resurgence of the Liberals in that year.
[Budge I. and Farlie D.J. 1983 p.126]
[e] The implications of the changing geography of voting
have been quickly realised:
A feature of voting behaviour in Britain in recent years has
been the increasing volatility of the electorate, with a growing
proportion prepared to shift allegiance between elections.
[Johnston R. & Pattie C. 1989 p.241]
[f] Instability exists in a superficially increasingly stable
electoral system:
If the socio-economic class cleavage basis of our present-day
two-party system does in fact develop into a more even
geographical distribution of partisan support, then in the last
stage of the developmental model of bias the voter proportion
distribution is symmetric and has a low variance. In terms of
our beta-binomial model, intra-constituency variance gains in
relative importance as the inter-constituency variance of the
voter proportion distribution declines. The result will be large
non-partisan ‘winner’s biases’ producing an increasingly less
stable parliamentary system. [Gudgin G. & Taylor P.J.
1973 p.23]
Chapter 5: Transforming the Mosaic
101
the electorates of their respective election. New seats squeeze in
between their neighbours and old ones are squeezed out79. All the
time the general shape of the country is changing as people migrate
both out of cities, and, in general, to the South. The changing pattern
of turnout is also interesting (Print CIII).
Politics is the animal life of our landscape. The political life can be
very variable, but is less affected by season than is the vegetation
cover of employment. Every aspect of our social landscape is subtly
connected to everything else, as are all the changes and pictures and
explanations. That is why it is worth looking at so many facets of life,
rather than concentrating on illuminating one corner of one image in
the gallery of pictures that exist to be seen.
5.7 Erosion and Deposition
The British social landscape changes. The industrial geology is most
intransigent, but when it is altered all else must change. The
population structure is stable, but reflects movement and the changing
79 [a] Only very simple electoral change can be shown on
a single map:
Third, few maps have been constructed to show electoral
trends on one map. This can only be done when constituency
boundaries remain unchanged, but even when this condition is
present too many results are simply presented as a series of
maps for each election. For example, Adam failed to devise a
map which showed electoral trends in France during the
period 1947-62 even though the electoral boundaries remained
unchanged. Trends in the pattern of election results can be
shown by a bar-graph for each unit, where each bar represents
one election and its length is proportionate to the percentage
of votes obtained by the successful candidate. Electoral results
in Belfast during the period 1920-1957 have been mapped this
way. [Prescott J.R.V. 1969 p.382]
[b] The time series shown here is as long as that described
below, the two-party decline does appear to have been of
fundamental importance:
Until time-series data over a period similar to, and preferably
longer than, that which forms the basis for this paper become
available, the true impact of declining partisanship on the
public legitimacy of the parliamentary system will remain
unknown. [Crewe I., Särlvik B. & Alt J. 1977 p.188]
[c] Just as it is important to consider as many places as
possible, times studied too should be numerous:
Fifth, it is important that conclusions should be based on the
analysis of as many elections as possible. Reliance on a single
election is dangerous because special issues or circumstances
may produce an atypical voting pattern. [Prescott J.R.V. 1969
p.381]
[d] The tendency for contiguous constituencies to show
similar behaviour is striking:
Spatial continuity has been a major characteristic of British
electoral geography for more than fifty years (Johnston,
1983). It has continued since 1979, but with substantial and
growing regional variations. The country was much more
polarized in 1987 than in 1979: the London suburbs and
exurbs, East Anglia, and the South West (though excluding
Devon and Cornwall) have swung markedly to the
Conservatives; the East Midlands and the nonconurbation
parts of the West Midlands form an intermediate buffer zone
of little change; and in relative terms the rest of Britain has
moved to Labour. The Alliance, too, finds its greatest support
largely, though not exclusively, in the areas of major
Conservative strength. [Johnston R.J. & Pattie C.J. 1987
p.1012]
Chapter 5: Transforming the Mosaic
102
fashion for having children, the end of an older picture and the
beginnings of a younger one. This is the soil of our landscape and it
comes in many colours. The changing positions of lifetime migrants
show several aspects of this restructuring through the movement of
people, to where they choose to live and to where they are constrained
to reside. The living parts of our landscape, those which change dayto-day, change fastest. The distribution of jobs, wealth, housing and
how people vote depend upon all the other layers in the overall picture
and on each other to an extraordinary extent80.
80 [a] Again, the North/South simplification should be
avoided:
An interpretation of the May 1979 general election results as
indicating the growth of a ‘two nations’ situation within
England is an over-reaction to the simple cartographic pattern
of changes in party allegiances. The analysis suggests that the
inter-regional variation that occurred resulted from the
consolidation of the strength of the Conservatives in regions
where they did well in 1974 and relatively small shifts to them
(relative, that is, to the national trend) in regions where
Labour and/or Liberals performed well in 1974. [Johnston R.
J. 1979 p.296-297]
[b] There has, however, been a regional polarization in
some attitudes:
The Conservatives, in contrast, seem to be much more
favourably placed: the pressure on the Conservative
governments since 1979 to change their policies specifically
to woo the industrial ‘north’ has been far less than that on the
Labour party opposition to adjust its political manifesto in
order to win support to the ‘south’. The Conservative electoral
base in the ‘south and east’ and the Midlands — which
together contain 60 per cent of British parliamentary
constituencies — is large enough to assure the party political
success. [Martin R. 1989 p.51]
[c] It is best to keep in mind the relative sizes of the
respective populations:
The popular picture of urban and regional growth in Britain, in
which the South is growing and the North declining, dates
from the inter-war years but continues to exert a powerful
influence. Today it is at best only a partial description of the
truth, as Chapter 2 demonstrates. For example London, at the
heart of the South, has lost well over half a million
manufacturing jobs during the last twenty years. To put this
into perspective, London has lost almost as many
manufacturing jobs as Scotland ever had. Indeed, some of the
fastest-growing areas are found outside the traditionally
prosperous South East and Midlands. This picture is, in fact,
one of considerable complexity. As a general rule the
differences within regions are far greater than the contrasts
between them. [Fothergill S. and Gudgin G. 1982 p.6]
[d] The growing divisions within London are stark:
Nevertheless, these trends suggest it is not impossible to
envisage the development in the not too distant future of a
socially polarized inner London, divided by tenure, with
middle class owner-occupation juxtaposed with a residualized
and predominantly working class public rented sector. Those
groups excluded from this process may be displaced into outer
London where many inner suburban areas may become
transformed into lower value ownership mixed with much of
what will be left of the private rented sector. The net result
therefore will be a stabilising but polarized inner city and a
declining suburban ring. In the process ‘inner city’ problems
may become gradually displaced into the suburbs. [Hamnett
C. & Randolph B. 1983 p.164]
[e] As the former capital of a long-gone empire London
has suffered greatly:
In 1987 London’s economy is in deeper crisis than it has been
in for a hundred years. In certain clear respects it is worse than
the 1930s. The rigours of those depression years affected other
regions more than London and did not bring quite the same
extent of misery and insecurity to the capital. [Townsend P.
with Corrigan P. & Kowarzik U. 1987 p.12]
[f] The second largest conurbation is often overshadowed
by the first:
The West Midlands once again emerges as a black-spot where
the cumulative human impact of recession was most severe,
(particularly on this percentage measure: as a once-prosperous
metropolitan region the scope for decline was greater here
than in traditional problem areas such as Liverpool and
Sunderland). [Green A.E. 1983 p.23]
[g] We must consider all these places and times together:
It would appear that the attempt to separate considerations of
“regional structure” from those of “secular” and “historical”
change can yield only partially valid results at best. [Duncan
O.D., Cuzzort R.P., & Duncan B. 1961 p.174]
Chapter 5: Transforming the Mosaic
103
For key contributions to the debate on the geography of polarization
in Britain see: Taylor P.J. 1979, Spence N.A., Gillespie A., Goddard J., Kennett S., Pinch
S. & Williams A. 1982, Goddard J.B. & Champion A.G. 1983, Champion A.G., Green A.E.,
Owen D.W., Ellin D.J. & Coombes M.G. 1987, Hamnett C. 1986, 1987, 1989a, Townsend P.
with Corrigan P. & Kowarzik U. 1987, Curtice J. 1988, Johnston R.J. & Pattie C.J. 1989a,
1989b, Halsey A.H. 1989, Pond C. 1989a.
How the landscape is changed depends on other forces which we have
not yet examined. It is the flows of people, like flows of water, which
both maintain and alter the picture. Every day the flow of people to
work links industry to population. Over the days, months and years,
people suddenly move in a quite different way: they move to different
homes — they migrate. It is the streams of migration which sculpture
our landscape, transporting its structure, depositing a new workforce
and eroding the old. These flows of people, which maintain, and
change the social landscape are the subject of the next chapter of this
dissertation.
104
Chapter 6: Cobweb of Flows
The developments of census cartography should be based first and foremost
on the use of models of the dynamics of the observed or planned area on the
long term as well as the short term. Look upon the population and its
various activities as a part of a vertically-rising stream in space-time with
oblique tributaries of movements in a short chronological perspective and a
longer one (for example, daily journeys to work and migratory moves
respectively).
[Szegö J. 1987 p.200]
6.1 What Flow Is
Flow is more than change. It describes the static structure of change;
how the change came to be, what changed. Change is a difference;
flow is an entity in itself. It is of an order of complexity above change
and involves an entire order of magnitude more of information. In our
social structures it is the movement between places, rather than
alterations within them, which are responsible for the restructuring
we see81.
81 [a] Showing movement is one of the most difficult
cartographic challenges:
[b] The quotation which began this chapter continues by
advising:
When the information involves both TIME and spatial or
GEOGRAPHIC ORDER, the correspondences translate a
MOVEMENT: movement of the pendulum; migratory,
demographic, or social movement. ... But when the two planar
dimensions are utilized to represent space, no planar
dimension remains available to represent the “time”
component; this is the basic problem with the representation
of movement in cartography. There are three solutions. Construct a series of images (figure 2). As in the cinema, this
solution can be applied to the most complex of movements.
But here the number of images is limited by the reading
process: with a long series it is difficult to suggest motion. Represent the path and direction of a moving body (figure 3).
This solution can suggest a continuous movement on the
plane, i.e., MOTION. However, we must consider both the
nature of the moving body, which can be a point, a line, or an
area, and also the complexity of the movement (with or
without reversal), which can only be perceived with a simple
division of the plane. -Utilize a retinal variable (figure 4). The
time component is divided into ordered categories represented
by the different steps of an ordered retinal variable. This
solution depends on the possibility of defining a small number
of categories, since the ordered retinal variables are relatively
limited. The figure does not generally suggest movement on
the plane. [Bertin J. 1983 p.342]
Utilize these dynamics as basic conceptual elements — in the
analysis of the past as well as in the planning of the future.
Document this approach with text, tables, diagrams and maps
in a co-ordinated manner. Let all these elements create a
linked chain around the basic concept. Do not sort out too
much: present even the less-formalized thoughts, sketches,
etc. if they help reproduce the dynamics. [Szegö J. 1987
p.200]
[c] Solutions to showing flows through distortion are not
new:
For early thematic maps developed in the nineteenth century,
concerns were not with planimetric accuracy, but with how
and why the environment and society function as they do. For
example, Charles Joseph Minard, one of the originators of the
flow-line map, placed considerable emphasis on accuracy of
data value depiction while freely altering geographical
position to allow effective data presentation (Robinson 1967).
On his 1850 map of coal exports from England, he enlarged
the Strait of Gibralter to a width of almost 500 miles (805
kilometers) in order to allow the flow line to pass through the
strait. [MacEachren A.M. 1987 p.102]
Chapter 6: Cobweb of Flows
105
The full matrix of travel to work flows between British
wards contains, in theory 10444², or over 100 million
separate counts. It was not possible to obtain flows
within Scotland from the census records available. Only
434,340 or half of one percent of all possible routes were
actually travelled, by the 20,602,790 people working.
People tend to work near to where they live. The entire
matrix of commuting flows was stored as a run length
encoded binary file of only 628,752 bytes (small enough
to fit on a floppy disc). This was achieved by sorting the
flows from each ward in ward order and recording only
the displacement in ward number and size of the flow
(each in four bits: a carry option could be set if this size
of field were inadequate). The set of flows from any ward
could be determined instantly and the entire matrix easily
held in computer memory.
To paint a picture of the
static social structure, at
any point in time, we need
only know the situation in
each cell of the structure.
To show where that
structure is changing we to
know the situation at at
least two points in time for
The migration streams were somewhat more dispersed
every cell. To show how
and also subdivided by sex. 893,941 streams of
4,210,900 people moving between 1980 and 1981,
that structure changed, what
stored in a file of 1,453,252 bytes (which could also be
moved from where to
held in computer memory).
Figure 17: Storing the Flows
where,
information
is
required about the relationships between all cells (Figure 17). Flows
are real. The people they measure did move from one place to another
(Print CIV). This is the structure of change, the structure of
movement82. It forms a cobweb that links places.
82 [a] Most census mapping is of the distribution of nighttime population:
This effect, which sometimes distorts at least some aspects of
city life, can be overcome in some degree by measuring
changes over time and movements in space; the static
structure is only a departing point for the analysis of a living
city. [Shepherd J., Westaway J. and Lee T. 1974 p.112]
[b] The problems and errors of data collection are usually
much greater when looking at flows; many are known and
some compensation can be made:
The correlation coefficient (Pearson’s) between inter-regional
flows from the two sources is 0.996, and between FPCAs is
still 0.980. We can be confident, therefore, in using NHSCR
re-registration data to update the picture of internal migration
provided by the 1981 census to 1985-86, and they are
employed extensively in the chapter. [Rees P. & Stillwell J.
1987 p.5]
[c] A particular example of unreliable information on many
peoples’ movement is given by:
The figures for Scotland in 1975 and 1976 show considerable
fluctuations which can be attributed to the operation of the
recording system rather than to real changes in migrant
numbers. [Ogilvy A.A. 1982 p.67]
[d] One unfortunate preoccupation of research is to
measure physical distance and relate it to migration — with an
accuracy that suggests every minute spent by the removal van
is of crucial importance:
Distance is measured as road milage between the zones and
historical tests suggest that the negative power decay function
(dij-ß) is preferable to the negative exponential form. [Stillwell
J.C. 1986 p.181]
[e] Another example is where:
Distances were estimated (in miles) on a straight line basis
between population centres of gravity for the counties, with
diversions being forced around major estuaries and inlets.
[Gordon I. 1982 p.9]
[f] Deviation from the expected propensity to migrate was
mapped in some of the illustrations in this dissertation:
To discover any flows of unusual magnitude between certain
areas, we calculated expected migration flows. These
prognoses are derived from the total out-flow from one area
and the total in-flow to another. (These form the expected cell
frequencies that are also determined in Chi-square analysis.)
The expected frequencies are subsequently compared with the
observed frequencies (actual flows). The difference reveals
any unusual attraction or repulsion exerted by an area. [Jobse
R.B. and Musterd S. 1989 p.247-248]
[g] It is the streams of migration that are real and of
importance — not the marginal distributions they produce:
The simple framework that reduces migration to ‘pulls’
towards desirable locations and ‘pushes’ away from less
desirable ones cannot adequately explain even total migration,
let alone the spatial structure of streams. [Mueser P. 1989
p.196]
Chapter 6: Cobweb of Flows
106
The final step in complexity taken here is to look at the change in
flows. Again the information required at least doubles; the flows
between all pairs of cells at several points in time. The differences
between webs are explicitly not counted in actual people. The measure
of how many more or less people moved between two places from one
pair of years to another provides an abstract quantity, not easy to
comprehend.
6.2 What Flows There Are
Social science abounds with flows. Many are not spatial. The flow of
votes between political parties would produce a small matrix within
each constituency or ward. Each element of the matrix would tell us
how many people changed their vote from one party to another, or
chose not to vote. Often flows are not given, while change can be
derived.
Flows on and off the unemployment register are an aspatial set which
are recorded. Here, within every employment area, only two states are
given, moving on or off the register. With a long time series, however,
these little matrices can tell us a lot about the frequency and length of
unemployment, and the spatial distributions of these occurrences.
Flows of spatial movement abound and are usually categorised by
purpose of movement. Some have been mapped in the past83, but we
83 [a] The limitations of mapping only night-time
population were realised at an early stage:
The temporal aspect of thematic map data is also undergoing
change. Emphasis has always been on relatively static, easily
managed information even though these data may not
represent the most crucial environmental variables. Maps of
urban population based on night-time residential distribution
are good examples. Potentially more interesting and revealing
maps of daytime, rush hour, or non-residential night-time
populations seldom exist! [Muehrcke P. 1972 p.8]
[b] Maps are not well suited to showing flow:
The map which has been the traditional instrument of
expression in geography has the disadvantage of treating all
organisms and objects as if they were more or less stationary.
Though spatial flows may be mapped, it is still useful to have
a mode of expression which is more attuned to a dynamic
view of nature and culture. A major advantage with the time
geographic notation system is that movements and changes in
location can be registered in the paths or trajectories in a timespace map, just as the path of a jet-plane can be seen in the
Chapter 6: Cobweb of Flows
107
have little information about many others, frequent flows to go
shopping or to school for instance. Less frequent, but important
moves, to holidays, hospitals or further education, for example, are
also difficult to obtain data for, or estimate.
The only spatial flows regularly recorded and disseminated are those
between home and workplace, and migration from one home to
another. Travel to work flows are sampled at the census. They link the
basic static points at which people are regularly enumerated, they give
day and night time populations and tell us how these change.
Migration is counted at a fine spatial scale by the census. A useful
series can be obtained from the National Health Service central
register of patients. Migration flows are collected to see how the night
time population is changing in the medium term. The creation of flow
matrices was a by-product of this. Here I show how the cobweb of
migration is wrapped around the honeycomb of social structure in
Britain, enabling important insights to be realised (Carrier N.H. & Jeffery
J.R. 1953, Davis N. & Walker C. 1975, Redford A. 1976, Gordon I. 1982, Green A.E. 1986,
Boden P., Stillwell J. & Rees P. 1987, Stillwell J., Boden P. & Rees P. 1988, Boden P. 1989,
Mueser P. 1989, Green A.E. & Owen D. 1990).
sky a few minutes after it has passed. A sequence of events
and activities for individuals and objects thereby becomes
frozen into a kind of historic-geographic matrix. This is in
contrast to the traditional overcommittment to the two
dimensional map as an analytical tool.
In a time-space region, each individual can be visualized as a
continuous path starting in a point of birth and ending in a
point of death. Depending on the observation period,
individual-paths can be referred to as day-paths, year-paths or
life-paths. This corresponds to the concept of life-time in
demography, an idea initially conceived by the demographers
Bexter and Lexis (Lexis 1875), mainly as a temporal concept.
Hagerstrand generalized this idea to a time-space concept in
his population mobility studies, but he also looked into time
perspectives shorter than the year, which is the conventional
unit of time in demography and demometrics. [Carlstein T.
1982 p.41]
[c] Again, it is the individual streams that must be
visualized:
In studying migration, it is common to focus on the total
number of moves to and from locations, ignoring the
particular paths traversed by migrants. Such an approach
allows analyses to focus on the way that location
characteristics influence total number of migrants, abstracting
from the complexity of spatial relations among locations. Yet,
to understand the meaning of these totals, it is necessary to
trace how the aggregates derive from decisions of individuals
which, in turn, are shaped by the spatial character of the
migration choice. We believe that the regularities in the
streams of migration are important in revealing the processes
of migration and the way that location characteristics
influence migration. [Mueser P. 1989 p.186]
Chapter 6: Cobweb of Flows
108
6.3 Unravelling the Tangles
Places in Britain can be divided into two; those where more people
leave for work in the morning than arrive — residential, and those
where more people arrive for work than leave — industrial. Such a
map could have two colours, black and white, on a grey background,
and would show the rings and sections of the basic pattern. But how
much would this picture tell us?
Elaboration can begin by moving from two colours to a continuous
scale from black to white. The image is smoother. Just how many
people are moving though? I can show the proportion of people
moving out of each place, or those moving in, or both. This can be
done with the colour of the cells, or their size; size for the proportion
involved, darkness for the proportion outgoing, for instance.
Next, we consider where, in general, people are going to and coming
from? This is implied by the rings, but may not be as simple as it
appears. I can show the average direction of outgoing movement by
changing the shape of each cell to an arrow, which points to where the
people are travelling.
Finally, how far are people moving, or better still, how long is it
taking them to get there? The length of the arrow is another aspect we
can alter. The length is in proportion to the average time it takes
people to travel; the direction, where they go; the size, how many of
them go; and the colour — the darkness — what proportion are
outgoing.
This succession of images just described has been concerned with
average flow. Such maps are useful, for instance in studying
commuting, because most areas are either residential or industrial,
Chapter 6: Cobweb of Flows
109
most people go in roughly the same direction, and travel the same
amount of time. It is only the nature of travel to work which allows it
to be mapped like this84. The arrows could not be used to show where
people were coming from, or how far they travel to get to the shops
for instance, as these aspects are too variable, being concentrated in
the centre of cities and large neighbourhoods.
None of what I have described so far has been flow mapping
(Thornthwaite C.W. & Slentz H.I. 1934, Schultz G.M. 1961, Matthews G.J. 1970, Morrison
A. 1970, 1971, Phillips D.J. 1977, Beddoe D.P. 1978, Craig J. 1981, Tamsma R. 1988). It
has been the mapping of gross averages and tendencies. It is similar in
assumption to mapping locally varying characteristics of a population
at a regional level. This is regional mapping of flow at the local scale,
when the ward level is used.
The simple travel to work pattern is as much an artifact of this method
as it is a reality. From seeing these pictures you would think you could
draw lines dividing the headwaters of the flow to define travel to work
catchments. Whether it is possible or not depends on how much flows
84 [a] Travel-to-work flows are spatially condensed, but
showing just the in- and out-flow to places can be
problematic:
Map 11 shows in- and outcommuters on one map. The
problem arises that, in some cases, the number of in- and
outcommuters are almost the same so that they should be
represented by the same size of circle. If the same size of
circle is used for each, there is no rim left to indicate which
group predominates. If it is attempted to show a rim, then the
graded circles are not accurate. The main advantage of this
method, however, is that in- and outcommuters can be
represented on a single map. [Dale L. 1971 pp.17-21] [a
technique, similar to the one described, with similar
problems, was used in some of the illustrations in this
dissertation]
state were indicates separately, the multiplicity of lines would
have made the map totally illegible, but through combinations
of migrations in the same direction it has been possible to
preserve legibility and still to show what was intended.
[Thornthwaite C.W. & Slentz H.I. 1934 p.14]
[c] Showing only flows above an average propensity
produces comprehensible images which still represent the
majority of the people under study:
In the second category we have been able to demonstrate that
the optimal threshold for the deletion of entries is the average
flow size. This data selection rule deletes as much as 80% of
the flow arrows. But generally only 20% of the flow volumes.
[Tobler W.R. 1987 p.348]
[b] Only a minority of all actual flow streams can be
shown between many places:
[d] We are only concerned with which places are
connected, not about the actual routes travelled:
The cartographic difficulties involved prevent the
representation on one map of all net movements from each
state to every other state, yet, since it seemed necessary for
purposes of comparison to get as much of the movement as
possible on one map, it appeared desirable to effect a certain
compromise. It was found that the rejection of total
movements of less than 10,000 into or out of any state resulted
in the omission of only about five percent of the total
migration. If the movement from each state to every other
Tracing the actual itineraries is not sufficient for representing
a system of relations. A map of maritime routes, even when
weighted, does not show the direction of trade among the
centres of activity; it shows the density of ships at sea. The
maritime trade among the cities of Europe and the
Mediterranean will only appear in its diversity, weight, and
geographic direction, when each connection, even through
maritime, is represented by a straight line (figure 4). [Bertin J.
1983 p.344]
Chapter 6: Cobweb of Flows
110
cross over, how many people travel in the same direction, how you
measure flow, indeed, how many people travel at all.
6.4 Drawing the Vortices
What have been discuused up to now have been vectors, single
attributes for sets of single areas — average direction and magnitude
of flow. Now I attempt to draw the matrix, not just a tangle of lines
connecting all places between which people flow, but a picture which
shows the static structure of the change in as much detail as possible.
Lines are used to show matrices of flow, as areas were used to depict
scales of attributes. Lines have length, width, colour, direction (by
arrow-head), and can be given order as they overlap. The first of these
qualities, length, conveys the strongest information, but is most
difficult to use, as the line links two places, and to alter its length
would make that connection ambiguous (Dale L. 1971, Forbes J. 1984, Tobler
W.R. 1987a, 1987b, Jobse R.B. & Musterd S. 1989, Becker R.A., Eick S.G., Miller E.O. &
Wilks A.R. 1990a, Teeffelen P.B.M. 1991).
The best choice we have, which has most influence over the final
image, is whether to draw any particular line at all (Print CV). With
areas this is not a choice; there is a place for every area. With lines,
there is only enough space for a minority to be shown (between more
than a couple of dozen places). A line should be drawn between two
places only if the flow between those places is significant. What is a
significant flow? There is a second and linked difficulty; this is the
most serious effect of the modifiable areal unit problem (largely
overcome above by the use of many small areas and cartograms). The
problem is that in particular the size, in people, but also the shapes, on
the ground, of the arbitrary areas between which flows are recorded,
Chapter 6: Cobweb of Flows
111
A flow between two places is of a significant magnitude
if it is larger than you would expect, given the
populations of the two places and the general
propensity to move between places. Here a flow was
deemed to be significant, and thus drawn when:
n
mijstm jist
>
pp
it jt
n
mijst
i
j
n
(n - 1)
p
it
i
Where mijst is the flow from place i to j between times s
and t, and Pit is the population which could move at
place i, time t (n is the total number of places being
considered). The equation therefore calculated whether
the geometric mean propensity to move between two
places is greater than the expected propensity to move.
Problems can occur when this method is applied to
commuting flows, particularly in central London where
many people are moving into an area of generally low
night-time population. It can be useful to use the daytime population estimate (caused by the flow) as the
denominator.
Figure 18: A Significant Flow
will affect the number of
flows counted, as much as
does the number of people
who are actually moving85.
More people will flow from
larger areas, but less people
move between such areas
(since more move within
them, Figure 18)), and more
will flow from long narrowly
shaped areas, than from more
compact places. Put simply,
the longer the boundaries are
over which flows are
recorded, the more will be
recorded.
One answer is the same as earlier, to use as many small areas as
possible, to record as many of the flows which occurred as possible.
85 [a] The problem of measuring propensity to move is
that:
A “migrant” usually is defined as an individual who moves
across the boundary of an areal unit within a specified time
period. Clearly, other things being equal, the rate of outmigration (number of persons leaving the area divided by
number of persons residing in the area) will be greater for
small areal units than for large areal units (see Bogue, 1959a).
Conceivably, if one could assume that the propensity to move,
i.e., probability of moving, a given distance, p(d), were
constant for each individual in the population, and if a simple
assumption about the distribution of population within areal
units were accepted, an “expected” amount of out-migration
could be ascertained by calculating the proportion of moves
carrying a mover across the boundary of an areal unit.
Expression of actual out-migration as deviation from
“expected” out-migration, then, might be regarded as a
measure of out-migration standardized for the size of areal
unit. The problem is further complicated by the fact that for
areas of the same size out-migration rates would be higher for
a long and narrow areal unit than for a circular one. [Duncan
O.D., Cuzzort R.P., & Duncan B. 1961 p.34]
[b] The problem is far from trivial:
All the variations introduced by spatial, numerical and
temporal aggregation procedures operate on origin and
destination data in an almost more bewildering variety than
they do on static data. Ideally a migration journey should be
represented by an arrow going from the point of origin to the
point of destination. In practice, individual arrows would be
too numerous, and the points too small to draw, so we
summarise by trying to pick out the main bundles of arrows
moving between pairs of areal units. Thus it depends entirely
what size, shape and position of spatial units we use, what
apparent bundles we pick up. [Forbes J. 1984 p.99]
[c] Movement is, certainly a geographical problem:
Migration is an event which by definition involves two places
— even if only adjoining houses. So spatial location and
spatial units are more basic to migration than to other events
such as births, deaths and consumers’ expenditure. For these
latter events, space is not fundamental to the event itself: it is
merely necessary to define the geographical limits of the
population to be included, though location may also be used
as an explanatory variable. But since migration is a movement
of people, analyses by location are intrinsic to it. While
aggregate migration to and from an area may suffice for
demographic calculations, analysis by place of origin or
destination are necessary to understand migration as a social
phenomenon. However for the whole country this involves so
many figures — even county by county analysis without any
sex or age analysis means a 54 x 54 matrix for England and
Wales — that the analysis becomes unwieldy. Moreover only
a few broad generalizations are possible as migration patterns
differ greatly from place to place. There is not some kind of
national average with regional differentials. [Craig J. 1981]
Chapter 6: Cobweb of Flows
112
The diagram shows how the size of migration streams
between two places can be shown by a single arrow:
The arrow-head points in the direction of net flow, the
size of the larger flow being shown by the width of the
black line, and the minor back flow by the width of the
grey line placed over it. A white border is placed around
the arrow to obscure lines lying beneath it and clarify the
image. The order of the arrows is then as that of spatial
continuity -flows between neighbours being uppermost
followed by second, then third, then fourth order
contiguities. The inset below shows part of the country
level migration structure north-west of London.
Here wards represent the
finest
resolution.
With
flows,
however,
long
distance movements can
come to dominate the image,
stretched across many other
areas and perhaps more
important flows86. Two
solutions are used, one
visual, the other statistical.
Firstly, we can order the
flows; lines can be placed
above, and partly obscuring,
other lines. This is achieved
by drawing a slight white
border around the lines. The
strongest flows could be
uppermost, but is was found
Figure 19: Drawing Overlapping
Arrows
most advantageous to put
those which were between
contiguous areas foremost, those of second order next, and so on. The
effect of this is to clarify the image, hide the most deviance and show
the most structure. Only strong unusual movements will show through
the mesh (Figure 19).
86 [a] Experimentation is required to overcome many of
the problems of movement mapping:
The question of “what” to map has been specified; it is the
“how” of mapping that remains to be done in more detail. As
the methods discussed in this paper are attempted, further
advantages and disadvantages will be discovered. This process
of experimentation will be a most valuable way of
determining the best cartographic techniques to apply to
journey-to-work data. [Dale L. 1971 p.34]
[b] Many different solutions have been suggested:
For all but the simplest networks these link data displays have
many intersecting lines and are difficult to interpret. There are
several possibilities for reducing clutter. One is to shorten the
line segments, that is, instead of drawing the line segments
50% of the way between nodes, draw them 30% or 10%, say.
Another is to draw only lines whose corresponding statistic
falls above or below some threshold. The difficulty with these
ideas is that it is quite hard to come up with a good heuristic
for setting these thresholds or line lengths (or overall line
thickness, for that matter) before making the display. [Becker
R.A., Eick S.G., Miller E.O. & Wilks A.R. 1990 p.93]
[c] Use of the cartogram opens up previously overcrowded
areas of the image.
Most traffic flow maps utilize variations of line width to
portray differences in traffic volume. There are times,
however, when crowded conditions on a map, extreme
constraints in flow, or other factors constitute a cartographic
problem and make something other than expected lines
desirable. [Schultz G.M. 1961 p.18]
Chapter 6: Cobweb of Flows
113
Secondly, we only draw flows which represent more than a particular
type of average propensity to move. The proportion of an area’s
population travelling to another area to work must be greater than the
national average propensity for the line to be drawn. This usually
means that over three-quarters of those travelling are represented on
the image, and that these are the most typical commuters. This
technique is particularly useful when flows between all the wards of
Britain are being shown, as there is too little space to use the visual
ordering technique effectively, and even the widths of the lines cannot
be gauged (Print CVI).
Width can still be used to convey magnitude of flow, and arrow-head
to convey the direction. The less the number of areas between which
flows are being drawn, the more effective these methods can be.
Colour can be reserved to show other things about those who are
travelling, what kind of areas they are coming from or going to, what
kind of people they are. The length of the line is a good surrogate for
the time taken when the population cartogram is used as the base. This
base also makes the picture clearer and gives some meaning to the
density of lines (Print CVII).
The cobwebs of travel to work are not as simple as they were first
drawn here. By looking at a relatively constrained set of flows we
have not encountered all the problems this kind of mapping can create.
It should also be noted that the picture produced is very sensitive to
the denominator used (Prints CVIII & CIX).
Chapter 6: Cobweb of Flows
114
6.5 Commuting Chaos
What the commuting flows show us is the well known city structure
of Britain, and the extent to which this is a true interpretation. On the
population cartogram the flows are much more confused than in
Euclidean space. This brings the image closer to the complexity of
reality87 (Dickinson R.E. 1957a, Lawton R. 1968b, Watts H.D. 1968, Census Division,
OPCS 1976, Becker R.A., Eick S.G., Miller E.O. & Wilks A.R. 1990b, Hodgson M.J. 1990).
Most people, in fact, do not work, and many of those who do, do so
within the ward in which they live. Thus even at ward level we are not
representing the majority of people. Circles, their areas in proportion
to the number of people living and working in the same ward, can give
an indication of this phenomenon, while the flow lines between wards
are still shown.
At this resolution the direction of flow is implied from the context —
where the lines converge. Where lines cross it can be seen that the
directions differ. There is a problem when they go in completely the
opposite direction, but this is rare. The magnitude of flow is also
difficult to see as the lines all appear very thin. This is partly because
almost all flows are small, but this could be compensated for by
making large flows darker.
Colour can be used effectively to show how the structure of flow
relates to other social structures (Prints CX & CXI). The lines can be
87 [a] The lines appear at an almost constant density on the
cartogram, as people move in all directions:
Programming the mapping of other than the most crude
versions of such flow maps is not trivial, not least because
large flows tend to occur between areas close together on the
ground and numerous lines occur if all flows are mapped.
Most frequently, they are mapped by arrows whose width is
proportional to the flow involved. [Rhind D. 1983 p.176]
[b] Visually, ordering lines according to spatial proximity
relieves some of the confusion:
There is a particular problem in using line segments to connect
locations on a map: the geographically longer lines are
visually dominant. In many data sets, such as migrations or
trade flows, the flow between locations varies inversely with
distance. One consequence is that, at times, short but
important lines may be difficult to see because of the long,
less important lines. One possible way to alleviate this
problem is to use wide lines for connecting nearby locations.
[Becker R.A., Eick S.G., Miller E.O. & Wilks A.R. 1990
p.289]
[c] An alternative idea is used for mapping average road
speed:
The map has some resemblance to the familiar traffic flow
map on which line width represents flow: but it is in fact more
satisfactory than the flow map in cities because the lines on
the speed map become narrower as they converge towards
city centres, usually the most crowded parts of the map,
whereas on the flow map the lines thicken and merge towards
city centres. [Morrison A. 1971 p.120]
Chapter 6: Cobweb of Flows
115
coloured according to a particular feature of the areas which people
leave for work, or about the work they do, where they are going, or
even all three aspects simultaneously.
Finally it would be possible, though perhaps too confusing, to place
the flow map over a smoothed three colour cartogram of some related
aspects of the population involved. If this were done with migration,
the cartogram could represent a few of the changes which the flows
were producing88.
The flows of travel to work are the heartbeats of the urban system,
people being pumped in and out of the cities. If they stopped there
would be no cities. The rhythm is well known, five days a week until
Christmas. It may miss a beat, but not often. Seeing the flows is
fundamental to understanding them. Understanding them shows us
how society ticks.
6.6 Migration Networks
What makes flow mapping in other subjects simple compared to
geography is that their flows are local, and in one direction — vector
fields89. Travel-to-work flows can be seen as an approximation to this;
88 [a] Much social change is thought to be caused by
migration flows:
89 [a] Simple vector mapping can itself still be difficult to
comprehend:
Taylor (1979) compared 1979 with 1966, at a regional scale,
and showed that the changing pattern of party support is
closely correlated with net migration patterns. The regions of
net population loss are those which have remained Labour
strongholds; those of net population gain have been those of
increasing Conservative strength. [Johnston R.J., Hay A.M. &
Taylor P.J. 1982 p.953]
We cannot directly display vectorial data on a twodimensional screen, for example as a set of little arrows, and
still interpret the result with the same ease as we would a
scalar image. Our visual systems simply are not adapted to
interpret large volumes of vectors in this way, whereas we
have superb abilities for understanding and interpreting
images or depth-cued surface displays. [Helman J. &
Hesselink L. 1990 p.62]
[b] However, most flows reinforce, rather than change, the
structure:
Many movements of individuals in the population do not alter
the characteristics of the area, since one council tenant often
replaces another, and one stockbroker moves only to sell his
house to another stockbroker and so on. Even where an
atypical individual arrives in an area, his new environment
may influence him towards adopting the characteristics of his
new neighbours. Correlations and slopes between 1966 census
data and partisanship change so little when using election
years other than 1966 that we would be surprised to discover
large biases due to the ageing of the census. [Miller W.L.,
Raab G. & Britto K. 1974 p.399]
[b] Here we are reaching the limits of what can be sensibly
drawn:
There is no doubt that graphic complexity would be enormous
if large populations in sizeable regions for a long time period
were to be drawn as paths; the picture of merely one day in a
small village is quite complicated even if computer plotters
were programmed to do the actual drawing. But the important
task of the graphic notation system is not to thrive on visual
complexity, but to reveal the under-lying logic of human
society and ecology in space and time. Simple graphs can be
strategically employed to pinpoint the elements, relations and
mechanisms which have principal importance for socialenvironmental structure and operation. [Carlstein T.
1982 p.45]
Chapter 6: Cobweb of Flows
116
most people go to work near to where they live and travel in the same
direction as other commuters. The extent to which this does not apply
was shown above. Migration takes us much further from the ideal,
simpler, situation to visualize. Migration flows can be much longer,
but migration does have a strongly localised tendency. If it did not do
so, as we see later, it would be practically impossible to map by the
techniques used here (Hollingsworth T.H. 1970, Ogilvy A.A. 1982, Brant J. 1984,
Devis T. 1984, Fielding A.J. 1989, Bulusu L. 1990, Salt J. 1990).
The more serious problem of flow mapping in general is that there
need not be a single strong net direction of flow. Flow can and does
occur in both directions. How can we show bi-directional movement
along a line? Various methods can be used when the flows are only
between a few dozen places and the arrows are still large enough to
have specific characteristics. Placing the smaller flow as an arrow on
top of, and in the opposite direction to, the larger flow is the solution
preferred here. Net flow is then represented by the differences
between the arrows, as it should be, being a difference and not an
entity in itself90.
90 [a] Gross migration rates measure the actual numbers of
people moving:
Gross migration stream (multiregional) models, on the one
hand, more realistically depict the phenomenon being
modelled (since there are no net migrants). The rates that they
use to represent directional movements are linked to the
populations at risk of moving and therefore measure true
propensities of migration (a feature that net migration rates
lack). [Rogers A. 1990 p.299]
[b] Some migration counts can include the same person
several times, so:
Although the number of moves into and out of the country
during the last ten years was close to 5 million — equivalent
to nearly 10 per cent of the total population — this does not
mean that 5 million different people were migrating. [Davis
N. & Walker C. 1975 p.2]
[c] There is much speculation over the processes which
might influence migration rates and destinations:
The data with which patterns of migration can be investigated
in greater detail does not yet exist, but the analysis of gross inmigration rates in the year preceding the 1981 Census
indicates that migrants are attracted to areas of employment
growth, but avoid areas of rapidly increasing unemployment
rates. [Owen D.W. & Green A.E. 1984 p.31]
[d] The relationships are not totally clear cut:
However, the trends in neither employees in employment nor
unemployment mirror those we have found in migration. The
number of employees in employment continues to rise from
1975 to 1979 while migration rates fall, although a rise in
employees parallels the rise in migration levels. If the
migration series followed unemployment rates closely, we
would expect greater falls in the 1979-82 period and no
recovery from 1982-83 in migration activity. [Rees P. &
Stillwell J. 1987 p.16]
[e] Housing necessity, rather than employment choice,
might be the major factor:
The fact that among men aged under 50 the percentage of
migrants who were unemployed was greater for those moving
within local authority districts than for all migrants is difficult
to reconcile with the hypothesis of movement to find work.
Although some districts cover a large area, a more likely
explanation for much of this movement could be to find
somewhere less expensive to live. [Brant J. 1984 p.30]
Chapter 6: Cobweb of Flows
117
To show just net flows would be simpler, but it would also be highly
misleading (Flowerdew R. & Salt J. 1979, Johnson J.H. 1984, Rees P. 1986, Rees P. &
Stillwell J. 1987, Owen D.W. & Green A.E. 1989, Rogers A. 1990). The majority of
the movements would be cancelled out. We are interested primarily in
how the change occurs, in how many people move and where — not
in a difference. In fact the net flows in migration are very small in
comparison with the absolute movements. Migration flows between
two areas tend to be roughly equal in both directions91. We can use
this fact to advantage when drawing images of migration between
many areas. The basic migration network is successively revealed
through a series of images (Prints CXII to CXIX).
One other solution which has been suggested (Tobler 1987b), is to reroute flows by the shortest path through contiguous areas. This was
tried, but it was found that for Britain the effect was to imply the
opposite of the true picture. Much of the flow went through the middle
of the country and the relationship between the metropolitan cities and
their hinterlands was reversed. The idea was abandoned.
One problem, which has held back work on the 1981 census migration
statistics, has been the method by which flows have been
amalgamated when too few people were involved, to a higher spatial
resolution. The solution to this problem is similar to that of the
changing constituency boundaries earlier — it does not matter. Just as
a series of election results can be drawn while the boundaries change,
so the lines on the map need not all be between wards, but can be
91 [a] But the distance migrants move is strongly related to
who they are:
[b] The illustrations drawn here show clearly how the top
third of the class structure dominate long distance migration:
The three economic classifications of the population all show
a similar pattern, but the socio-economic groups give the most
illuminating explanation of differential migration. Apart from
the effect of distance, tenure, age, family status, and sex, there
seems to be a tendency for self-employed and managerial
people to move rather less than many of their subordinates,
especially over short distances. This might be termed a social
effect, reflecting position in the hierarchy of status and power;
at longer distances, more economic effects become stronger,
so that the poor move much less often than the comparatively
rich. [Hollingsworth T.H. 1970 p.62]
The majority of labour migrants are middle class, in the 25-44
age group, and have middle-high incomes. They are
professional and managerial workers in a career structure that
encourages movement. Their occupational status frequently
entitles them to financial and other forms of aid which make
migration easier. They move within a housing market that is
especially geared to their requirements. [Johnson J.H., Salt J.
& Wood P.A. 1974 p.246]
Chapter 6: Cobweb of Flows
118
between amalgamations, as long as the basic populations of the areas
involved are used to determine their significance. In fact, the Census
Office may even have done researchers a favour in amalgamating,
creating clearer images of the basic movements, which can be
coloured according to the attributes of the migrants92.
6.7 A Space of Flows
Again, images of migration, the average distance and direction can be
drawn, at resolutions as fine as the ward level. These pictures show
that migration is a much more diffuse process than commuting.
Propensity is most useful, coupled with distance it gives us an idea of
how often and how far people move house. Without being able to see
the structure of individual streams we are blind to the pattern.
Between the ten thousand wards in Britain there could be as many as
one hundred million migration streams. In fact only one percent of
these actually occur in a year — one million streams carried some five
million people between the years 1980 and 1981. The flows are
generally equal in both directions. If we amalgamate these two flow
propensities as a geometric mean, and plot only those which are
significant, merely one hundred thousand lines need be drawn,
representing the spatial distribution of the movement of the majority
of migrants between those years at the finest available resolution.
These lines need no longer convey direction, and are generally too
thin to give an impression of magnitude. Each represents, on average,
92 [a] Colouring flows by the attributes of migrants may
show which divisions are actual, and which are fictional:
An interesting and here easily seen feature not commented on
by Hamnett is that skilled workers choose a region in the same
way as the professional, managerial, intermediate and ownaccount workers. The big divide between the well-off and the
disadvantaged groups seems to be between skilled and
semiskilled workers. [Berge E. 1988 p.976]
[b] Some basic patterns are very clear:
Over Great Britain as a whole, the pattern of gross migration
levels seems to reflect the political map remarkably well, for
Labour Party strongholds correspond quite closely to places
where little migration takes place. [Hollingsworth T.H. 1970
p.33]
[c] As are the social implications:
The process is one of deprived people being left in the urban
priority areas as the successful move out to middle Britain.
[Halsey A.H. 1989 p.22]
Chapter 6: Cobweb of Flows
119
the change of residence of half a dozen families between two wards.
Again they can be coloured. This could be done according to the
nature of the area being left and that being entered, or by any aspect of
one or the other. Lines from obviously different areas should stand out
from the crowd. As we can see, the pattern of movement is deeply
embedded in the social structure (Prints CXX & CXXI).
What is striking about the images created is the extent to which
migration actually maintains the status quo (Rowley G. 1970, Coombes M.G.,
Green A.E. & Owen D.W. 1985, Champion A.G. 1989, Hubbuck J. & Coombes M.G. 1989,
Allen J. & Hamnett C. (eds) 1991, Fielding A.J. 1991). The vast majority of
movements are within similar areas and the boundaries can be clearly
seen93. It appears that there is a single, overriding spatial social
structure to Britain; a social structure encapsulated by the streams of
migration, crossed daily by travel to work. People in Britain must
work together, or at least at the same place, but they can and do live
apart. The patterns of their movements testify to this. These
movements are as much part of the spatial structure as are the pictures
of who lives in households or works in offices94.
93 [a] People have been able to travel further to work as
car ownership has spread:
Social changes are likely to add to economic changes in
loosening up the pattern of settlement in the industrial regions
of Britain in the next generation. [Lawton R. 1968 p.39]
[b] Most people do not move house when changing job,
however:
Most migrants had moved short distances. In 1981, of those
moving within Great Britain about 69 per cent moved less
than 10 kilometres (six miles) and only 13 per cent moved
more than 80 kilometres (50 miles)-distances measured as the
straight-line distance between the grid reference of the address
a year before census and the grid reference of the enumeration
district of the usual address at census. [Brant J. 1984 p.23]
[c] We can speculate about what other factors might
operate to affect these decisions:
If migration is to become more predictable, we can however
suggest what parameters will be important. In the first
instance, distance seems to matter a great deal; but we should
probably use distance merely to separate short moves from
long and regard distance as relative in our study of
occupationally-induced migration. Movement to quite a
distant place will still be likely if there are few nearer places
that could supply migrants. [Hollingsworth T.H. 1970 p.164]
[d] Migration to and from London is dominant, even when
sensible areal units are used to measure it:
Migration to and from London dominated population
movement in much of Britain: 56 of the 67 largest flows in
1971 involved the London SMLA. There was a tendency for
migration to London to come from a rather broader area than
migration from London, thus suggesting a process of
population redistribution operating through the Capital
(Johnson et al. 1974: Salt & Flowerdew 1980) [Johnson J.H.
1984 p.305]
94 [a] Travel to work flows have even recently been linked
to aspects such as political partisanship:
Some researchers have seen the workplace-residence
relationship as a key to explaining different patterns of
working-class behaviour in general. This is largely due to the
importance of the work situation as a determinant of
friendship networks, incomes and lifestyles. [Eagles M. &
Erfle S. 1989 p.118]
[b] People are arranged in space, through migration, over
relatively short distances:
It is also clear that, at the broader regional level, no simple
distinction can be made between regions of labour surplus and
shortage. Much has been written about the so-called ‘drift to
the South’ and the conclusion has been drawn from the use of
this generic term that the principal internal currents of
Chapter 6: Cobweb of Flows
120
With information from the National Health Service central register we
have an opportunity to depict how the flows of migrants are changing
over time. A change of flow is a strange concept. If between 1975 and
1976 two thousand people moved from Liverpool to London, and ten
years later only one thousand did, what does that tell us? Firstly we
must know whether the level of flow throughout the country has
fallen, then we need to know whether the flow from Liverpool has
fallen in general, or that to London dropped, and finally what has
happened to the movement in the opposite direction.
Mapping change of flow involves two images; one showing between
which places, and by how much, the relative increases have occurred
and the other showing where there have been decreases in the number
of moves. Circles centred on the places themselves can show what has
happened to their total in, out, and net flows. It is difficult to know
whether, with the use of dark and light arrows, these two images could
be put on a single map. What certainly could not be shown is the
changes over fifteen consecutive years that have been recorded up to
1990 between the ninety seven mainland family practitioner areas.
What we have seen in this part of the dissertation, has been how the
official and conventional place and time based information about
migration have been long-distance moves from the depressed
to the prosperous areas — for example, from north-eastern to
south-eastern England. To a large extent this view stems from
the misconception that unemployment is high throughout the
assisted areas. In fact this is not so and especially over the last
decade, unemployment in Britain has been developing a
pattern of greater local variation. Thus, within the assisted
areas, now covering virtually all the northern and western
parts of Britain, there are adjacent local labour markets with
widely differing levels of unemployment (Fig. 1.2). At the
same time, within midland and south-eastern England, there
are pockets of unemployment considerably above the national
average. This situation indicates that spatial equilibrium in the
labour market as a whole can be approached through
relatively short-distance movements of labour from local areas
of unemployment to nearby ones where job opportunities
are more plentiful. [Johnson J.H., Salt J. & Wood P.A. 1974
pp.9-11]
[c] The overall result of all these moves, at a gross scale,
has been the continued contraction of the major cities:
The single most impressive finding of the 1981 Census in
relation to population distribution was the massive decline in
population sustained by Britain’s larger cities over the
previous decade. The population of Greater London alone fell
by almost three-quarters of a million between 1971 and 1981,
a drop of almost 1 in 10 (OPCS, 1984). Even bigger relative
rates of decline were recorded by some of the provincial
centres, notably Glasgow (-22.0 per cent), Liverpool (-16.4)
and Manchester (-17.5). To some extent, the latter are related
to the North-South drift described in the previous section, but
they are also partly the outcome of a general shift in
population from more urban to more rural areas. Evidence for
the importance of this ‘rural-urban shift’ can be found in the
fact that London sustained such heavy population loss in the
1970s despite being located at the heart of the most dynamic
region in the UK. [Champion A. G. 1989 p.121]
Chapter 6: Cobweb of Flows
121
people can be turned into pictures depicting the spatial social structure
of this country made up from their lives. A rich tapestry has been
woven, the warp and weft of which are made up from the flows of
people to work and to new homes, different directions — holding the
picture together95. Both sets of lines can be placed on a single image,
illustrating how the two are linked. Without pictures we would never
cope with such complexity.
Much is missing from this as a complete view of British society.
Flows of money, wealth and goods would be essential, as well as
knowledge about their static distributions. What I have accomplished
here, though, is the ability to depict this type of information visually.
A change from tables to pictures, numbers to colours, words to
drawings, allows us to move from vague impressions to concrete
images.
95 [a] Some thought that housing and labour market areas
would roughly coincide:
[d] The relationships between commuting, migration and
economic growth and decline are strong:
Highly skilled and highly paid workers are likely to have a
much larger potential travel to work area than the less skilled
and low paid who are more likely to be constrained by the
difficulty and cost of commuting. But, such caveats aside,
there is an approximate congruence between housing and
labour markets which is imposed by the journey to work.
[Allen J. & Hamnett C. (eds) 1991 p.5]
Of particular importance has been the growth of relatively
high-level employment in both manufacturing and service
sectors in southern England along the M4, M3 and M1
corridors. The strongest performances in the North can mainly
be attributed to the surge in residential decentralization which
took higher-status people out of the major cities into
surrounding towns with a long manufacturing tradition, but
some of the more successful freestanding settlements
experienced significant growth in their service sectors, partly
in connection with the expansion of higher education. The
lack of improvement in the more rural and peripheral regions
of the north can be associated in part with the swelling of the
labour force by low-paid part-time female labour and lowskilled manual jobs for men, while the falling status of the
South Coast towns reflects a partial substitution of their
traditional high-class retirement role by the wider range of
locally based jobs and by long-distance commuters seeking
out lower house prices. [Champion A.G., Green A.E., Owen
D.W., Ellin D.J. & Coombes M.G. 1987 p.93]
[b] But, again, most people moving house do not do it
because of a change in employment:
Only 10.5% were moving house because of a change in their
job or location of work, while for 4.3% the prime motivation
for moving was to be nearer their place of work. Data from
the 1984 LFS reinforce this conclusion, with 10.5% of those
reporting a change in address claiming that the move was jobrelated. [Owen D.W. and Green A.E. 1989 p.119]
[c] Even the majority of moves between regions did not
involve a change of employer:
Figure 4.1 indicates that in 1981 over half of inter-regional
migrants in the UK (defined as those employed both one year
before and at the time of the Labour Force Survey) did not
change employer. The figures for 1975 and 1979 were similar.
[Salt J. 1990 p.54]
[e] The relationships also serve to constrain what can be
done to improve the situation:
The latter policies are unlikely to have a large effect on
unemployment in the inner city because of the induced
changes in commuting and migration, and often the types of
jobs created do not fit the skills of the inner city residents.
[Ermisch J. & Maclennan D. 1987 p.198]
122
Chapter 7: On the Surface
To undertake a project such as the design of this is roughly akin to painting
a landscape. One has a mighty scene at one’s feet with extensive views and
multi-faceted build up. It lives as clouds sweep over it, the light shifts and
continuously changing aspects stand out. From all these possibilities of
continuously changing pictures the task is to capture precisely that one
which is most apposite — for however much the panorama changes before
one’s eyes, the picture one paints is, even so, static.
[Szegö J. 1984 p.17]
7.1 2D Vision, 3D World
Many advocates of visualization claim the practice begins with
rendering surfaces. Anything simpler is merely presentation
graphics96. This thesis clearly rejects that argument, behind which is
often the desire to promote more expensive machines rather than more
useful images. What this thesis claims is that, if something can be
adequately represented as a two-dimensional image, it is often
detrimental to depict it as a more complex object, just as it is better
not to use colour unless it is actually needed (Prints CXXII & CXXIII).
Much of today’s three-dimensional visualization is unnecessary, and
often a damaging embellishment of what is essentially a twodimensional structure. The primary purpose is usually for dramatic
illustrative effect. A dramatic mountain range of unemployment is
more interesting to look at than the simple grey shaded cartogram, but
96 [a] Some claim visualization must be dynamic:
Visualization of scientific data is very different from graphical
analysis or presentation graphics. Visualization implies the
use of dynamic graphics to portray changes in an environment
over time, or to show the relationships between variables.
Dynamic graphics implies rapid update of graphic displays
based on operator input, or simulation of real-time changes in
an environment through display of movie loops. [Thompson
J.M. 1988 p.1084]
[b] The first results often elicit astonishment:
“The simulation has improved my understanding of the filling
phase dramatically”, Ellson declares. “When I first saw the
animation, I watched it over and over again. I thought
something like this was going on — but never exactly this”.
[La Breque M. 1989 p.527]
[c] The subject is still at an early stage of development:
We see a parallel between doing multimedia work today and
making a film in 1923. Filmaking today is a sophisticated,
major industry. [1991 Grimes J. & Potel M. p.50]
Chapter 7: On the Surface
123
is it more informative? We must weigh up the disadvantages of
obscuring features, emphasising the foreground, exaggerating the
vertical scale, and so on, against the advantage that the eye is used to
recovering meaning from surfaces, as that is what it is most often used
for.
We do not tend to think in three dimensions, however, and often
become greatly confused when forced to do so (Parslow R. 1987, McLaren
R.A. 1989, Peak K.D. 1989). It is because surfaces are what we are used to,
rather than implicitly useful, that we consider them here. We have
basically evolved two-dimensional vision, but live in a threedimensional world. We constantly estimate three-dimensional
structure from a series of two-dimensional images. We are good at
it97. The challenge is to harness that ability usefully; to visualize our
97 [a] We are not quite as good at understanding threedimensional structure as we may believe:
When a three dimensional scene is rendered into two
dimensional space with any level of abstraction, an ambiguous
image will probably be portrayed. This is compounded by the
fact that our eyes are not a window into the world, but instead
the world is created in our mind based on preconceived
models that vary from person to person (Gregory, 1977).
Therefore, if new computer graphic presentation concepts do
not match these preconceived models, then they are open to
mis-interpretation. A wire frame model presents the viewer
with the maximum degree of ambiguity. To compensate for
this loss of inherent three dimensional information, techniques
have been developed to increase the three dimensional
interpretability of the scene using depth cueing techniques that
attempt to match the perceived computer generated image to
our “natural” visual cue models. [McLaren R.A. & Kennie
T.J.M. 1989 p.87]
[b] It has been found that 90% of people are ‘3D-blind’,
including as many as 70% of engineers working with 3D
graphics:
The first problem is in design conception. Workers, unaware
that they are 3-D blind, are designing components which do
not accord with reality. Even top professionals have produced
faulty algorithms based on a false 3-D view. Most designers
agree with Robin Forrest that ‘3-D makes life difficult’ so
structures have tended to be designed in ‘two and a half’
rather than true three dimensions.
The second problem is in presenting the 2-D picture of the 3D artefact. Emphasis has been placed on producing ‘realism’
with a gradually extending set of depth clues: hidden line /
surface removal, perspective, shadows, colour and hue, stereo
... We employ enormously expensive systems such as ray
tracing to get closer to realism, but if reality itself allows for
misinterpretation of the scene, as in all illusions, standard
depth clues do not provide a solution and they are not even
necessary. It is possible to produce recognisable pictures of 3D structures which do not use depth clues. A line drawing of a
cube is recognisable in isometric projection and when using
an overhead projector often appears with reverse perspective.
In fact, for westerners, it is extremely difficult not to see the
cube but to see only a flat picture consisting of three
quadrilaterals. [Parslow R. 1987 p.25]
[c] Interactive control is crucial to grasping threedimensional structure:
Perhaps the fundamental hand-eye question is whether the
distinction between active and passive dynamic systems made
in the introduction is relevant to the strength of the 3D
illusion: Do the hands contribute to the eyes’ 3D perception?
Our hunch is that active control of the motion is a strong cue
in creating the illusion.
Note, however, that a surprisingly large portion of the
population do not perceive depth, and that for them, no matter
how many cues are present there will never be a 3D illusion. It
also seems that the popular distinction between “algebraists”
and “geometers” is relevant here. There are many data
analysts who would rather look at tables of numbers and
equations than at pictures of the numbers and equations,
strange as that may seem to some of us. [Young F.W., Kent
D.P. & Kuhfeld W.F. 1988 p.419]
[d] The use of two-dimensional terms, when discussing
multi-dimensional situations, illustrates how our thinking is
trapped in flatland:
The method used here attempts to find tight spherical clusters
in a multi-dimensional space. If the data structure consisted of
rectangles or triangles of overlapping clusters, then it would
not be correctly identified. [Openshaw S. 1983 p.261]
[e] There is often no need to work in three dimensions:
Many geographers still feel that when they discuss terrain they
are conversing in “three dimensions,” but this is an
unnecessarily complicated conception of terrain since we can
always reduce a problem one dimension by converting one of
the dimensions (variables) to a density. Thus, a contour map
can be viewed more conveniently as a density of elevations
rather than as a moulded surface. [Bunge W. 1964 p.16]
Chapter 7: On the Surface
124
world through surfaces when it is appropriate to do so, and to do so
effectively (Prints CXXIV & CXXV).
7.2 Surface Definition
A surface is the boundary, edge or limit of a shape. It exists in, and
encloses, a dimension one order above its own. It contains and defines
an object, while expressing form itself. Although the surfaces we are
concerned with here are mostly two-dimensional areas in threedimensional space, it is useful to drop down a dimension to consider
another form of surface, one-dimensional lines traversing and
enclosing two-dimensional space, more commonly referred to as
graphs.
Graphs show the relationships between two dimensions. Visually,
graphs illustrate the form of the relationship, turn simple equations to
life and project complex dependencies. Several graphs can be drawn
on a single plane to compare and contrast them. Complex graphs can
split and merge into many lines, but even a single line can contain
infinite complexity.
Visualization began most dramatically in computer graphics when the
Mandelbrot set was first discovered (Mandelbrot 1980). In the beginning it
was treated as a two-dimensional structure. Only later was it realized
to be the manifestation of an infinitely long one-dimensional line,
winding its way around the complex plane, never splitting, never
ending, almost two-dimensional, but not quite.
Nowadays, the Mandelbrot set is shown by animations flying through
highly colourful wispy three-dimensional landscapes. While
Chapter 7: On the Surface
125
staggeringly beautiful, its true nature as a one-dimensional object is
best shown by drawing the single black line which it determines on
white paper. Surfaces are not simple, just as graphs are far from
trivial98.
Much work has been done on how best to present graphs in statistical
graphics. The problem begins with the axes. If these are not directly
related to each other, the ratio between them is arbitrary, and its
choice can drastically affect the visual form. Rules have been
developed to aid depiction, but, to date, the most successful choice has
been to put the graph in a window on the computer screen and allow
the viewer to stretch it and view certain parts. This problem recurs
when we consider surfaces.
Visual improvement in graphing is achieved by transforming the axes
more generally. Logarithmic scales are most often used, but anything
is possible. Here we have a simple one-dimensional version of the
area cartogram problem to solve. A particularly interesting variant is
98 [a] Depth cues are essential to seeing surfaces:
Therefore, to obtain an objective impression of relevant
features, the surface must be illuminated from different
directions. The subjectivity is partially reduced if two light
sources of different colours are created by mixing reflected
light intensities from both sources. Thus most features of the
pattern are shown in one image but the image is less natural
and more complex to interpret. An illuminated hemisphere as
a legend aids the user to identify the slopes of the surface by
colours.
The most important advantage of the shaded relief, compared
to coloured content-variation surfaces or choropleth maps, is
that the reflectance of a given pixel is made independent of its
vertical position, so that features are revealed at any level of
contents and that linear features in the relief are clearly
brought out. Content levels are difficult to estimate by the eye
but the map gives a visual depth clue.
The major disadvantage of the relief shading is its subjectivity.
The effect of the shading is governed by the position of the
light source and the viewer, and by the relation between
content and the geographical scale. In the simplest model the
viewer is located at the zenith which is natural in this
application. [Bjorklund A. & Gustavsson N. 1987 pp.99-100]
[b] Animation takes us back to illustration:
Several trial films revealed one very necessary characteristic
of animated mapping: simplicity and extreme clarity are
essential. In a static map, the reader has time to interpret
complex or unclear information. However this is not the case
in animated mapping where the image must be interpreted
immediately. [Mounsey H.M. 1982 p.130]
[c] There is much more to animation than meets the eye:
To animate is, literally, to bring to life. Although people often
think of animation as synonymous with motion, it covers all
changes that have a visual effect. it thus includes the timevarying position (motion dynamics), shape, color,
transparency, structure, and texture of an object (update
dynamics), and changes in lighting, camera position,
orientation, and focus, and even changes of rendering
technique. [Foley J.D., Dam A. van, Feiner S.K. & Hughes
J.F. 1990 p.1057]
[d] It is possible to get over-enthusiastic about the potential
of the technique:
To recover the lost information from 4D to 3D, we can
continuously change the position and orientation of the
hyperplane, by either a pure translation or a pure rotation or a
combination of both, and obtain different 3D images
reflecting all aspects of the 4D Mandelbrot set. To recover the
lost information from 3D to 2D, we can change the position of
the camera around the image (even move inside) and have a
complete view of the 3D image. [Ke Y. & Panduranga E.S.
1990 p.222]
Chapter 7: On the Surface
126
An equilateral triangle can show the composition of the
votes of three parties, amongst a number of
constituencies, very clearly. Position (x,y) on the triangle
is calculated from the Conservative (C), Labour (L) and
Liberal/Alliance (A) proportions of the vote as follows:
C + L + A = 1
x =
(1 - C + L)
2
y = A 3
2
Position on the equilateral triangle formed then gives the
share of the votes cast in any one constituency, and the
distribution of all constituencies simultaneously:
Liberal
Conservative
Labour
Figure 20: The Electoral Triangle
99 [a] Upton has vigorously advocated use of the electoral
triangle:
The method of using the triangle appears to be one of those
things which is continually being rediscovered. The earliest
descriptions of the technique that the author has located date
from 1964, but it seems likely that others were using the
technique earlier. [Upton G.J.G. 1976 p.448]
[b] Use of the triangle’s "third dimension" also has a long
history:
Before leaving this subject a brief reference must be made to
an ingenious form of solid chart described by Professor
Thurston in several of his articles. It is called the tri-axial
model. By its use it is possible to take into account four
different variables instead of three as was previously the case.
It is a necessary condition, however that for each set of
corresponding variables three of them should add up to a
constant value, generally 100 per cent. The fourth is
unrestricted. [Peddle J.B. 1910 p.109]
100 [a] The triangle can clearly show the influence of
tactical voting:
In his analysis of the net swings between the two elections,
Steed (1975, p.338) suggested that tactical voting had been
the triangular graph99, where
the distance of any point
from the apexes of an
equilateral triangle increases
as the influence of what is
represented by that apex
upon the point declines
(Figure 20). This device is
used in this dissertation to
show the share of the vote
among three major political
parties for a number of areas
(Prints CXXVI to CXXX).
The forms created are
extremely
interesting100
(Upton G.J.G. 1976, Rallings C. &
Thrasher M. 1985, Gyford J., Leach S.
& Game C. 1989, LeBlanc J., Ward
M.O. & Wittels N. 1990).
important to the results, especially with regard to support for
the Liberal Party. He showed a clear correlation between
marginality and the decline of the Liberal vote, and also
between marginality and the change in turnout. He concludes
that overall a majority of those in marginal seats who would
have either voted Liberal or abstained if the constituency had
not been marginal instead supported the Conservative Party.
[Johnston R.J. 1982 p.51]
[b] Only recently have three parties stood often enough to
warrant the use of the triangle in studying local election
results:
Among English county councils the process of formal party
politicization was completed at the 1985 elections. [Gyford J.,
Leach S. and Game C. 1989 p.27]
[c] Party competition is clear when shown graphically:
The more that the Conservatives spent, the poorer the Liberal
performance, as well as visa versa, bolstering this
interpretation: Conservative and Liberal (Alliance) were
competing for the non-Labour vote. [Johnston R.J. 1986 p.77]
Chapter 7: On the Surface
127
Once the space in which the graph is to lie has been determined, there
remains only the relatively simple decision to take on the way in
which it should be drawn. Many different choices can be made,
however. A featureless line is usual, but bar charts and histograms can
depict particularly simple cases. Scatter-plots show the observations
upon which the line is based, and can be arranged to show
multivariate information. Repeated rendering of convex hulls around a
set of points produces something akin to a contour diagram.
7.3 Depth Cues
The fundamental problem in visualizing two-dimensional surfaces is
the need to provide depth cues and their unwanted side effects. These
are all the products of turning two-and-a-half dimensional information
into two-dimensional form: something has to be lost.
The most simple measure is to perform an isometric projection of the
surface, mapping all the points in three dimensions to two by matrix
multiplication. The most basic of these adds half the vertical position
of each point to its horizontal position, then scales the vertical position
by half the square root of three and adds to it the height of the point.
Three dimensions are turned into two, and a wire-frame image is
produced. The direction from which this frame is viewed is arbitrary,
and greatly influences what is observed. More importantly, what is
seen is often ambiguous. One two-dimensional view could be several
three-dimensional realities. And several two-dimensional views are
often required to convey one three-dimensional reality (Prints CXXXI
to CXXXVI)
To aid perception, a hierarchy of techniques can be employed. The
first of these is to use a perspective projection. Objects further from
Chapter 7: On the Surface
128
The orthographic projection onto image space (u,v) of a
point (x,y,z) with the viewpoint at an angle ( , ) is:
u = xcos
v = xsin sin
- zsin
+ ycos
+ zcos sin
The perspective projection at a distance (r) and with a
particular focal length (f) is given by:
u =
v =
f(xcos - zsin )
r - (xsin sin - ysin + zcos cos )
f(xsin sin + ycos + zcos sin )
r - (xsin sin - ysin + zcos cos )
For derivation, extension, and a full discussion see
Plantinga W.H. 1988.
Figure 21: The Perspective Projection
the viewer appear smaller
(Figure 21). This obviously
distorts the image. Secondly,
hidden lines can be removed
so that a wire-frame is no
longer seen, but a more
natural solid object is in its
place. Now, however, part of
the object is obscured. A
fishnet of parallel lines can
be placed over the surface,
their convergence signifying
distance,
but
their
orientation
remaining
arbitrary.
More sophisticated options make the image more natural. Lighting the
surface from a particular direction creates shadows and more subtle
cues, but lighting distorts any other colouring being used. Ray-tracing
makes the surface even more realistic, allowing for reflections, or
more usefully transparency, but still takes us further from the original
form (Adams J.M. 1969, Moellering H. 1980a, Grotch S.L. 1983, Holmes J.M. 1984, Lavin
S.J. & Cerveny R.S. 1987, Papathomas T.V., Schiavone J.A. & Julesz B. 1987, Dale R.S.
1989, Devaney R.L. 1989, Robert S.D. 1989, Gershon N.D. 1990, Moellering H. 1990).
The most useful depth cues are to be found in animation, particularly
where the viewer interactively chooses the direction to view from.
Rotation of the object, even simple rocking, helps greatly, although
diving with a camera down across the surface is more dramatic.
Parallax is the property being exploited here — the apparent
displacement of objects as the point of observation changes. All we
Chapter 7: On the Surface
129
are doing is making the image appear more and more like the real
world that we are so good at observing. Animation and ray-tracing can
be combined to produce stunning images101. The difficulty is in
gauging how much of the picture seen is a product of the techniques
required to make it look three dimensional.
7.4 Landscape Painting
As should be realised from the difficulty of visualizing twodimensional surfaces, the variability of their structure can be nowhere
near as great as that of graphs. Only the most simple surfaces are
susceptible to the depth cue method, as most surfaces in our real world
are of this simple form. A two-dimensional version of the Mandelbrot
set winding its way around three dimensions would, to us, look a
complete mess.
It is claimed here that what is seen in an image containing surfaces is
not truly three-dimensional, but suggests something just beyond the
101 [a] Animation can show us objects in apparently
featureless static images:
watching a spinning object. [Kaufman A., Yagel R., Bakalash
R. & Spector I. 1990 p.162]
We have already seen from Ullman’s (1979a) counterrotating
cylinders experiment, illustrated in Figure 3-52, that both the
decomposition of a scene into objects and the recovery of their
three-dimensional shapes can be accomplished when the only
available information is that afforded by their changing
appearances as they move. Each frame in that demonstration
consists of an apparently random collection of dots and is by
itself uninterpretable. Only when shown as a continuous
sequence does the movement of the dots create the perception
of two counterrotating cylinders. [Marr D. 1982 p.205]
[d] There are means of seeing the effect of depth without
animation:
[b] Unfortunately:
The major problem is that if rotation stops, the 3-D effect
disappears. This is unfortunate because it is helpful to stop
rotation to get one’s bearings with respect to the axes; the
continuous movement can make it quite difficult to get these
bearings. [Becker R.A., Cleveland W.S. & Weil G. 1988
p.252]
[c] A spinning object can be off-putting:
One of the most effective depth cues is achieved by providing
the observer with an animation sequence of parallel
projections. However, the usefulness of this method is limited
since the biologist can extract significant information by
carefully examining a well-shaded still image rather than
Stereo vision enhances the three-dimensional effect of the
rotating cloud but, even more importantly, the threedimensional effect remains even when the motion stops. This
is important for reasons that will be given shortly. Because
our visual systems also use perspective to see depth, we can
enhance point cloud rotation by having the sizes or intensities
of the plotting symbols obey the rules for perspective. Another
way to enhance the three-dimensional effect is to enclose the
cloud in a rectangular box whose edges are the axes of the
three variables; the box provides perspective, which enhances
the depth effect, and also helps us perceive the axis directions.
[Becker R.A., Cleveland W.S. & Wilks A.R. 1988 p.30]
[e] However, it is doubtful how useful stereo vision really
is:
From the test results it can be learned that for the combined
Spatial Map Images the response time is significantly shorter
for the stereo maps compared with the mono maps. However
the quality of the answers to the ‘stereo-questions’ does not
differ significantly from the ‘mono-questions’. Viewing a
Spatial Map Image in stereo means a faster, but not
necessarily better, understanding of the map. [Kraak M.J.
1989 p.112]
Chapter 7: On the Surface
130
plane102. To visualize true three-dimensional complexity we would
have to be able to unravel a ball of wool in our mind, to see all facets
and aspects of an object at once, to understand how features would
intersect from all around, above and below, and to grasp instantly
what would result from the rotation of any element in any direction or
pair of directions. Surfaces do not show us three dimensions; they just
persuade us to begin to imagine them. Then only one half of
visualization is what we see, the other is what we think.
A major advantage claimed of surfaces is that once one variable is
projected as height, other related variables can be shown, say, as
surface colour, contours, or whatever. This method certainly has its
merits. It allows two spatial distributions to be compared before using
colour and it dramatically highlights the differences and distinctions
(Cornwell B. & Robinson A.H. 1966, Jenks G.F. & Brown D.A. 1966, Mohamed B. 1986,
Kraak M.J. 1989, McLaren R.A. & Kennie T.J.M. 1989, Thiemann R. 1989, Kluijtmans P. &
Collin C. 1991).
However, in projecting one distribution as shading upon another as
height, information is lost and confused. It is lost because it cannot be
seen, and it is lost as our ability to see and compare difference in
(illusory) height is not as good as it is in estimating shades of
intensity. It is confused because colour and shadow are created from
102 [a] Surfaces show 2D elevation, not 3D structure:
The definition of three dimensional mapping has been
incorrectly preempted in many cases, by the advertising of socalled 3-D computer programs and video displays that are
nothing more than 2-D representations of perspective or
similar type projections. [Hardy R.L. 1988]
[b] The real third dimension provides very much more than
an extension of the second:
Applying 2-dimensional tools to 3-dimensional problems has
been only moderately successful at best. As the new 3dimensional geoprocessing tools get into the hands of the
users, answers will be discovered to the questions that we
currently don’t understand or even realize we can ask. [Smith
D.R. & Paradis A.R. 1989 p.153-154]
[c] It is important to differentiate between data, variables,
dimensions and objects of interest:
Data are information sources for mapping but not the objects
to be conceived and communicated. Moreover, we need to
study more carefully the relationship between types of data
and the spatial dimensions of the phenomena the data
describe. [Hsu M.L. 1979 p.121]
Chapter 7: On the Surface
131
the projection used, and as the shading of the second variable creates
the illusion of changes in height of the first103.
Surface shading is not a good substitute for two-colour mapping. The
idea of showing the relationship between four spatial distributions by
colouring a surface with a trivariate map of colour could only work if
the underlying surface were very simple. Where one variable is of
dramatic importance and has a relatively simple spatial structure, it
can be useful.
A simple surface of, for instance, unemployment (Prints CXXXVII),
can be coloured by levels of voting for various parties. Major (net)
migration streams could be draped over this, as people, perhaps, flow
down and around the mountains of discontent? To create the idea of
an industrial landscape this type of depiction can be very useful. But,
used like this, it is closer to illustration than visualization —
something to present, rather than study.
7.5 Surface Geometry
There is value in using surfaces beyond their illustrative purposes and
natural appeal. A surface contains much more information than the
mere height measurement, which is normally extracted from it and
103 [a] A traditional means of showing surface elevation is
through contours or isarithms, but:
Isarithms Do Not Permit Us
-to carry out overall quantitative comparisons;
-to represent a component QS, that is absolute quantities
calculated for variable areas (the densities must be calculated);
-to represent a sparse sample, that is, information involving
unknowns whose numerical value cannot be inferred from the
known points. [Bertin J. 1983 p.385]
thematic maps as a subset of the latter. The object of such
maps is not only to inform but also to serve as a pictorial
representation of some written work. In this respect their most
desirable qualities are the ease with which their contents can
be visualised and remembered. It is only in the display role
that block diagrams or three-dimensional views of surfaces
can become serious alternative methods of mapping. [Worth
C. 1978 p.86]
[b] It is claimed that some perspective views are only
useful for illustration:
[c] A surface showing hospital utilization in America
illustrates some of the problems caused by assuming smooth
continuity:
Traditional methods of representing relief such as hachures,
contours, hypsometric tints or hillshading, were developed for
topographic mapping and when applied to special purpose
maps or thematic maps their effectiveness is often limited.
Taylor (1975) makes the distinction between maps as data
stores and maps as data displays. This paper deals with
That utilization is not simply a matter of physical availability
stands out with startling and unfortunate sharpness in
Cleveland. The high peaks of hospitals and of physicians is
almost literally across the street from the major Black enclave,
yet we know the utilization of Blacks to be low. [Bashshur
R.L., Shannon G.W. & Metzner C.A. 1970 p.406]
Chapter 7: On the Surface
132
used in graphics. Surfaces define distances between the objects on
them. Surfaces can contain spatial information more complex than any
flat plane, in any dimension. It is this property of surfaces, the
geometry they create, which holds most promise to visualization, and
has been least exploited.
A Euclidean plane has to obey the triangle inequality, which states
that the distance from one place to another must be less than or equal
to the distance of a route via another location. Euclidean space is thus
flat; the shortest routes in it are found by following straight lines. On a
surface, however, the straight line distance between two points may
well not be the shortest. It is often advisable to travel via another
route, round mountains, avoiding gorges and so on (Ewing G. 1974, Clark
J.W. 1977, Ewing G.O. & Wolfe R. 1977, Muller J.C. 1982, Hyman G.M. & Mayhew L.D.
1983, Mayhew L. 1986).
If we have a set of distances between points, and wish to visualize the
space those distances create, then we must form a surface on which
the shortest routes between points are given from a matrix of
distances. This matrix has to be symmetrical (the distance is equal
irrespective of direction travelled) and only the shortest possible
routes are successfully depicted. Nevertheless, in this surface we have
an invaluable visual image, which is not a mere elaboration of some
simpler information104.
104 [a] Bunge has discussed the use of surface geometry at
length:
[b] Tobler saw surface geometry as being of paramount
importance in geography:
Geographic situations involving terminals require multiple
inversions of space that cannot be mapped26. Problems of this
sort make the ordinary distance map extremely misleading.
For many purposes London is closer to New York than is
Pittsburgh, and the market area for New York includes San
Francisco before it includes Wichita. The twistings and
invertings of space necessary to represent real distance can be
recorded only in pure mathematics26.
[footnote] 26 This statement has proved to be utterly
erroneous. Waldo Tobler, in a series of brilliant papers,
especially “An Analysis of Map Projections” (unpublished
manuscript University of Washington, March 1960; later
released in his Ph.D. Thesis, “Map Transformations of
Geographic Space”, Department of Geography, University of
Washington, 1961), has revolutionized and greatly simplified
the venerable subject of map projections. [Bunge W. 1966
pp.60-61]
A basic notion is that the measuring rod of the geodesist or
surveyor is less relevant to social behavior in a spatial context
than is a scaling of distances in temporal or monetary units.
Hence, it is necessary to take into account not only the shape
of the earth, but also the realities of transportation on this
surface. Automobiles, trains, airplanes, and other media of
transport can be considered to have the effect of modifying the
distances — measured in temporal or monetary units — in a
complicated manner. Different distance relations, however,
can be interpreted as different types of geometry. A
geographically natural approach is to attempt to map this
geometry to a plane, in a manner similar to the preparation of
maps of the terrestrial sphere. The geometry with which we
must deal is rarely Euclidean, and it is, in general, not possible
to obtain completely isometric transformations. However,
maps preserving distance from one point are easily achieved,
whatever the units of measurement, and these have been
Chapter 7: On the Surface
133
Such a surface creates a two-dimensional space in three dimensions,
which cannot be arbitrarily stretched and remain valid, although it can
be rotated and internally reflected. This property could be used to
indicate if real distance were greater in one direction than another, by
deciding which way to make uphill and hence which downhill. It is
uncertain whether this could always be truly depicted and if the ratio
of the differences in direction could be shown in any reliable way.
One further detail of this approach is that the surface could be built
upon any two-dimensional, flat spatial distribution. So, when viewed
from directly overhead, a familiar geographical picture would be seen,
while bringing the orientation of the camera down would show
discrepancies from the more simple metric. The most useful
employment of the technique possible here is in the depiction of travel
time.
7.6 Travel Time Surface
Geographers have attempted to depict travel time on maps for many
years. Because they have usually limited themselves to flat twodimensional representations, this has proved to be impossible (Blome
D.A. 1963, Marchand B. 1973, Muller J.C. 1978, Carstensen L.W. 1981, Lai P.C. 1983,
Tikunov V.S. & Yudin S.A. 1987). Correct travel times from a single origin
discussed in some detail. The maps at first may appear
strange, but this is only because we have a strong bias towards
more traditional diagrams of our surroundings and we tend to
regard conventional maps as being realistic or correct. [Tobler
W.R. 1961 p.164]
[c] A time surface can be drawn over a two dimensional
population cartogram but other constructions are not possible:
In view of the results of the present chapter it is impossible to
retain all three spatial assumptions: the assumption of the
Euclidean plane, the assumption of uniform densities, and the
assumption of uniform transport facility. In particular the
refutation of Wardrop’s conjecture precludes the possibility of
constructing a flat map of a city which correctly represents
travel time. However, since Warntz’s conjecture is true we
can construct a curved surface which represents travel time.
Tobler’s transformation enables us to transform a nonuniform
distribution on the Euclidean plane. This enables us to adapt
von Thünen’s theory of agricultural production in order to
deal with a nonuniform distribution of resources. The most
serious implications follow from the refutation of Bunge’s
conjecture. Since it is impossible to retain both the assumption
of uniform densities and the assumption of uniform transport
facility even if a curved surface is adopted, we will not be able
to use transformations to apply the theories of Lösch and
Christaller to realistic environments. So we can never expect
to observe the pattern of hexagonal market areas predicted by
these theories, however much we try to distort the map. The
spatial assumptions of these theories must therefore be
relaxed. [Angel S. & Hyman G.M. 1976 p.44]
Chapter 7: On the Surface
134
can be drawn, and have been on many interesting occasions. These
linear cartograms are created by showing isolines of equal time
distance from a point and then transforming them into circles around
it. Where the travel time space is inverted, however, even depiction of
a single point may not be possible in Euclidean space. Imagine what
happens as the isolines reach round the globe.
Statistical multi-dimensional scaling has often been used to try and
find the best fitting two-dimensional representation of a set of
distances. Frequently all this achieves, geographically, is the
reconstruction of the original map with a bit of distortion — only
useful when you didn’t know the original. The essential problem is
that travel time, unless exactly equal to physical distance, cannot be
drawn on a flat plane105. Just as, over large areas of the globe,
conventional maps distort shape.
105 [a] Time surface can be defined as:
Given a velocity field on the Euclidean plane, we define a
transformation of the plane into a two-dimensional curved
surface lying in three-dimensional Euclidean space. The
surface characterized by the transformation has the property
that travel time on any path in the original Euclidean plane is
equal to the length of the image of that path on the
transformed surface. In particular, the image of the minimumtime path between two points on the plane is the geodesic
curve joining their image points on the surface. This surface
has therefore been referred to as the time surface. [Angel S. &
Hyman G.M. 1976 p.38]
[b] The idea of a landscape of accessibility is not new;
Let us suppose that after an appropriate rotation two
dimensions represent the classical longitude and latitude
forming a “basic” plane, and the third dimension, the altitude
above the plane thus defined, represents the “inaccessibility”
of a city. The higher above the basic plane, the worse a city’s
linkages with the global network. This three-euclidean space
cannot be disconnected by a line, but by a plane, which means
that a given constraint on the traffic will have differential
results according to the third coordinates. For example,
checkpoints along the road, where the police would check the
papers of the truck-driver and its cargo, would not hinder
transport on bad roads, but might have a prohibitive effect on
modern highways. This is equivalent to drawing a line on the
basic plane: it disconnects points on the plane but has no
effect on points “above” it. Conversely, the third dimension
may be conceived as representing an inverse of the volume of
investment. The links which are in or near the basic plane will
be the most costly of all. An interesting case is presented by
the American road network: it may consist of two
homogeneous two-dimensional networks (the Interstate
Highway System and other roads) which are linked in three
dimensions. [Marchand B. 1973 p.519]
[c] The problem of showing the conflicts between ordinary
roads and motorways has also been realised:
Unfortunately, the determination of optimal routes is not as
simple as presented to this point. Consider the rate (speed)
map of automobiles in an urban area. How should the rates on
a freeway be presented? In the direction of the freeway the
rates are obviously high but across the freeway they are slow.
The freeway might be a serious barrier to traffic across it if
crossovers are spaced parsimoniously. These and similar
complications make optimal route solutions difficult to solve.
Notice that with many phenomena, such as the flow of air or
water, the complication is absent. [Bunge W. 1966 p.128]
[d] Many of the obstacles claimed to prevent the creation
of linear cartograms have disappeared through technological
development:
This type of diagram has disadvantages which would confine
its use to special circumstances: /a/ The reader may find it
difficult to find places on the diagram because most points
will be displaced from their correct positions, and because the
official classification cannot be shown without influencing the
reader’s choice of route. /b/ An effective diagram cannot be
constructed by a draughtsman merely following standard
instructions: judgement and experimentation are needed. /c/
The diagram may need to be entirely redesigned if the travel
time on only one link is changed, for example by a road
improvement. /d/ It would be difficult to write a computer
programme which would enable this type of map to be drawn
by machine. [Morrison A. 1970 p.52]
[e] But there are some old challenges still to be addressed:
Perhaps our almost exclusive concern with such spacewarpers is due to the disproportionate influence of economic
geography in current theoretical work. We need a grisly
“death-miles” distance to explain human migration of a gross
planetary sort. [Bunge W. 1964 p.8]
Chapter 7: On the Surface
In travel time space, internal airlines would hang like the
lines of cable cars between the peaks of inaccessible
cities. The surface would undulate smoothly in response
to the pressure of traffic on the roads and the general
quality of the infrastructure. A main line railway would
form a ridge along which settlements cluster in the
search for access to work in the city, coupled with the
desire to sleep away
from it. Occasionally,
Airline route
an international airport
may create a hole in
Airport
this fabric, down which
travellers can speed to
distant locations.
Road links
Rail Station
The travel time
surface would show
us the economic
shape of the country.
It may also tell us how
some decisions were
made to locate
factories and why
many people live
where they do. In
some places the
surface would be
monotonous,
elsewhere it could be
a tangled mess. It
would change over
hours and years,
revealing yet another
shape to the country.
Figure 22: Travel Time Surface
135
The answer is to begin with
the simple flat geography,
and raise or lower points in
some third dimension until
the correct distances are
achieved, creating a surface.
Just as an infinite number of
area cartograms can be
created to any given
specification, so too can an
infinite number of travel
time surfaces. The actual
algorithm required must
create the simplest such
surface, containing the least
rucks or changes in vertical
direction. Thus, for any
given Euclidean space, a
unique travel time surface
can be projected above and
below it.
For Britain this would create a landscape dominated by mountainous
inner cities, with London supreme, as it takes the longest time to travel
into. The major motorways would cut great gorges through the hills of
minor roads, or more appropriately tunnels, as they could only be
accessed at specific intersections. The ease of access would be made
clear, constructed of congested city centres and the great trunk roads
and railways. If internal airlines were included for passenger transport,
they might appear as tightropes connecting the city mountain tops
together (Figure 22).
Chapter 7: On the Surface
136
Real space need not be the basis for such projections, however. It only
tells of the difference between physical distance and travel time. If a
population cartogram were used, the cities would flatten and the land
in between rise up. The picture would not be nearly as mountainous as
before, as distance in population space is much closer to travel time.
Motorways would form a river system into which all other roads
flowed, the more minor being the headwaters at the highest points on
the surface.
What is more, upon such a surface it would be possible to drape, and
see information about, the population between which the roads flow.
A multi-coloured mosaic of places could be seen rising up in the areas
of inaccessiblity, spread evenly over the well connected plains, where
the roads were many and the vehicles relatively few. To help us
understand what the industrial structure of Britain has created in terms
of spatial accessibility, and thus in turn what creates industrial
structure, such images would be most valuable.
7.7 Surface Value
This chapter has shown how surfaces can be created and rendered in
visualization to depict far more than a series of two-dimensional
heights. Just as a one-dimensional graph shows slope, direction, and
distance as well as vertical value, a two-dimensional surface can show
a multitude of aspects, an entire network of local distances106.
Much more than mere travel time or fuel cost can be shown. Any
pertinent variable which can be transformed into a matrix of distances
106 [a] Breaking our thinking out of the plane is an issue
of growing importance;
Even though we navigate daily through a perceptual world of
three spatial dimensions and reason occasionally about still
higher dimensional arenas with mathematical and statistical
ease, the world portrayed by our information displays is
caught up in the two-dimensional poverty of end-less flatlands
of paper and video screen. Escaping this flatland is the major
task of envisioning information — for all the interesting
worlds (imaginary, human, physical, biological) we seek to
understand are inevitably and happily multivariate worlds. Not
flatlands. [Tufte E.R. 1988 p.62]
Chapter 7: On the Surface
137
or dissimilarities can be projected as a surface and used as a base for
further visualization work. The inverse propensity to commute
between wards could be used to show where the divides were
strongest, the connections greatest. Social cliffs would appear as real
divides, creating exposed plateaus and sheltered valleys.
It has to be remembered that these surfaces can only show the shortest
distances between localities. The idea could not be used to show the
spatial divisions which long distance migration creates and destroys.
What is more, to be successfully interpreted, the surfaces must be
relatively simple in form, particularly if they are to be the base upon
which further information is drawn.
When the geometry of a surface is not being used, a great deal of
compressed visual information is being wasted, or worse still, is
misleading the viewer. There are enough valid reasons for using
surfaces, without having to use them as a substitute for more simple
and effective graphical solutions.
138
Chapter 8: The Wood and the Trees
The world is complex, dynamic, multidimensional; the paper is static, flat.
How are we to represent the rich visual world of experience and
measurement on mere flatland?
[Tufte E.R. 1990 p.9]
8.1 Sculptured Characters
So far in this dissertation we have only considered the simultaneous
visual representations of a handful of variables — three or four at
once, at most. We are often presented with situations in which far
more aspects are available, from the census for instance. What is
more, we know that there are strong but subtle relationships lying
among all these numbers. One aim of visualization is to take
understanding beyond simple numerical relationships — the idea that
when one variable goes up another always goes down. The ideal
situation in which to do this is multivariate analysis, where the
connections are known to be complex and are usually hardly
understood. How can visualization illuminate the situation?
The position of an object in the visual plane exhausts our first and
most valuable two dimensions. The colour of an object can capture
three variables. After employing position and colour we are left with
control over the size, shape and orientation of the objects which
Chapter 8: The Wood and the Trees
139
Statistics have often had to be reallocated among areal
units in this dissertation. Where the destination level was
a super-set of the source level this was a simple
amalgamation, but where the boundaries of the two did
not coincide the problem was somewhat more difficult:
The formulae used to estimate the value of a statistic (v)
from one set of units (i) to another (j) relies upon there
being available a second variable (p) known to be related
to the prevalence of the first variable. The value of the
second variable must be known for every areal unit
created from the intersection of the two sets of
boundaries (pij). The formulae is then, put simply:
n
p
ij
vj =
p
i =1
vi
i
represent our cases or
places107. An almost infinite
number of subtle alterations
could be made to these
aspects of the visual
representation. It is not the
number of variables which
can be crammed into its
features, but the number of
variables which can be
visually appreciated and
interpreted in its context
(Figure 23), that limits and
forms
our
multivariate
visualization methods (Evans
I.S. 1983b, La Breque M. 1989).
Figure 23: Areal Interpolation
107 [a] Position and colour are the most effective visual
tools:
One way to see high dimensional structures is to try to invent
pictures that show as many dimensions at a time as possible.
One of the simplest ways to add dimensions to a picture is
through color. We start with a three-dimensional scatter plot.
We can add a fourth variable to the picture by giving each
point in the scatter plot a color that depends on the value of a
fourth variable. With an appropriately chosen color spectrum,
we can easily see simple or gross dependence of the fourth
variable on position in the three-dimensional space. Our
ability to perceive distinctions in color does not compare to
our ability to perceive position in space; we should expect to
miss subtle or complicated relationships between a color
variable and three position variables. Color works best for a
variable that takes on only a small number of discrete values.
[Friedman J.H., McDonald J.A. & Stuetzle W. 1988 p.126]
[b] Researchers have often found great difficulty in trying
to visualize beyond two dimensions:
Eventually EXPLOR4 will be pushed into representing five
interval or ratio variables. Ray length is suitable for showing
one more dimension so a viable 5-D symbol is available. We
have little hope of finding a viable symbol for 6-D data. We
have looked at 6-D plots of PDE solution set velocity vectors.
The base of each vector was located in 3-D space. The relative
coordinates of the tip of each 3-D vector represented three
additional variables. This stereo ray did not work well because
the depth angle is poorly represented by small stereo
separation differences and the angle complicates interpretation
of ray length. For the near term the task of interpreting 4-D
graphics provides sufficient challenge. [Carr D.B. &
Nicholson W.L. 1988 p.328]
[c] Some say we can only comprehend four variables
simultaneously, some say five:
At best we may be able to achieve perhaps five dimensions of
display using a two-dimensional display plus color. Perhaps
stereo displays might achieve six dimensions and animation
(time) could in some applications present a seventh
dimension. How can we display data values representing
points in a ten-dimensional data space? What kinds of display
techniques demonstrate patterns in such a way that a scientist
can perceive those patterns? [Bergeron R.D. and Grinstein
G.G. 1989 p.393]
[d] Others claim as many as nine or more variables can be
understood:
Ellison’s solution: an artist’s “sleight of hand”. Donna Cox
created an innovative technique that clearly displayed a record
nine distinct variables simultaneously changing in an
animated videotape. To pack variables to such a density, Cox
invented a unique 3-D wedge shape, the glyph (from
hieroglyphics, the Egyptian pictographs), to represent each
computed portion of the flowing plastic. The shape, color (the
blue side of the spectrum for pressure and the red for
temperature), and orientation of the wedge indicate the state of
the flowing material at particular points. The finished
videotape shows the plastic (in the form of an army of small
wedges) marching into the mould, swivelling, changing
direction and color, and eventually settling and hardening in a
series of complex steps. [Anderson G.C. 1989 p.17]
Chapter 8: The Wood and the Trees
140
These visual objects are often referred to as glyphs, meaning
sculptured characters or symbols. In this chapter I begin with the
simplest and move through to some of the more complex, though not
less successful, representations. We learn what it is that makes glyphs
work as visual representations, and how, when and why they fail108.
8.2 Circles, Pies and Rings
The basic shape I have used up to now for spatial objects, when they
were large enough to have shape, has been the simplest — the circle
(or hexagon). This has been because I did not want the shape to
distract attention from the overall impression of the image. Rotation
has no effect on the circle, but it can reflect one variable through its
size. This has been used most effectively in this work to represent the
total population of a place. Circles have also been used here to show
the discrete states at two points in time across many places (Prints
CXXXVIII & CXXXIX). Could the circle be subdivided to show the
relative sizes of different sections of that population?
Pie charts may well be the first possibility to spring to mind. These
appear ideal; a dozen sub-groups could be shown simultaneously. The
circle could be cut into male and female slices, these then each
divided into the proportions working and not working, further
subdivided into full-time and part-time workers and so on. There are,
108 [a] Conventional glyphs may not be enough:
Often it is desirable to utilize maps showing age-sex
characteristics of tracts, either as a tool to help the planner
visualize possibilities or to communicate alternatives to
interested groups. Two types of maps are typically utilized to
display such information: (1) choropleth maps for individual
age-sex groups (eg: males 65 years or older) or (2) multiple
population pyramids superimposed upon a base map. The
choropleth map suffers by requiring as many maps as age-sex
groups, making intergroup comparisons difficult. The use of
pyramids permits a single map, but is visually so complex that
comprehension of spatial patterns is difficult. [Lycan R. 1980
p.172]
[b] Complex relationships need to be shown through such
symbols:
This type of presentation makes it easy to grasp the interacting
relationships between age and race. For example, there are
tracts in which most of the children are nonwhite but a
majority of the elderly are white. [Applied Urbanetics INC
1971 p.4]
[c] Various possibilities were experimented with in early
grid square mapping:
Other experiments include shading the centre of a square to
indicate the denominator where the size of the square
indicates the ratio. [Rhind D. 1975 p.12]
Chapter 8: The Wood and the Trees
141
however, many problems associated with this. We are not particularly
good at comparing angles, especially when they are presented to us at
differing orientations, or at gauging the slight differences in the area
of the slices. Worse still, when we are presented with more than a
couple of these symbols, we quickly become visually perplexed. We
see a multitude of individually complicated parts, which we cannot
comprehend as a whole.
The basic requirements of glyphs is that not only should they each
form an acceptable single entity individually, but that when viewed
together, they should melt into a gestalt collection so that overall
patterns in the multivariate information can be discerned109. A group
of leaves can combine into differently shaped trees, and groups of
trees create different looking woods. We must be able to see the
woods, not just the trees, from the pictures of the leaves.
This explains why many initially promising ideas often fail in
practice. One method envisaged for depicting the changing spatial
distribution of unemployment was to draw a series of rings inside the
circle representing each place110. There would be one ring for each of
twelve years, like the old bark of a tree trunk, and the rings would be
109 [a] Individually well designed glyphs may fail to
combine into a single overall image:
110 [a] The distribution of employment is renowned for its
spacetime complexity:
The dimensions of the trees and castles also lack perceptual
integrality (Garner 1974). They do not provide their observer
a single image or concept or gestalt that he or she can process
and remember, binding together the values of all the
coordinates of the point. For example, polygons and faces
tend to provide observers with such a concept, while glyphs
and bar charts tend to look simply like the accretion of their
several elements. Trees and castles appear to fall in the latter
category. [R.J.K. Jacob, in Kleiner B. & Hartigan J.A. 1981
p.271]
Apart from the difficulties of reconciling information for
different areal frameworks, one of the inevitable consequences
of adopting a nation-wide perspective is the need to rely on
statistically aggregated information. Nowhere is the limitation
of this approach clearer than in the study of employment
change, where net changes need to be decomposed into their
various components — the birth, death, migration, expansion
or contraction of manufacturing or service establishments.
[Goddard J.B. & Champion A.G. 1983 p.xvii]
[b] Glyphs must be simple to produce a gestalt impression:
Except for extremely simple forms (3), the superimpostion of
several images destroys each of them. We must use a more
elementary level of reading, which excludes perception of the
overall form of each characteristic and activates the memory.
[Bertin J. 1981 p.182]
[b] There are many well researched interconnected
relationships operating through the employment
characteristics of an area:
In this broad context, some of the stability of unemployment
characteristics becomes more easy to understand. The
dominant trend of increasing female participation in the labour
force is one that is likely to find only a weak reflection in
unemployment statistics as a result of the generally low level
of female registration for benefit. [Frost M. & Spence N.
1981 p.70]
Chapter 8: The Wood and the Trees
142
coloured increasingly darker, the more people there were out of work.
It did not work (Print CXL). The image that appeared held little
meaning, even when only a few dozen circles were employed. This
was because it was not possible to compare across space any more, as
it had been cut up by circles of time. Each circle was an accurate
individual record of unemployment in that place, but the places could
not be seen as a group.
One of the most popular forms of glyph in the current use is the
polygon, or its inverse structure, the weather vane. This is formed by
representing each case as a point and projecting spokes from it at
regular intervals, their lengths in proportion to the value of each
variable being shown. If the tops of the spokes are connected an
irregular polygon is drawn, containing aspects of size, shape and
orientation.
This symbol works well when the direction in which the spokes point
has some meaning, for instance when showing wind speed in certain
directions, or the numbers going that way to work. The polygon can
produce ambiguous images, however, as two different sets of numbers
create the same object. As the number of variables increases, the
glyph quickly becomes a formless blob. Perhaps the basic problem
with all of these methods is that they are not producing naturally
comprehensible images. What is needed are collections of objects we
are used to seeing as a group, and already have the skill of assessing
as a group.
8.3 Bars and Pyramids
If we were trying to show the multivariate information about a single
place in isolation, we would probably not draw circles, we would use
charts (Prints CXLI & CXLII).
Chapter 8: The Wood and the Trees
143
The simplest chart is made up of bars, one bar for each variable, its
height in proportion to the value of that variable. Thus we could show,
for instance, the numbers of people employed in eight types of
industry simultaneously. If we divided the bars we could also try to
show the proportions of male and female, full and part time111. We
would obviously be limited in the number of places that could be
compared, as the number of aspects we chose to include increased.
One basic problem with glyphs is that to have shape requires size.
There may well even be a predictable maximum amount of
information than can fit on a piece of paper. Thus the number of
places shown declines in line with the number of variables added.
The problem with the bar chart is that the order in which variables are
placed along the bar greatly influences the visual impression given,
and the order is arbitrary. If the order of the industries, say, were made
the same as their national ranking, then charts where a gradual rise
was broken would show areas where the industrial mix was at odds
with what would be naively expected (Frost M.E. & Spence N.A. 1981, Begg I.
& Moore B. 1987).
The bar chart is taken to one more level of detail when population
pyramids are constructed. These are simply two charts placed back to
111 [a] Subdivision by industry, gender and status is
essential to understanding employment geography:
A more detailed appreciation of sub-regional employment
trends requires a disaggregation into primary, manufacturing
and service sectors in order to establish in general terms the
industrial nature of the total relative employment changes
which have already been discussed. From this analysis two
dominant and more or less ubiquitous effects on employment
structure can be identified. A consistent relative decline of
male employment in primary activities is matched against an
equally consistent relative increase in female service
employment. Between these two extremes are found the more
varied performances of male and female manufacturing
together with male service employment. Female primary
employment is generally too small to be of much interest.
[Spence N.A. & Frost M.E. 1983 p.90]
[b] Migration patterns also strongly influence the
relationships:
In the London boroughs the dominance of net out-migration
tends to produce different relationships between the
components of labour-force change. In boroughs with
increasing or stable economically active populations (which
are all suburban boroughs), the pattern is generally that net
out-migration offsets large increases in female economic
activity. [Congdon P. & Champion A. 1989 p.188]
[c] Different places can exhibit very different employment
statistics:
Job losses in London over the period since 1966 have been
shared more or less proportionately by men and women. This
is in sharp contrast to the national trend, which showed an
increase in women’s employment (at least until 1979) as
against a substantial decline in jobs for men. [Buck N.,
Gordon I., Young K., Ermish J. & Mills L. 1986 p.73]
Chapter 8: The Wood and the Trees
144
back and standing vertically,
usually used to depict the
detailed age/sex structure of
The reflected pyramid is a collection of bar charts
showing four closely related distributions. Here, they are
of eight industries sub- an area. What is most
Proportions of divided by the
Full
important
about
these
Employees
proportions of male
Time
in each
and female, full-time
Industry.
symbols (as with glyphs) is
and part-time workers
Proportions of in each place. The
that
they
create
a
1987
Part
Residents in
Time
area
of
the
symbol
is
each type of
recognisable shape. It is the
proportional to the
Employment
The National Distribution
number of employees.
Male
Female
The height of the bars outline of the pyramid that is
gives the share of workers in each industry, the width
important, and this is often
shows how they are spread among the different
categories of employment.
simple enough to compare
A similar use of height and width is used with the trees
places
across
space,
showing house price
Detached or
Bungalow?
particularly if differences
structure. Here lengths are
£50,824 Yes No
average price and width is
Central
£34,565
1983 Heating?
are exaggerated. Finally the
number of sales in each
Yes
No
£19,172
sector, giving total revenue
£24,750 Bathrooms >= 2
Detached or
pyramid can be reflected
as area. Now, however, the Bungalow
£68,686
£36,884
combined statistics of subBedrooms < 4
Bedrooms >= 4
again, horizontally, to show
markets are shown in
£28,839 national mean
All Sales in the Year
branches lower down the
four related distributions as
tree, the trunk giving the
total sales, average price and revenue for the whole
a cross (Figure 24), which
market. The angle at which the branches divide has not
has been done for some of
been used here, but could be employed to present yet
more information.
the illustrations shown here
Figure 24: Trees and Pyramids
(Prints CXLIII & CXLIV).
Two unusual glyphs were designed specifically for this
dissertation. While appearing very different they share a
number of common traits.
Other Service Industries
Public Administration and Defence
Transport/Communication,Banking,Finance
Distribution,Hotels/Catering,Repairs
Construction
Manufactoring Industries
Energy and Water Supply
Agriculture, Forestry and Fishing
However, the fundamental difficulty remains. Bar charts, graphs and
pyramids were originally designed to stand alone, and thus contain
enough complexity and detail as single entities. Glyphs, to be used in
a spatial context, must generalize and simplify the information if the
overall patterns are to be understood, particularly if more than a few
dozen areas are to be compared112. As the number of areas grows
112 [a] The supply of information on employment has
been particularly poor recently:
Between 1971 and 1978 the Census of Employment was held
annually and thus became known as the Annual Census of
Employment (ACE). In the early 1970s, processing of the data
was carried out clerically (which proved costly) but, by the
1977 and 1978 Censuses, computerised processing was
underway. However, this was insufficiently planned and led to
the delay of the 1977 and 1978 Census results. As a result of
these delays and in an attempt to find economies following the
Rayner Report, the 1979 and 1980 Employment Censuses
were cancelled (England, 1985). Since 1978, the Census of
Employment has been carried out only once every three years,
in 1981, 1984 and 1987. [McKee C. 1989 p.9]
[b] Unemployment is indelibly linked to industrial
structure:
Thus, the sub-regional results have raised a number of
interesting questions. These centre upon the role of industrial
structure in determining sensitivity levels, the apparent lack of
change in regional sensitivities and the apparent stability of
Chapter 8: The Wood and the Trees
145
bigger so to do the differences. The industrial structure becomes less
predictable and the population structure more varied. Unfortunately
our symbols get smaller and comparison becomes more difficult. We
must design simple glyphs which do not require a lot of space, and
which the eye can quickly comprehend, without excessive
examination.
8.4 Flocks of Arrows
A glyph which can satisfy the above criteria is the arrow. This is the
simplest sign expressing mainly orientation, although size and shape
can also be incorporated. Its simplicity allows trajectories at many
hundreds of places to be shown. Most importantly at this level, the
aggregate begins to express a form of its own — the sum of its parts,
and what imagination and intuition adds to that. Like a flock of birds
in flight, a group of arrows pointing in a similar direction appears to
be going that way; they become a visual group. This is exactly the
impression I am trying to create, and it is through an analogy with a
natural image that I am able to do this.
Arrows have been used in many ways in this dissertation. The
direction of the arrow can represent the levels of two variables as a
vector. Here they have been used to show the three-party swing in
the ‘system of unemployment’ with few overall shifts in
relative rates of unemployment between areas. To this list may
be added the differences that exist between male and female
patterns and, in particular, the poor female performance of the
West Midlands. [Frost M.E. & Spence N.A. 1983 p.257]
[c] The role of London is of crucial importance to the
developing geography of industrial structure:
For what we must remember above all about service activities
are that they are growing; that although they are increasingly
dispersed within regions, their growth is increasingly
concentrated in areas within about 100 miles of London but
excluding London itself; and that in this respect especially,
and in the close relationship of their distribution to functional
areas, their behaviour is unlike that of manufacturing.
[Marquand J. 1983 p.134]
[d] An apparently favourable industrial structure will not
necessarily improve levels of unemployment:
The persistent decline in London’s employment over the past
twenty-five years or so has occurred despite an industrial
structure which has been consistently biased towards activities
in which there has been expanding employment nationally.
[Buck N., Gordon I., Young K., Ermish J. & Mills L.
1986 p.66]
Chapter 8: The Wood and the Trees
146
constituencies between general elections113. The arrow can point in
the direction that a dot representing the constituency would move on
the electoral triangle. The length of the arrow can be used to show
another variable — the size of the swing. The size of the arrow is in
proportion to the electorate, and its colour shows the proportions of
the vote going to the three major parties (Print CXLV). The position of
the arrow is dictated by the constituency cartogram, which could be
animated to show changes over time (Prescott J.R.V. 1959, Miller W.L. 1977,
1990, Johnston R.J. 1981, Crewe I. 1988, Johnston R.J. & Pattie C.J. 1988b, Cochrane A. &
Anderson J. (eds) 1989, Galbraith J.W. & Rae N.C. 1989).
In one sense, nine dimensions were being seen in this relatively
simple picture — two for position, two for direction, three for colour
and one for each of length and size; but that would be a gross
exaggeration. The position of the constituency is shown by two
dimensions, while the image is representing seven very closely knit
variables. It is the strength of the relationships between the variables
that allows so much to be depicted. Ten elections worth of results are
shown on an A4 page containing over six thousand visible arrows
(Print CXLVI).
The arrows worked well in this example because direction was
meaningful, and the two variables which made up direction were
really one, just as the latitude of a place means little without knowing
its longitude. They also worked well because the spatial relationships
113 [a] The same electoral swing does not necessarily
imply the same political behaviour in different constituencies:
In fact a uniform swing could only come about if a party’s
voters behaved differently, not the same, according to the
constituency in which they lived: a uniform 5% swing from
Labour to Conservative logically requires Labour voters to
defect at higher rates in hopeless seats than safe seats.That this
tended to happen reflected the ‘partisan neighbourhood’
effect: [Crewe I. 1988 p.5]
[b] The geographical pattern to political swings is not
simple:
Using entropy-maximising estimates of the flow-of-the-vote
matrix for each constituency in the 1979-83 and 1983-7 interelectoral periods, this paper explores the extent of that
polarization. It indicates clear geographical variations that are
more complex than the simple north-south and urban-rural
dichotomies often applied. [Johnston R.J. & Pattie C.J. 1988
abstract, p.179]
[c] Localities have become more politically polarized in
recent years:
Overall, despite the decline in the class alignment among
individuals, social groups within the British electorate have
not become more politically homogeneous. Parliamentary
constituencies have never been more politically polarized and,
in consequence, the number of marginal constituencies held
by small majorities has halved since the 1960s (Curtice and
Steed, 1988, p.354). [Miller W.L. 1990 p.49]
Chapter 8: The Wood and the Trees
147
in voting were strong enough for discernible patterns to exist. If I had
wished to look at the effect of changing employment, migration,
housing and industrial influences upon the elections visually, these
simple arrows would not have been so useful.
8.5 Trees and Castles
More complex glyphs than arrows have been specifically designed to
allow quick comparison of the overall pattern of multivariate
information. The most accepted of these usually take the form of trees
or castles, where various aspects of a basic shape are altered to
produce many variations of an underlying structure which aids their
comparison. It is the maintenance of this basic structure which easily
assimilates into a picture, that distinguishes these glyphs from the
polygons, bars and pyramids described earlier. They have specifically
been designed as glyphs.
Castles have various parapets, which alter in height and aspect as the
values of the variables change. In many ways they are simply an
embellishment of the bar chart, altered so as to allow the mind to form
an impression of the general shape of the place more easily, using a
more familiar symbol. Bar charts can only go up, or down, have a
peak here or there, but they are still charts. Castles appear more as
single objects, and so it is hoped that an overall image can be
obtained. A more familiar alternative of houses could be employed,
where the shape of the roof, size of the windows and so on would be
altered to show information. So a town of houses would be created,
allowing particular suburbs, estates, and streets to be identified. This
method might be especially appropriate if it were aspects of housing
amenity at different places in which we were interested. House prices
for broad categories of housing have been shown in this work using
Chapter 8: The Wood and the Trees
148
the branches of trees to show the shape as well as local buoyancy of
the owner occupied market114 (see Appendix D).
Just as castles grew out of bar charts, trees have grown out of weather
vanes. Rather than order the spokes as a wheel, they become the
branches of a tree. This works because we are used to seeing trees
which vary in their shape but have a rough symmetry about them,
whereas all wheels are round. The order in which the variables are
assigned to the trunk, branches and twigs is crucial for the impression
gained. What is usually done is to place the most important variable at
the base, and so on. Whether this works or not depends on the
information being depicted. A relatively convincing wood can be
created. Again, thickets, copses and spinneys of different species can
be identified. Overall tendencies for trees to have a certain
combinations of features in certain parts of the picture, and for other
combinations never to occur can also be noticed (Print CXLVII).
The idea of using the two-dimensional position of the glyphs to show
information has often been mentioned, but, because of technical
problems, in particular inability to create cartograms, is rarely used.
Glyphs really come into use when the order in which they are drawn
on the page has meaning, as well as the order of variables within their
own structure115. At this point it really becomes possible to see the
114 [a] House price differentials show clear regional
patterns:
[b] It is interesting that places with extreme (high and low)
house prices (Appendix D), also shair the extreme positions in
analysing their census data:
At the end of the 1980s, such a claim would be almost
unbelievable. Instead, numerous newspapers and television
programmes, politicians and market researchers announce that
the country is divided between a poor north and a rich south.
The evidence to support — or, more rarely, contest — this
finding is provided by government statistics on employment
and income, building society figures on house-price trends,
investigative reports comparing living conditions in different
towns and increasing polarization in the electoral support of
the main political parties. A north-south divide is now
presented as one of the distinctive characteristics of Britain in
the 1980s. It is thought to be a feature that affects the lives of
ordinary people, as well as the fortunes of politicians. It raises
vital questions about efficiency and equity in the country
today. [Lewis J. & Townsend A. 1989 p.xi]
There were six clusters with fewer than five districts including
two in which single districts are so distinctive that they each
form a cluster on their own. These are the City of Glasgow
and the London borough of Kensington and Chelsea. [Webber
R. & Craig J. 1978 p.13]
115 [a] Early on in the development of glyphs it was
realised that position could be used to advantage:
Both the glyphs and the triangles can raise the dimensionality
by two by locating the center on a point in two-dimensional
space. [Chernoff H. 1973 p.365]
[b] Later, others independently made the same suggestion:
Nevertheless, because we can now plot high-dimensional data
on a two-dimensional surface we should not squander the two
Chapter 8: The Wood and the Trees
149
wood through the trees through the leaves. Here I show the housing
situation in Britain (Prints CXLVIII & CXLIX), through individual
areas’ markets and the changing prices of different types of houses
therein (Storrie M.C. 1968, Kleiner B. & Hartigan J.A. 1981, Hamnett C. & Randolph B.
1983, Champion A.G. & Brunsdon C. 1988, Brunsdon C., Coombes M., Munro M. & Symon
P. 1991).
8.6 Crowds of Faces
The most contentious glyphs created to date are based on human
faces, drawn by Herman Chernoff. Faces, it is argued, are the visual
image we are best equipped and experienced to decipher. We naturally
combine their features to interpret moods — such as happy or sad, sly
or stupid. What is more, we can easily compare faces to look for
family resemblances or the mood of the crowd116. Faces maintain a
basic structure in which even slight variation often holds meaning.
The original Chernoff faces aimed to show the values of as many as
eighteen variables simultaneously (Jacob R.J.K., Egeth H.E. & Bevin W. 1976,
Chernoff H. 1973, 1978, Chernoff H. & Rizvi M.H. 1975, Wang P.C.C. (ed.) 1978, Webber
R. & Craig J. 1978, Flury B. & Riedwyl H. 1981, Rahu M. 1989). Here I am being
dimensions of the page. As earlier displays have shown, their
use can provide a very evocative image. Thus planting the
trees into a Cartesian forest with specified axes may be a
useful notion. [H. Wainer in Kleiner B. & Hartigan J.A. 1981
p.275]
[c] The use of faces on a cartogram has been proposed
before:
As noted by Johnston engineers prefer line graphs, sales
people bar charts, demographers pie charts and medical
personnel lists of numbers. Epidemiologists, at least those
dealing with cancers seem to appreciate horizontal bars. In
cancer statistics and epidemiology the discrepancy between
sophisticated statistical methodology and elementary
graphical techniques is large. Certainly, elegant technical
refinements can be found in cancer mapping, but even here
there is exciting potential for maximizing the information
content of maps by combining cancer frequency levels with,
eg indices of data quality. Moreover, no objections exist to
combining cartograms and faces. [Rahu M. 1989 p.765]
116 [a] Research has found that even slight changes in
expression are perceived:
This latter finding suggests that extreme caricature like faces
are not crucial in obtaining good performance [Jacob R.J.K.,
Egeth H.E. & Bevin W. 1976 pp.193-193]
[b] Aesthetics are, as always, important:
Undoubtedly, the faces give a more attractive gestalt
impression than the other symbols; people like to look at
them. [Kleiner B. & Hartigan J.A. 1981 p.261]
Chapter 8: The Wood and the Trees
The face glyphs used in this dissertation are a modern,
and somewhat less ambitious development of those
originally created by Chernoff (1973). Here, only five
variables are shown, and the faces are made to look
somewhat more life-like through the use of curves, rather
than lines, to describe them. The faces are each
described by a single path made up of eleven Bezier
curves, each consisting of two control points and an
absolute point (which the curve must lie on). Three
curves are used to describe the shape of the face, and
two each for the eyes, nose and mouth. Their positions
are shown, shaded in grey, behind the faces opposite.
The minimum, maximum
and average extent of
each curve is shown. The
absolute points remain
Lowest
fixed, ensuring the
Levels
general character of the
shape does not alter too
much, and that features
will not overlap. The faces
used here are
symmetrical, as that
produced the most
pleasing results. After
Average Levels
shape, eye size, nose
size and smile, the overall
size of the face allows up
to five variables to be
Highest presented at once, in a
Levels novel manner.
Figure 25: Constructing Face Glyphs
150
somewhat less ambitious
(Figure 25, Print CL).
Chernoff faces have been
used to study general
election voting patterns in
Britain in relation to just five
variables — the electorate
(head size), house price (thin
face for low, fat for high),
employment
(smile),
election turnout (nose size)
and industrial structure
(large low eyes for younger
industries). The colour of the
face can then represent the
actual voting patterns, when
the faces are arranged as a
group on the constituency
cartogram (Print CLI).
This is intended to be a tongue-in-cheek extension of Chernoff faces
to the level of crowds. Nevertheless the inner-city/outer-city and
north/south divides in many aspects, as well as voting, can clearly be
seen (Print CLII). The difficulty of drawing precise lines between the
regions and around cities is clear. What is more, specific outliers can
be identified, which do not fit in (just as before with arrows that did
not go with the flow). Variables which appear to be unrelated to the
rest of the picture can be identified. Complex three or four way
interactions, where certain levels of some variables apparently
combine to produce a particular effect, can also be identified.
The use of the population cartogram developed here, as the spatial
base for these faces, has particular advantages. None of the faces
Chapter 8: The Wood and the Trees
151
overlap to obscure each other, and they are all, by their size, in
proportion to their importance based on the numbers of people they
represent. It may well be that glyphs have not been used in spatial
images before, because of the very problems of spatial congestion,
which the creation of cartograms overcomes. It would be difficult, for
instance, to use them in place of the circles which overlap and cluster
on the electoral triangle, although that may produce an interesting
picture to compare with the crowd on the cartogram.
Strong local relations in space are perhaps the clearest message
formed by the images. Sharp divisions are also immediately apparent,
as are more gradual changes. The faces can also be used to show that
the changes over time in the variables might be contributory to the
changes over time in voting117. Thus the expressions become places’
reactions to a changing situation, their colours perhaps indicating
some of the electoral results of those changes (Print CLIII).
Chernoff faces are contentious for the very reasons they are seen as so
useful. People’s reactions to faces are much stronger than to more
neutral objects, which are claimed to depict the information more
objectively. We have moved along a continuum, from personal likes
and dislikes of certain colours in maps, to individuals’ reactions to
cartoon faces. Visualization, at a higher level, is all about engaging
our imaginations and emotions.
117 [a] The dependancy of voting on other measures of
change is a widely held, but infrequently substantiated,
hypothesis:
[b] Faces can provide an alternative to the use of aggregate
indices in studying multivariate spatial change:
As yet, this remains a hypothesis. What it suggests is that the
changing electoral geography of Great Britain is linked to the
changing economic and social geography because people in
the relatively prosperous areas are more likely to vote for the
incumbent government than those who live in areas where
revival has yet to come (if it ever does). Thus although the
1983 and 1987 campaigns were largely waged at the national
scale, via the mass media, substantial proportions of the
electorate apparently interpreted the messages not in the
national context but in the context of circumstances in their
constituency and geographical region. The Conservative party
argued that it was producing a new, prosperous, disciplined
country, where enterprise flourished. For those whose local
circumstances confirmed that message, there was a greater
propensity to vote for that party than was the case for those
whose local circumstances indicated that if the government
was restoring prosperity, it was doing it elsewhere. [Johnston
R. & Pattie C.J. 1989 p.104]
The over-riding impression of the changes taking place in
local economies since 1981 is clearly of the division between
north and south. The map evidence (Figure 3.4) shows how
few are the places south of the Severn-Lincolnshire line with a
Change Index score below the median, though it also reveals
that more buoyant local labour-market conditions extend
across this boundary in the English Midlands and into parts of
central and north Wales. [Champion T. & Green A. 1989
p.84]
[c] The north/south divide in attitudes and variables such
as housing price is clear at certain scales:
However, until Britain moves decisively towards a more-equal
society again, its inequalities will continue to express
themselves as a north-south divide. [Lewis J. & Townsend A.
1989 p.19]
Chapter 8: The Wood and the Trees
152
8.7 Information Overload
A serious criticism of the use of glyphs is that they can overload the
viewer with information. Too much is being asked of the eyes and the
mind118. In this chapter I have shown that badly designed symbols are
impossible to decipher spatially. I have also shown that well thought
out images can help the viewer form higher level structures out of the
simple pictures of collections of places. A most efficient way to
achieve this is to use arrows, but these require direction to have some
meaning, and can show only a few other related features.
The creation of crowds of faces is certainly the most ambitious use of
symbols. This may well be the first time they have been used in this
way — pictures of people’s faces on paper, to show information about
people in places. Whether these glyphs work spectacularly or not at
all, one thing is for certain: they get people’s attention and make them
118 [a] More and more information is being loaded into
spatial displays:
The observation was made that maps portraying more than
one aspect (variable) of a phenomenon are being published in
increasing numbers and that the comprehension and
understanding of these maps is likely related to some basic
structural characteristics of the maps. [White R.D. 1984 p.45]
[b] Research has produced some rough rules to deal with
the problem:
Furthermore, every test has shown that visual reading leads to
a heavy loss of information. In order to limit this and obtain
maps with efficient messages, the following rules must be
acknowledged:
- the first glance of a reader takes in the map as a whole and
records the shapes and the figures (Rimbert, 1968);
- groups are unconsciously set up in a hierarchy around which
a fuller reading is organized (Salichtchev, 1983);
- as attention span is limited, the reader’s mental efforts
should be spared (Rimbert, 1968, Zipf, 1949);
- the perception of separate elements is done mainly through
the surface differences: size variations in point symbols are
feebly perceived when those variations are small, as was
widely demonstrated by Dobson (1983);
- past a certain level, too complex a picture leads to
diminution of received information. [Cauvin C., Schneider C.
& Cherrier G. 1989 p.97]
[c] We are, however, used to seeing and understanding
complex situations:
Under natural conditions, vision has to cope with more than
one or two objects at a time. More often than not, the visual
field is overcrowded and does not submit to an integrated
organization of the whole. In a typical life situation, a person
concentrates on some selected areas and items or on some
overall features while the structure of the remainder is sketchy
and loose. Under such circumstances, shape perception
operates partially. [Arnheim R. 1970 p.35]
[d] The more effective the technique — the more
information can be shown:
Secondly, the ability of humans to analyze effectively spatial
distributions is alleged to deteriorate progressively as the
number of variables increases, inter-relationships among
variables becomes subtle, and the magnitude of variations
decreases. This suggests that cartographic presentation must
demand as little mental computation and conceptualization as
possible if the full potential of creative intuition and decision
making is to be realized. If the cartographer can develop more
effective data reduction techniques, and the map reader can be
taught to understand their underlying concept (i.e., readily
decode them), then the amount of information communicated
by a single map might be greatly increased. [Muehrcke P.
1972 pp.19-25]
Chapter 8: The Wood and the Trees
153
think. The use of symbols which bear some relation to the subject
being studied is an asset. How better to show differences in the sizes
and quality of houses than by a collage of images of those houses?
How better to show factory closure and growth than with pictures of
industries being born and dying?
It is a mistake to think that these symbols can add another dimension
to the two we have on paper. Glyphs show multivariate structure, not
multi-dimensional form. We can look at a lot of categorical aspects of
many places in space simultaneously. We cannot see how some
feature varies with, say, place and varying wealth, multi-party voting,
or disease in spacetime. Varying the features of an object is not a good
substitute for varying its position. Features of an object have no
geometry and thus a limited ability to take only a few values. To get a
real extra dimension, beyond the first two, we must begin to think in
terms of volume, and the final chapter of this dissertation.
154
Chapter 9: Volume Visualization
It certainly feels like time is passing; I’d be foolish to argue otherwise. But I
want to show you that this feeling is a sort of illusion. Change is unreal.
Nothing is happening. The feeling that time is passing is just that: a feeling
that goes with being a certain sort of spacetime pattern.
[Rucker R. 1984 p.140]
9.1 The Third Dimension
A dimension in this dissertation is something which can be both
measured and moved around in, allowing the existence of a geometry
— the relative arrangements of objects in space. Thus, real world time
is not strictly a dimension for us, as we cannot move around in it.
Although we live in a three-dimensional world our vision waters it
down to two dimensions. We build our cities and homes along twodimensional lines, and usually only think with two-dimensional
concepts.
Time can be viewed as a third dimension119 in the social world when
phenomena beyond the simple single lives of individuals are being
119 [a] The introduction of time as a third dimension
renders many of our conventional techniques obsolete:
Once time becomes a dimension within which activities can
be viewed, the map, because of its static cross-sectional view
of phenomena, loses its usefulness. [Holly B.P. 1978 p.12]
[b] And we can only just begin to grasp the complexity of
four dimensions:
If a fourth spatial dimension cannot be visualized, it is
probably because geometry is concerned with relations that
can use perceptual and physical space as a convenient image
up to the third dimension, but no further. Beyond that limit,
geometrical calculations — just as any other multidimensional
calculations, such as factor analysis in psychology — must be
content with fragmentary visualization, if any. This also
means probably putting up with pieces of understanding rather
than obtaining a true grasp of the whole. [Arnheim R.
1970 p.292]
[c] How can we begin to take our thinking beyond two
dimensions?:
Since it has been proven that the traditional geographic map
cannot hold the solution to our space straightening problem,
what will? It seems to me that the mapping will have to be on
the surface of some object in hyper-space.25 [footnote]25 I
have been struck with this notion, unable to advance for four
or five years. Also Tobler does not warm to it so I do not trust
it, but can offer no alternative. [Bunge W. 1966 p.272]
[d] Too much concentration on temporal change may lead
us to forget the underlying two-dimensional structure:
Practically useful though this selective attention to change is,
it also has its drawbacks. It makes it difficult to become aware
of the constant factors operative in life. This weakness shows
up when the thinker or scientist needs to consider agents lying
beyond those that display observable change. In physical as
well as in psychological or social matters, the constant aspects
of a situation are most easily overlooked, hardest to be
understood. The characteristics of perception not only help
wisdom, they also restrict it. [Arnheim R. 1970 p.21]
Chapter 9: Volume Visualization
155
considered. A social order of opportunities, jobs, customs and culture
exists and moves in time and space. A disease is a spacetime entity,
and its social repercussions can only be understood when it is seen as
such, knowing when, as well as where, it strikes. Other economic or
political relationships often include a third dimension when two are
too limited to contain their full implications. Of course, we could
imagine working with even higher dimensions. You may be surprised
how difficult even trivial three-dimensional structures are to grasp.
Four dimensions is well nigh impossible, and two is almost always
preferable (Hardy R.L. 1988, Bjorklund A. & Gustavsson N. 1987, Thompson J.M. 1988,
Ke Y. & Panduranga E.S. 1990, Hart J.C., Kauffman L.H. & Sandin D.J. 1990).
When does a variable become a dimension? That is essentially a
question of the resolution of measurement. If place is just one of a
dozen regions of this nation it is a variable which should be put in
tables, not mapped. Once a variable has numerous possible values it
can be considered a dimension. Movement and measurement along the
variable must be possible, and the three-dimensional space created
should be theoretically continuous. Time in the study of the last ten
general elections is too discrete to consider approximately continuous,
and interpolation of votes between the elections would be
meaningless.
If we have a third dimension, how can we see it, let alone understand
it? This is the problem which is responsible for relegating this chapter
to the end of the dissertation. Basically, the answer is — not easily.
Do not hope to understand the picture unless it is very simple. The
traditional way to see in such blocks is to show some two-dimensional
slices, as we might cut open a human brain in a medical scan. It is a
small step from there to take many slices, allowing animation. To
create more of an illusion of three dimensions, perspective and various
lighting effects can be employed. These too can be animated.
Chapter 9: Volume Visualization
156
All we are really showing with traditional three-dimensional graphics
is a series of surfaces — two and a bit dimensional, but a long way
from three — often containing almost one-dimensional information
(Print CLIV). Recently, several innovations have been made in
computer visualization which can create far better illusions of more
complex three-dimensional worlds. The problem is then no longer
deceiving the eyes, but teaching the mind120.
9.2 Spaces, Times and Places
Places exist temporarily as well as spatially (Morrill R.L. 1970, Parkes D.N. &
Thrift N. 1975, Carlstein T., Parkes D. & Thrift N. 1978, Holly B.P. 1978, Cebrian de Miguel
J.A. 1983, Rucker R. 1984, Monmonier M.S. 1990). Over the years people move
home; over the decades new homes are built and old ones decay; over
the centuries towns are formed and decline (Print CLV). An animation
of the national boundaries of the European continent over the last four
hundred years would show near continuous turmoil. Nations exist only
as pockets in space and time, as also, in the long run, does the world
120 [a] We are well equipped for visualization, but still
often find it difficult:
It is estimated that fifty percent of the brain’s neurons are
involved in vision. 3D displays light up more neurons and
thus involve a larger portion of our brains in solving a
problem. [Dreil van J.N. 1989 p.2]
[b] Animation is almost always required to gauge depth
correctly:
Although it is not obvious why it should be, small, rapidly
repeated, changes in the viewing transformation are seen as
continuous motion of a rigid object — the point cloud. We
automatically see the three-dimensional shape of the point
cloud, using the unconscious human ability to perceive shape
from motion [Marr, 1982]. [McDonald J.A. 1988 p.184]
[c] Other techniques are less helpful:
Stereo pairs are not very convenient for showing results to
large groups of people at conferences. So we made movies
with the wells and points rotating about a vertical axis. The
human visual system can easily see three dimensions with
rotating points in space. In fact, the movie was just as
effective as stereo pairs in showing the 3D patterns among the
points. [Prueitt M.L. 1987 p.5]
[d] Showing a third dimension as depth through motion
might also be more effective than the alternatives of using
colour or glyphs:
We now recognize the great value of the dynamic aspects of
the display, especially easily recognizable rotation. Two
aspects, horizontal and vertical, are always before us. We now
have a strong feeling that the third aspect which supports these
two best is this dynamic aspect of rotation, more useful than
stereoscopy, color, flicker, or distinctive characters.
[Fisherkeller M.A., Friedman J.H. & Tukey J.W. 1988 p.108]
[e] Interactive graphics allow us to retain and greatly
expand upon the advantages of physical models:
A commonly overlooked but important advantage of physical
models is that no vantage point is assumed by the mapmaker.
The viewer has the option to determine the vantage point
thought to be best suited for the purpose at hand. More
importantly, perhaps, the physical model can be viewed from
successive vantage points to gain some notion of the extent to
which the landscape configuration is distorted in any single
view. This vantage point flexibility eliminates most problems
of a geometrical nature that are normally associated with
reading fixed-view maps. [Muehrcke P. 1981 pp.21-22]
Chapter 9: Volume Visualization
157
system of nation states. Regions coalesce, fragment and disappear.
The plate tectonics of human geography is a violent spectacle. Even
the patterns of spatial inequality can alter in the space of a few dozen
years. We can never understand why something is, if we do not look
at how it came to be, and what it is becoming121.
The theme of unemployment has run right through this work.
Unemployment as a national phenomenon has a well defined
spacetime geography. Monthly records have been kept by the eight
hundred and fifty-two amalgamated office areas for every month since
1978 (and for every ward since 1983). Over one hundred and fifty
temporal observations constitute a dimension in the above sense. How
then to view this information?
Many attempts were made to show the structure, some have been
mentioned in the previous chapter. The structure was just too
121 [a] It is only now technically possible to draw easily
the third dimension (by computer):
[d] We simplify the study of complex societies by placing
them within their dimensions of geography and history:
The limits on what can be done are, as usual, the vision of the
user. With continuing improvements in processor speed,
display quality and software techniques, the presentation of
information in visually arresting forms will become faster,
easier and cheaper. To take full advantage of these capabilities
3D representations are essential. [Kluijtmans P. & Collin C.
1991 p.550]
Added to this is the interplay between space and time.
Consider that space too is multidimensional and the tapestry
we have the privilege to study unfolds in front of us, a tapestry
in constant flux as society packs and repacks time-space and is
itself influenced by such changes. But the complexity is
sufficiently awesome that we must of necessity start with well
ordered deductive statements about the physical environment
and build up from the basics rather than, like much of
economics or sociology, plunging into the middle of the ndimensional pool and trying to swim back to the edge. We
have to realize that man lives in many-dimensional time and
space and is himself multidimensional — until we realize this
we will continue to be trapped in the x, y, z and t. And yet
paradoxically we may not escape the trap until we are fully
aware of the constraints and limits the x, y, z and t impose on
action in time-space. [Carlstein T., Parkes D. & Thrift N. 1978
p.4]
[b] Often we do not have enough information to move out
of the plane:
Visualization techniques have released the world from its
traditional two dimensional approaches to display and in so
doing, have highlighted the three dimensional deficiencies in
our sources of data in terms of availability and accuracy.
Indeed it is the lack of data that is currently inhibiting the
wider application of many of these techniques. [McLaren R.A.
1989 p.13]
[c] Recently the value of a three-dimensional perspective
has been realised in other areas of geography:
It is by positioning our geography between space and time,
and by seeing ourselves as active participants in the historical
geography of space and time, that we can, I believe, recover
some clear sense of purpose for ourselves, define an arena of
serious intellectual debate and inquiry and thereby make
major contributions, intellectually and politically, in a deeply
troubled world. [Harvey D. 1990 p.433]
[e] Appreciation of spacetime requires us to take an
unfamiliar vantage point:
My world is, in the last analysis, the sum total of my
sensations. Sensations can be most naturally arranged as a
pattern in four-dimensional spacetime. My life is a sort of
four-dimensional worm embedded in a block universe. To
complain that my lifeworm is only (let us say) seventy-two
years long is perhaps foolish as it would be to complain that
my body is only six feet long. Eternity is right outside of
spacetime. Eternity is right now. [Rucker R. 1984 p.136]
Chapter 9: Volume Visualization
158
complicated for a few views into a spacetime block to uncover (Prints
CLVI & CLVII), so a series of time-slices were drawn, one for each
year since the series began. To highlight the changes, deviations from
the expected value were drawn, knowing the average for the year and
the place. If this had not been done, the changes over these twelve
years would not have been visually apparent. Similar problems were
encountered when trying to show house price change in a single image
(Print CLVIII).
The twelve cartograms were created using both counties and
amalgamated office areas to show how spatial resolution changed the
image (see Prints XCI & XC). One picture was drawn a year, partly
because that is all that would fit on the paper, and partly because
unemployment is known to vary seasonally. The images show
dramatic changes in the social structure of Britain. Initially there was
high unemployment only in an expanded celtic fringe, but gradually
the picture changed until by 1990 unemployment was highest in the
north and inner cities, leaving a ring of almost full employment
around Outer London. Between those dates, at the height of the early
1980s recession, places like Liverpool were seen to do relatively well
as their position improved in relation to other areas, though it became
worse in absolute terms.
Work such as this requires very large matrices of data, over a million
rates for the ward time series (which, as a consequence, is not
visualized here). It would perhaps be better if the fate of individuals
were better known, rather than these giant matrices of aggregate
counts of people’s fortunes, but while that is all we have, we must use
it. A second problem concerns the use of deviation from the expected,
to highlight change. This makes the images seen dependent on the first
and last years chosen to bound the study period. It might be better
practice to show the changes between individual years, which would
be insensitive to which time span were chosen. Finally the twelve
Chapter 9: Volume Visualization
159
slices only begin to capture the inside-out goblet shaped structure of
spacetime regional inequality in Britain. To see it as it is, we need
software which can sophisticatedly render a projection of a great many
observations in space — or, a lot of imagination122.
9.3 Spacetime Continuum
In some cases, usually when the information is sparse, we do have a
near complete record of individual cases. For instance a list of firms
opening and closing, and the number of their employees, could be
created to try and understand unemployment change. The situation
where such information is most plentiful is with the medical records
of rare diseases. The incidences of when these are detected are
available to the level of the address of the sufferer and the day of
diagnosis. Such records are relatively few, allowing more
sophisticated techniques to be attempted than could be used with the
voluminous unemployment data (Print CLIX).
Childhood leukaemia cases in the North of England over the last
twenty years number several hundred incidents, with a high level of
accuracy involved in their recording123. We can think of these cases as
122 [a] Animation provides a qualitative change of
dimension:
If, while maintaining the reference axis invariable, we film a
collection of minimally dissimilar graphs, each one of which
represents a moment in time of the distribution of a
characteristic in the spatial sample, and if we present them for
viewing at the rate of 24 images per second, the result will be
the continuous movement of volume if, as in the present case,
the type of representation selected is one of visualized block
diagrams in isometric perspective. The successive
configurations of this volume manifest, with a qualitatively
different expressive force, the basic outline of a particular
process of space-time evolution [Cebrian de Miguel J.A. 1983
p.478]
[b] We can create a spacetime block from the frames of an
animation;
Try to imagine a picture like figure 137 that encompasses the
entire space and time of Flatland. This vast tangle of worms
and threads would make up what we call the Flatland block
universe. You could think of making a model of the Flatland
block universe by standing above Flatland and filming the
action as the polygons move around. If you then cut apart the
film’s frames and stacked them up in temporal order, you’d
have a good model of part of the Flatland block universe.
[Rucker R. 1984 p.137-138]
123 [a] That cancer incidence location may be important
has even been noted by professors of the theory of art:
Also any sensible enquiry limits beforehand the sort of
property to look for. The cancer specialist may not spend time
on finding out with which letter of the alphabet the names of
his subjects start but he may conceivably be interested in
where they were born. [Arnheim R. 1970 p.163]
[b] Childhood leukaemia is one of the most evenly spread
of all diseases:
The most notable feature of these maps are the complete lack
of any discernible geographic pattern and the similarity of the
rates in each of the four cities. While Aberdeen is known to
have a background level of naturally occurring radiation, this
is not reflected in an increased leukaemia risk. [World Health
Organisation 1985 p.186]
Chapter 9: Volume Visualization
160
points, sitting geometrically
in a block of two decades of
Northern English spacetime
(Print CLX). If we were to
render these cases as simple
1, 1, 1
32 16 32
Below is shown a perspective
points, then, because of their
1, 1, 1
view of the bivariate normal
64 32 64
kernel which could be used to
sparsity, we may well not
smooth a point distribution. A
1, 1, 1
pick up any slight increase
32 16 32
trivariate function was used to
1 , 1, 1
or decrease in the density of
16 8 16
1, 1, 1
cases or some more subtle
32 16 32
spatial
and
temporal
1, 1, 1
64 32 64
arrangement (Levison M.E. &
1, 1, 1
32 16 32
1, 1, 1
Haddon W. 1965, Hunter J.M. &
64 32 64
Young J.C. 1971, Dean A.G. 1976,
Howe G.M. 1979a, 1979b, Slocum
smooth the cancer cases in this dissertation. The width
and sharpness of the kernel is more important than the
T.A. 1983, Selvin S., Shaw G.,
actual shape of the function chosen, in its effect on the
final image. These parameters correspond to the number Schulman J. & Merrill D.W. 1987,
of passes of binomial smoothing undertaken. Often it is
useful to explore the effects of a range of parameters, if
Cliff A.D. & Haggett P. 1988,
possible, as the image changes before your eyes.
Schulman J., Selvin S. & Merrill
Figure 26: Three-dimensional
D.W. 1988, Selvin S., Merrill D.W.,
Smoothing
Schulman J., Sacks S., Bedell L. &
Wong L. 1988). To aid visual
interpretations the points are represented by spheres (Print CLXI),
their influence decaying with distance (Figure 26) from their
The one- and two-dimensional binomial smoothing
discussed earlier can easily be extended to work in three
dimensions. The three matrices shown here give the
smoothing factors around a single
voxel in three dimensions. After a
few passes application of this
1, 1, 1
filter approximates to the trivariate
64 32 64
normal distribution.
{ }
{ }
{ }
[c] Cancers are, in general, not particularly obviously
clustered:
The pattern differs somewhat according to cause of death. For
some specific causes, the north west — south east divide is
clear (heart diseases, stomach cancer, bronchitis) but other
causes (e.g. neoplasms) are more variable in pattern. [Curtis S.
& Mohan J. 1989 p.177]
[d] The reason for visualizing disease is to lead others to
investigate unusual patterns:
While the geographical representation of cancer on maps has
been recognised as useful in describing the ‘cancer scenery’ of
a particular country (Frentzel-Beyme et al., 1979), the real
purpose lies in identifying geographical areas or hypotheses
that require more detailed epidemiological study. [World
Health Organisation 1985 p.41]
[e] Before we begin looking at leukaemia, we can assume,
from past research, that we may not find any discernible
patterns:
The limited amount of geographic variation for certain cancers
may also provide insights into etiology. Leukaemia rates were
nearly constant across the country, similar to the minor
international differences that have been reported. This
suggests that the role of environmental exposures may be less
important or conspicuous than for other cancers. [Melvyn
Howe in Blot W.J. & Fraumeni J.F. 1982 p.190]
Chapter 9: Volume Visualization
161
incidence in both space and time124. This process can also create a
truly three-dimensional surface, a single value existing at every point
in time and space being related to the general prevalence of the
disease about that time and around that place.
To see this space we could again resort to time-slices, in this case
taken from an animation showing the development with one frame for
every month in the period. But, with only a few hundred cases more
elaborate software can be employed where the spheres are actually
placed in an abstract three-dimensional space and a camera flown
around it, recording the views of specific interest (Prints CLXII to
CLXVI). Again these are shown here as individual frames, but no
longer time-slices, as they were taken at an angle through the block,
and show an image looking into it, obscured only when cases are
eclipsed by one another. As can be seen in the illustrations, the cases
124 [a] The need to smooth the distribution of incidents, to
see structure in this particular data set, was identified many
years ago:
If space and time are considered jointly as a three-dimensional
block of space-time with co-ordinates of time, latitude, and
longitude, and if incidence (occurrences related to the
population at risk) is represented within the volume of the
block, it follows that there must be some unevenness. [Knox
G. 1964 pp.20-21]
The linking of time and space units is readily illustrated by the
many infectious diseases such as influenza which arrive in one
part of the globe from another and are then passed on after an
interval of time to further areas, supporting the remark of
statistician, Stephan (1934), that ‘data of geographic units are
tied together like bunches of grapes, not separate like balls in
an urn.’ [Cliff A.D. & Haggett P. 1988 p.169]
[d] The idea of representing incidents by a cone has been
previously suggested in two dimensional geography:
Poisson probability maps were constructed for leukaemia
mortalities in the administrative counties of England and
Wales for each year from 1950 to 1966 inclusive. This was
done in the belief that much of the variation of these data on
the time-space scale of the ‘county-year’ might be ascribed to
random variations. Thus the probability maps would appear as
filtered data through which only the non-random recordings
would appear. [White R.R. 1972 pp.177-178]
It may be useful to think of total population influence in yet
another way. Each person’s influence may be represented by a
pile of sand, with the height of the pile at the place the person
occupies and decreasing away from him. Suppose there is a
similar sandpile around the place of residence of every
individual. Now let all this sand be superimposed. At any
point the total height of the sand will be the sum of the heights
of all the individual sandpiles. The total height is a measure of
total population influence at that point, and a contour map or a
physical model may be made of the entire surface. [Warntz W.
1975 p.77]
[c] A geographical rather than statistical approach is
warranted as people are not uniformly distributed:
[e] In general there is often too little reliable information to
allow this approach to be taken:
However, the separation of space and time made in these two
chapters produces but a partial understanding of the dynamics
of disease processes. Regions do not operate as isolated units
and the incidence of disease varies simultaneously in both the
spatial and the temporal domains. Indeed, it is the
interdependence of time and space which, as Gould (1970,
p.44) has noted, ‘allows us to substitute pattern, and therefore
predictability and order, for chaos and apparent lack of
independence — of things in time and space.’
Thus, if we think of space as the weft and time as the warp of
a space-time fabric, then it is evident that the threads are
broken in many places. The many countries which have never
made returns to WHO sever the weft and the missing
observations in each country over time break the warp.
Together, the resulting holes in the data matrix make the interregional and time-series comparison of morbidity and
mortality data extremely complex. [Cliff A.D. & Haggett P.
1988 p.72]
[b] Other such work has also been carried out, on a crude
scale, in the past:
Chapter 9: Volume Visualization
162
are very evenly spread in population space (Knox G. 1964, White R.R. 1972,
Chinese Academy of Medical Sciences 1981, Gardner M.J., Winter P.D., Taylor C.P. &
Acheson E.D. 1983, World Health Organisation 1985, Howe G.M. 1986b, Williams-Pickle
L., Mason T.J., Howard N., Hoover R. & Faumeni J.F. 1987, Glaser S.L. 1990).
The actual space in which we place the cases is a very important
consideration. A simple Euclidean space has only been used to show
how that image differs from one obtained when a more appropriate
two dimensional population cartogram is used. The relationship
between time and space is not simple125. Physicists use the speed of
light as a common unit in both metrics, but we are not physicists. Here
it was arbitrarily chosen to make one year equal to twenty five
kilometres or the root of three hundred thousand people. The
distribution of the childhood population at risk from leukaemia hardly
altered over the period, in relation to the slight oscillations of the
disease. But a more thorough study would have to consider carefully
the construction of a volume cartogram, in which every life was equal,
and placed as close as possible to those with which it shared life, as
well their immediate ancestors and offspring. The relationship
between time and space would depend on how far and how frequently
people tended to move in their lives.
125 [a] The relationship between time and space can be
relatively simple in pure physics:
Usually in drawing Minkowski diagrams, one adopts a system
of units so that the path of a light ray is represented by a 45
degree line. Light moves at about one billion miles per hour,
so the idea is to mark off the space axis in units of one billion
miles and mark off the time axis in units of one hour. [Rucker
R. 1984 p.151]
[b] But, even in basic physical geography the aspect of the
third dimension produces problems:
In the special case of meteorology there are some particular
issues and concerns. The small thickness of the atmosphere
(relative to its horizontal extent) necessitates a “stretched” zaxis for visualizing weather phenomena. Also, the desire to
view the distribution of several variables simultaneously has
given rise to a few interesting solutions: one is to portray each
variable by a different attribute (color for variable A, height
for variable B, iso-valued contours for variable C, etc.);
another is to assign different transparency indices to the
various surfaces that represent the variables. Both of the above
methods result in images that are highly “unrealistic”,
illustrating that there may be instances in scientific computing
in which the visualization technique may have to transcend
“realism”. Finally, the need to associate the atmospheric
phenomena to the underlying map and terrain imposes
additional display constraints that must be addressed.
[Papathomas T.V., Schiavone J.A. & Julesz B. 1988 p.329]
Chapter 9: Volume Visualization
163
9.4 Three Dimensional Graphs
Things other than space and time can be projected to occupy three
dimensions. A three dimensional graph is created by merely raising a
third axis at right angles to the conventional two, and plotting points
inside that space. It is not
The idea of the equilateral triangle can be extended into therefore what the term is
three dimensions in a tetrahedron to show the
composition of the votes of four parties, amongst a
commonly
used
to
number of constituencies. Position (x,y,z) on the triangle
describe, a one dimensional
is calculated from the Conservative (C), Labour (L),
Liberal/Alliance (A) and Nationalist (N) proportions of the
bar chart, with each bar
vote as follows:
drawn as a pillar. ThreeC + L + A + N = 1
dimensional graphs are
(C - L)
(A + N) 3
z = N 5
y =
x =
12
2
2
often used in statistical
Position in the equilateral tetrahedron formed then gives projection pursuit, where
the share of the votes in any one constituency, and the
you need all the dimensions
distribution of all constituencies, simultaneously:
To understand the
SNP 100%
distribution within the you can get to explore
three-dimensional
aspects
of
multispace it must be
rotatable by the
dimensional spaces. They
viewer. A twoLiberal/Alliance/SLD 100%
dimensional net of the are also becoming common
space can be opened
out to expose some of on microcomputers, where
the pattern on flat
Conservative 100%
paper, but a lot of the the ability to rotate, or at
dynamism of the
Labour 100%
least rock them is essential
graphic is lost. It is
hard to imagine how this device could be profitably
to maintain the threeextended to show the composition of the vote amongst
five parties. Three dimensions are hard enough to grasp. dimensional illusion. Some
Figure 27: The Electoral Tetrahedron claim that stereoscopic
displays are also an asset.
Here I have developed the peculiar electoral triangle into the logical
three-dimensional analogue of a tetrahedron which attempts to show
how the vote is shared between as many as four parties in a large
number of areas (Figure 27). We need to be able to do this if we are to
Chapter 9: Volume Visualization
164
include Scotland in our analysis of electoral composition in Britain126
(Print CLXVII). In Scotland, in recent years, the nationalist party has
consistently come in third or second place, but the third major British
party is still in the reckoning and has quite a different pattern of
support (Bochel J.M. & Denver D.T. 1984, 1986, 1988, 1990). The abstract
creation of an electoral tetrahedron is quite simple. The points,
representing the electoral divisions in which the vote is counted, are
placed so that their distance from the four apexes is in exact
proportion to the share of the four party vote represented by those
apexes. To stick to convention, looking from above, the Conservatives
have the right hand apex, Labour the left, the Centre party the top (still
on the plane), and the Nationalists the apex in the centre (now above
the other three).
The problems are familiar when we attempt to project the threedimensional structure of voting onto a two-dimensional plane for
drawing. The methods mentioned above have been used and are
shown here, taking slides from an animation, and showing slices from
particular angles. But another method has also been developed in this
case. That is to unfold a net of the tetrahedron as four equilateral
triangles (Print CLXVIII). A point is drawn on the side of the
tetrahedron it was originally closest to. As the centre was relatively
empty (due perhaps to tactical voting) this technique does not create
results that are too ambiguous. In fact each triangle contains only
those divisions where a particular one of the four parties came last.
126 [a] Scottish election results have recently become a
truly four-way affair:
Two-way contests, which were far and away the most
common in 1974, have declined pretty steadily and
significantly. In particular, straight fights between Labour and
the Conservatives, which were again the most common, are
now relatively rare. The increase in Conservative V Labour V
SNP contests is a direct function of the larger number of SNP
candidates. This also explains why, despite the fall in the
number of SLD candidates, the proportion of four-way
contests reached a high point of twenty-three per cent of
contests in 1988. [Bochel J.M. & Denver D.T. 1988 p.v]
[b] The old two party system in Britain has become three,
it could easily split further:
British electoral politics seem to be on the threshold of
moving into a new era of permanent three-party competition,
which may or may not be accompanied by a radical change in
the voting system. For this sort of shift there are no real
parallels, and even to sketch a future scenario still seems
precipitate. [Dunleavy P. 1983 p.58]
[c] Movement to the apexes of the electoral tetrahedron
would indicate that the following had occurred:
This apparent consolidation of strength in the parties’ own
territories is an interesting phenomenon; it is unclear on the
available evidence whether incumbency of itself gives an
advantage or whether parties successfully targeted their
campaign effort to exploit existing support. [Bochel J.M. &
Denver D.T. 1990 p.vi]
Chapter 9: Volume Visualization
165
The net can actually be further subdivided into areas in which the
exact order of the parties are known. Such an arrangement makes
interpretation of a complex three-dimensional situation considerably
simpler (Print CLXIX), although in explicitly using two dimensions
something has to be lost — in this case the exact fortunes of the party
coming fourth.
What do we do, though, if we wish to see how the four-way situation
changed over time? A slight change in the number of votes could send
a division spinning across the net, which in reality would hardly move
in the tetrahedral space. This would be unfortunate, unless only
changes of party position were of interest. What if there were a fifth
party also of some importance — say the green vote rose up in the
future. Could we show the two-dimensional net of the three
dimensional shadow of a four-dimensional hyper-tetrahedron? Or
would it be better to observe the rotating three-dimensional net of the
four dimensional point cloud (Prints CLXX & CLXXI)127? These
situations are avoided for the while, but remain to be addressed.
127 [a] It is easy to get lost in all these dimensions:
We need to be able to tell which three-dimensional subspace
of the euclidean data space we are looking at. We also need to
see how the point cloud is oriented in that space. To satisfy
these needs we draw, in a corner of the screen, an object
called the coordinate axes. This object was called the dreibein
(German for tripod) in previous PRIM systems [Fisherkeller,
Friedman, and Tukey, 1975] and is sometimes referred to as
the gnomon in the computer graphics literature [Foley and van
Dam, 1982]. [McDonald J.A. 1988 p.185]
[b] How reliable are our visual and mental abilities when
dealing with this complexity?:
The use of such a system poses interesting theoretical
questions: Is exploring data by looking at projections “safe”
— if you look at enough different projections of structureless
data, will you find structure by chance? If it is safe, is it
“effective”? — in what sense can the information in a ddimensional point cloud be extracted from a few of its 3dimensional projections? The method, properly applied,
appears to be both safe and effective, even allowing for the
fact that we do not know the statistical properties of the eye as
pattern detector. [Donoho D.L., Huber P.J., Ramos E. &
Thoma H.M. 1988 p.119]
[c] Our vision is an effective means of analysis and can be
conditioned to become even more so:
Powerful viewing capabilities present a problem of overexploring data and finding spurious structure. In our own
experience, this has rarely been a serious issue, perhaps
because human vision is a far better instrument for
distinguishing between the real and spurious in scatter plots
than commonly believed. On the other hand, we tend to use
graphical methods for screening data and obtaining rough
qualitative insights, while in-depth analysis and subtle
quantitative judgements are left to more formal methods, once
their applicability is established by the screening process.
Whatever the reason for the relative reliability of visual
judgements may be, there still arise occasions when one
wishes to have tools for sharpening one’s perception of
random fluctuations in data. It has been suggested that data
analysts should gauge their eyes every once in a while on
some artificially created pseudo-random data, like
multivariate normal point clouds [Diaconis, 1983]. We have
followed this advice on occasion, and found it helpful in
establishing structure as real, and in realizing that the most
frequent types of random structure, such as local clottedness
and moderate outliers, are usually not of interest to the data
analyst. [Buja A., Asimov D., Hurley C. & McDonald J.A.
1988 p.292]
Chapter 9: Volume Visualization
166
9.5 Flows Through Time
If the possibilities at the end of the last section appeared a little
daunting, consider, for a moment, the problem of showing how a
pattern of flows has changed over a number of years, not a single
change but a complete succession. Just such a truly three dimensional
matrix has been constructed from the National Health Service central
register, for flows (in both directions) between every pair of ninetyseven mainland family practitioner committee areas for each of the
last fourteen years128. Even this low level of spatial and temporal
resolution produces over one hundred thousand separate counts of
migration streams. How can we begin to see what is happening to the
flows of people which create the spacetime pockets of existence we
call places in Britain?
The basic two-dimensional flow maps showed numerous overlapping
arrows. When the change between two years was sought, even
depicting the single differences could require two separate images.
Depiction of spacetime flows of people would have to be constructed
in three dimensions. Theoretically there would be a plane to represent
every year, which would contain the changing population cartogram.
Places would be linked by tubes, the width of which, say, was in
proportion to the number of migrants. To prevent the image becoming
too tangled in practice, a measure of significant change would have to
be found, similar to that used in the two-dimensional case. Otherwise
almost ten thousand tubes would have to connect every pair of planes.
128 [a] Migration patterns have been fairly consistent over
time, but do fluctuate:
[b] The geography of migration alters along with the
history:
In 1989, the total number of moves between FPC areas within
England and Wales, at 1.76 million, was 6 per cent less than
the 1.88 million in 1988 (Table 1). There was little variation
in the total number of moves during the years 1979 to 1985,
which ranged from 1.50 million (in 1981) to 1.60 million (in
1985). However, in 1986 the number of moves increased to
1.83 million (a 14 per cent increase over 1985), with further
increases to 1.87 million in 1987 and 1.88 million in 1988
(Figure 1). During this period, expansion of financial services,
resulting in easier access to mortgages, and relatively low
interest rates may have contributed to the increased number of
moves. Similarly, the fall in the number of moves in 1989
could have been partly due to the rise in interest rates. The
total number of moves in 1989 was still 13 per cent above the
average for the seven years before 1986. [Bulusu L.
1990 p.33]
For a while during the 1970s these counterurban tendencies
were operating so powerfully that they replaced the NorthSouth drift as a primary dimension of regional population
change in Britain (Champion, 1983). Particularly impressive
was the way in which the South East’s population began to
decline in the late 1960s, following its rapid growth in the
1950s and the early 1960s. [Champion A. G. 1989 p.122]
[c] Two dimensional thinking often limits our descriptions
of three dimensional processes to ripples or waves:
Analysis of flows suggests that population is moving further
and further from conurbation centres in the form of a ‘wave’
or ‘ripple’ process. [Spence N., Gillespie A., Goddard J.,
Kennett S., Pinch S. & Williams A. 1982 p.281]
Chapter 9: Volume Visualization
167
A complex three dimensional structure is sure to
appear extremely confusing when forced to fit
onto flat paper. These two graphics show some
experiments to
project the
spacetime
distribution of
unemployment,
and the use of
tubes to show
migration flows
across space and
time.
At least the origin and destinations
of migration would be obvious,
even if the paths between them
were, to say the least, a little
confused (Figure 28).
129 [a] With such complexity it may be better to show
only some of the structure:
cues. From experience, we have found that rendering of
opaque or semi-transparent surfaces (such as the interface
between two fluids or a contour surface) provides the best
results. In particular, when combined with an interactive
surface-peeling capability for examination of interior flow
detail, surface rendering is preferable over displays of stacked
contours or dot patterns. Alternatively the source-attenuation
method provides transparency and is relatively easily
implemented, but at the expense of strong depth cues; fluid
flows tend to look like clouds unless interfaces or other
surfaces are accentuated. ... [Hesselink L. 1988 p.474]
The structure just described has
not been created here, as it would
Eventually being
just produce a perplexing mess129
able to rotate these
images is not
(Bryanston-Cross P.J. 1988, Carr D.B. &
enough. We need
to be able to get
Nicholson W.L. 1988, Hesselink L. 1988,
inside them to
explore and
Congdon P. & Champion A. 1989, Dickinson
discover what the
structure to the
R.R. 1989, Helman J.L. & Hesselink L.
patterns may be.
For now we can only paint pictures on the outside 1990a, 1990b, Stillwell J., Boden P. & Rees P.
of what we wish to be able to see from within.
1990). To understand such a
complex structure, even after
generalization,
requires
the
development of new techniques to
look into three dimensions. A
maze of tubes crisscrossing in
spacetime will not reveal its
Figure 28: Three-dimensional
structure through the illumination
Structure
of its outside surfaces. Cross
sections through the connections would be confusing, and it is also not
possible to simplify such a complex organization to a plane and retain
its essential form.
Rather than trying to simply display the data the idea is to
extract certain topological information and to display this. As
the authors point out, a jillion little arrows displayed in a cube
would not reveal much about a three dimensional flow.
[Nielson G.M., Shriver B. & Rosenblum L.J. (eds) 1990
p.261]
[b] Ways of reducing the visual complexity are currently
under development:
To visualize complicated three-dimensional flow structures,
one requires displays with strong three-dimensional depth
Chapter 9: Volume Visualization
168
9.6 Volume Rendering
We have only really been talking about volume visualization in this
chapter, not looking at it. That is because all the facilities we, in social
science, have to date, and for a little time to come, can only show
surface views of three-dimensional structures. That is why only one
chapter of this dissertation has been devoted to the subject which is
currently the subject of most interest in computer science
visualization130 (Drebin R.A., Carpenter L. & Hanrahan P. 1988, IEE sponsored 1988,
Papathomas T.V., Schiavone J.A. & Julesz B. 1988, Bak P.R.G. & Mill A.J.B. 1989, Herr L.
1990, Hibbard W. & Santek D. 1990a, 1990b, Laszlo M.J. 1990). This is conceivably
because it is still being developed. Software is being written to look
inside the surfaces, to create images on the screen which we could not
see in a picture on paper. Volume rendering defines what can be done
with this kind of software, which can only be described in these pages.
The key theme is translucence. Surfaces can be peeled off a volume,
like two-dimensional contours, but really to see the structure you must
be able to see all the contours at once. To do that in space, objects
must emit and transmit light, so that they can be seen through, but also
not be transparent, to still be seen themselves. There is obviously a
limit to how many layers can be pierced; each obscures a little more
than the last. The combination of translucence with perspective,
130 [a] The old approach was to show three-dimensional
structures through two dimensional surfaces:
The second, newer approach to volume visualization is called
direct volume rendering, volume imaging, direct voxel
rendering, or just volume rendering. This approach maintains
an explicit connection between the volume data set and the
volume visualization. The algorithms use no intermediate
geometric representation. The resulting voxel clouds, perhaps
more visually ambiguous, permit users to explore directly the
contents of their data. The scientist can slice-and-dice the
visualization to explore arbitrary cross-sections of the original
volume data set. Viewing is not limited to surfaces, although
surfaces are sometimes portrayed. [Herr L. 1990 pp.201-202]
[b] Great claims are made for the future of interactive
computer graphics:
Interactive computer graphics is the most important means of
producing pictures since the invention of photography and
television; it has the added advantage that, with the computer,
we can make pictures not only of concrete, “real world”
objects but also of abstract, synthetic objects, such as
mathematical surfaces in 4D (see Color Plates 1.3 and 1.4),
and of data that have no inherent geometry, such as survey
results. Furthermore, we are not confined to static images.
Although static pictures are a good means of communicating
information, dynamically varying pictures are even better —
to coin a phrase, a moving picture is worth ten thousand static
ones. This is especially true for time-varying phenomena, both
real (e.g., the deflection of an aircraft wing in supersonic
flight, or the development of a human face from childhood
through old age) and abstract (e.g., growth trends, such as
nuclear energy use in the United States or population
movement from cities to suburbs and back to cities). [Foley
J.D., Dam A. van, Feiner S.K. & Hughes J.F. 1990 p.3]
[c] It be advantageous to see how the information looks
from the data’s point of view:
It is certainly feasible, and may prove useful, to offer a “biod’s
eye” view of the dataset as viewed by one of the biods, using
stereoscopic viewing and other “virtual reality” techniques as
they develop. [Kerlick G.D. 1990 p.127; "biod" is made up of
the words "bird" and "icon"]
Chapter 9: Volume Visualization
169
animation and lighting allows us almost to see inside the volume as
we move around and through it (Dutton G. 1978, Doctor L.J. & Torborg J.G. 1981,
Papathomas T.V. & Julesz B. 1988, Sabella P. 1988, Meyers R.J. & Stephenson M.B. 1990).
Imagine the economic spacetime of Britain with the unemployed areas
shown like dark storm clouds through which it is possible to see better
times ahead (in time), or to the side (in space). The whole structure
would be held in the holographic image where no pocket of prosperity
or despair could remain hidden. What would the spacetime continuum
of childhood leukaemia incidence look like seen through translucent
space? Well, for one thing no case could eclipse another. More
importantly, when two or more cases fall in almost exactly the same
time and place they will appear much darker than is usual, rather than
as a single occurrence. The three-dimensional electoral graphs would
appear more like a cloud of dust particles, or a galaxy. The true
density and sparsity of spatial divisions would be apparent, where,
before, they had quickly obscured each other as a dark mass. Lastly
there are the flows through time. It might be possible to trace the path
of each migration stream through the myriad structure of pipes and
columns.
Translucence is not true three-dimensional imaging. To argue that is
rather like telling a two-dimensional being which can only see a onedimensional strip that if objects were made translucent they would see
two dimensional structure. They would not. They would merely see
what was previously completely hidden from their view, and may,
through rotating the angle and position from which they viewed the
two dimensional space, come to guess some of its structure. However,
they would never have the full luxury of being able to see
simultaneously all that it contains and how it is arranged from above,
because they are part of that two-dimensional space; just as, in the real
world, we will never have that visual ability in three dimensions.
Chapter 9: Volume Visualization
170
9.7 Interactive Visualization
To bring the discussion up to date, it is increasingly being claimed
that there are two types of visualization — the mundane variety which
would include this work, and the interactive kind, the most extreme
example of which is found in the artificial realities of computer
graphics. Interactive visualization, like interactive graphics, allows the
viewers instantly to choose the direction and position from which they
are viewing, what they are seeing and how it is depicted, lit, animated
and so on. What you see moves, and so can you. This freedom allows
any aspect of a structure to be examined at will. It is almost as if you
could pick it up and turn it around in your hands. In some systems you
see the object stereoscopically through two images in a pair of goggles
— better still, etched on contact lenses131 (Becker R.A. & Cleveland W.S.
1988, Becker R.A., Cleveland W.S. & Wilks A.R. 1988, Becker R.A., Cleveland W.S. &
Weil G. 1988, Friedman J.H., McDonald J.A. & Stuetzle W. 1988, Stuetzle W. 1988, Kerlick
G.D. 1990, Wills G., Bradley R., Haslett J. & Unwin A. 1990, Haggerty M. 1991). Your
wishes are executed through the movements of your head, hands and
even entire body. For the majority of us, however, interactive
visualization will not arrive for several years yet.
131 [a] Rather than wear goggles containing visual
displays:
An alternative design would be to fabricate a display on a
contact lens and a sensor would detect eye movements as well
as head and body movements. This display must then generate
the image that the eye would see. Since it would only need to
illustrate the small area that the fovea would see, the
resolution of the image could be very modest. [Krueger M.W.
1983 p.100]
[b] Statistical graphics is one pioneering area in which
artificial reality is being applied:
The amazing computing power now available to data analysts
carries with it the potential for new graphical methods —
dynamic graphics — that utilize visual input and achieve
virtually instantaneous graphical change. High interaction
methods represent a new frontier in data analysis and are an
important adjunct to conventional static graphics. [Becker
R.A., Cleveland W.S. & Wilks A.R. 1988 p.47]
[c] Interactive visualization is very different from
animation:
At the extreme end, we find ourselves manipulating plots
which change so fast that they appear in motion for all
practical purposes. this is the domain of real-time graphics:
plots are recomputed and redrawn so rapidly that the visual
effect of smooth motion is achieved, and at the same time the
user is given the possibility of controlling the process at any
point in time. This contrasts with animation, where sequences
of views are precomputed, stored away, and retrieved at the
time of viewing. Motion graphics can be generated either way,
but non-trivial user control is possible only with real-time
graphics. The price we pay is that currently affordable off-theshelf equipment can handle a real-time approach only on
fairly sparse pictures, such as plots of point scatters. [Buja A.,
Asimov D., Hurley C. & McDonald J.A. 1988 p.278]
[d] Yet another revolution is being heralded:
Just as ‘visualization’ has been invented to describe the
process of providing more immediate access to very large
amounts of data, ‘interactive visualization’ will be ‘invented’
to describe the process of providing more immediate access to
the particular features that are of interest to the analyst at
particular points in both the spatial and time domains of a
given field. [Dickinson R.R. 1989 p.10]
Chapter 9: Volume Visualization
171
The basic questions of what it is we wish to see and how that should
be drawn remain as important as ever. Interactive computer graphics
will allow you to pick up the earth in your hand and view it just as if it
were a real globe — but we can already do that in the classroom with
the plastic model. What is exciting about visualization is the facility it
offers for us to transform what we wish to observe to a form most
amenable to our understanding and then change that, if it does not suit
us, at a whim.
Interactive visualization will reach the micro computer screen by first
offering the user the ability to link several displays of the same data to
gain greater insight; say a rotating tetrahedron of the Scottish voting
composition in one window coupled with an animated cartogram in
another — showing how the distribution of divisions changed
geographically over time. An area of Glasgow in the 1970s could be
selected and the points representing those divisions would light up in
the tetrahedron simultaneously. As you moved a pointer over the
changing cartogram of Scottish divisions, other points would become
lit and you could trace patterns between geographical, historical and
political spaces.
Artificial reality allows us to go one step further; to be actually inside
the tetrahedron; to look in a spacetime cartogram down at the 1986
regional election results, and up at the 1990 contest; to see the dark
clouds of unemployment rising above Glasgow to meet the fine detail
of the 1991 census in the distant sky. Below us would lie the remains
of decades of industrial structure and behind us the same for England
and Wales. All this will require considerable research and
development, but if cannot specify our aims at this stage, how can we
plan for the future? Whether these technical innovations will result in
useful still pictures for printed work (such as this) also remains to
be seen.
172
Conclusion: Another Geography
Perhaps one day high-resolution computer visualizations, which combine
slightly abstracted representations along with dynamic and animated
flatland, will lighten the laborious complexity of encodings — and yet still
capture some worthwhile part of the subtlety of the human itinerary.
[Tufte E.R. 1990 p.119]
New techniques have been presented in this dissertation which are
superior to many past practices. This is because they exploit the visual
sense we are naturally best equipped to learn from, and provide the
opportunity for general interpretations that best capture the nature of
what we are interested in. This is the opposite to black box techniques,
in which the last thing you can see is what is really going on. In
simple statistical analysis, for instance, sharp lines are often drawn
between what is significant and what is not, giving little insight into
the true nature of a complex picture132.
By placing people in their spatial context, we often find unforeseen
patterns of great interest (Print CLXXII).Visualization allows for a
more open-minded style of analysis. It may seem as if we are doing
little more than observing the world, but that world was hidden from
us in vast tables of facts and figures. The best instrument we possess
through which to acquire and assemble such information is our visual
132 [a] Statisticians are beginning to move away from
numbers towards graphics:
[b] Some are becoming increasingly earnest in their
condemnation of past practice
Even statistically nonsignificant interactions can be
informative when plotted multivariately. The corollary of this
is that graphically specifiable, precise, and useful interactive
effects can escape detection in traditional analysis of variance
methods. [Hirsh N. & Brown B.L. 1990 p.207]
Finally, solutions obtained from kernel-function and nearestneighbor techniques are essentially uninterpretable “black
boxes” compared to the more readily interpretable graphical
solutions yielded by projection pursuit techniques. [Crawford
S.L. & Fall T.C. 1990 p.107]
Conclusion: Another Geography
173
processing ability through which our imaginations can work to
construct knowledge133.
When computers were first introduced to social scientists a
contradictory position was often held. The computer would soon be
able to comprehend the world, to see it. The machines could then tell
us what was going on134. Only later did it ironically become obvious
just how complex and powerful our own human visual perception is,
and how difficult it is to get a computer to mimic vision, however
inadequately.
Computer simulation of tiny fragments of artificial life is an area of
great activity in the field of computer intelligence today, and advances
are being made. It is almost possible to emulate the actions, and more
133 [a] Early on it was realised that geographic
information systems would only answer simple questions, not
help us think about more complex problems, as maps can:
A computer bank would probably be more geared to
answering some specific question ad hoc and perhaps less to
provoking thought about what questions should be answered.
[Bickmore D.P. 1975 p.344]
[b] Pictures allow us to imagine that which numbers
cannot show:
For the human imagination, always too limited, always curbed
by socio-cultural contexts, map collections present
possibilities as vast as the data bank is large. Visual selection
is faster and better than any automatic selection, since it
permits from the outset a variety of nuances beyond the
capability of any computer. But its costs in terms of time only
pays off with “seeing maps.” “Reading maps” make the
operation impossible. [Bertin J. 1981 p.161]
[c] These discoveries are made repeatedly:
Graphic displays of large complex data sets may also reveal
relationships in the data which might not have been explored
in conventional numerical analyses, and thus serve to direct
subsequent statistical tests. There is also the possibility that
the eye may be capable of detecting spatially or temporally
distributed features of the data which could not easily be
detected by mathematical techniques. In this latter respect,
man may act as more than a filter or a monitor of conventional
data analyses; he may play an important role as a data
analyzer, per se. [Pickett R.M. & White B.W. 1966 p.76]
[d] Similarly, from another perspective:
In the face of the dissolution of the “national” it is no longer
possible for the sociological imagination to ignore the
geographical. Or as Eco put it, with an appropriate double-
entendre, “let’s give back to the spatial and the visual the
place they deserve in the history of political and social
relations” (1986, p.215). [Agnew J.A. & Duncan J.S. 1989
p.4]
134 [a] It did not take long to realise how human and
computer imaginations compared:
The human mind is still far better than the computer at
deciding which patterns exist in census data that are sensible
and which ones are absurd, although of course humans cannot
rival the computer’s ability to identify and produce plausible
patterns for evaluation. [Openshaw S. 1983 p.250]
[b] The crucial distinction is between recognition and
perception;
There is no need to stress here the immense practical
usefulness of computers. But to credit the machine with
intelligence is to defeat it in a competition it need not pretend
to enter. What, then, is the basic difference between today’s
computer and an intelligent being? It is that the computer can
be made to see but not to perceive. What matters here is not
that the computer is without consciousness but that thus far it
is incapable of the spontaneous grasp of pattern — a capacity
essential to perception and intelligence. [Arnheim R. 1970
p.73]
[c] Using faces to allow people to see patterns in data was
especially ironic:
This approach is an amusing reversal of a common one in
artificial intelligence. Instead of using machines to
discriminate between human faces by reducing them to
numbers, we discriminate between numbers by using the
machine to do the brute labor of drawing faces and leaving the
intelligence to the humans, who are still more flexible and
clever. [Chernoff H. 1973 pp.365-366]
Conclusion: Another Geography
174
importantly reactions, of a sea-slug — one of the world’s simplest
organisms — with the world’s most powerful computers, and most
able of programmers135 (Berlekamp E.R., Conway J.H. & Guy R.K. 1982, Langton
C.G. 1986, 1989, Moravec H. 1989). Even advocates who claim that life could
one day be created in a computer, do not think it will be possible for
machines to interpret complex pictures for some time. However, we
can use machines to allow people to see information, using the
different powers of the machine and the mind in unison for those
things which each is most able to do.
Writers in the past have thought of, and asked for, much of what has
actually been created here (Tobler W.R. 1959, Bunge W.W. 1973, Warntz W.W.
1973, Bunge W.W. & Bordessa R. 1975): a new look at cartography, more
productive employment of graphics, harnessing the machine’s power
and the mind’s intelligence. It is hoped that this work has gone some
way in showing how to achieve many of those wishes. The writings
and drawings of a great many authors have been consulted to try to
ensure that no substantial contributions were overlooked.
The production of new forms of, and uses for, area cartograms is
original. These took the longest time to create, and the algorithm was
not easy to develop. Its implementation was actually achieved using
computer graphic techniques (Prints CLXXIII, CLXXIV & CLXXV).
This dissertation was also the first to work visually with social
135 [a] Researchers in artificial life are becoming more
ambitious:
[c] The creation of artificial social systems is as complex
as the creation of (Virtual State Machine) life itself:
We would like to build models that are so life-like that they
cease to be models of life and become examples of life
themselves. [Langton C.G. 1986 p.147]
It demonstrates the way in which simple VSM’s can interact
with one another in complex ways, and suggests that one
might identify systems of interacting VSM’s at the level of
social systems as well as at the molecular level. [Langton C.G.
1986 p.133]
[b] Success is not impossible:
Rudy Rucker sees a still wider future for cellular automata. “I
feel that science’s greatest task in the late 20th century is to
build living machines... This is the computer scientist’s Great
Work as surely as the building of the Notre Dame cathedral...
was the Great Work of the medieval artisan.” [Dewdney A.K.
1990 p.138]
[d] But it will be a long time before we are talking to our
creations:
The figure shows that current laboratory computers are equal
in power approximately to the nervous systems of insects. It is
these machines that support essentially all research in artificial
intelligence. No wonder the results to date are so sparse! The
largest supercomputers of the mid-1980s are a match for the
one-gram brain of a mouse, but at ten million dollars or more
apiece they are reserved for serious work. [Moravec H. 1989
p.193]
Conclusion: Another Geography
175
information of such magnitude and detail, overcoming many of the
problems often said to make the handling of so much information
impossible. Much of the practical side of this work was only feasible
due to the use of very recent advances in computer hardware and
software, but also the arduous collection of large amounts of digital
information (Prints CLXXVI & CLXXVII)
More important than the practical achievements of this work is the
change in attitudes it asks for in general social science research. The
abandonment of many past practices is suggested, accepting that all
methods, the ones advocated here included, are tied to the times and
places in which they were created, and can never be universally
appropriate.
Visualization, it must be stressed, is much more than pretty
pictures136. It is a fundamental methodology for visually modelling
aspects of our world to gain a new, useful understanding. Such a
136 [a] Before automated cartography became widespread,
some questioned its motives:
And let us be aware of the natural tendency to design
computer-graphic programs to imitate traditional cartographic
conventions in the mistaken belief that because they are
traditional they are, ipso facto, legible. [Bickmore D.P. 1975
p.350]
[b] The practical achievements of visualization still surpass
any theory:
Unfortunately, technological improvements in graphics
hardware far surpass developments in understanding the
cognitive and perceptual mechanisms by which spatial
patterns are identified and interpreted. It is unrealistic to
expect that a few more bitplanes and larger palettes will
facilitate the understanding and communication of vast
quantities of data. The ability to light phosphor in new ways
will not by itself result in more effective data processing,
understanding, or communication. [Buttenfield B.P. & Ganter
J.H. 1990 p.307]
[c] Visualization provides a very different paradigm to
graphic design:
These examples reveal an unconventional design strategy: “To
clarify, add detail.” This strategy works because humans are
well-equipped to deal with masses of data. Massive structures
fill our world (we see the tree rather than count the leaves),
and the presence of micro information allows viewers to select
their own level of detail, picking out the data important to
them. This contradicts a commonly held view that data display
should be reduced to poster-like simplicity, which imposes the
designer’s view on the data and limits the usefulness of the
graphic. Ultimately, we need complex displays of data
because of the complexity of the world being modelled.
[Freeman S. 1991 pp.113-114]
[d] Visualization is choice:
We no longer must choose among accurate location
specification, problem-solving capabilities, and effective
communication. We can routinely change the representation
of our base information from on that preserves area relations,
to one in which space is scaled according to travel time, cost
or any other relevant factor. We can produce planimetrically
accurate maps together with maps using non-standard distance
metrics as a means of illustrating the importance or both
distance and cost or time (Muller 1982).
A dynamic, multilayered conception of geographic reality is
developing along with technology that lets us display it on
maps. Not only can we update changing information quickly,
but we can produce maps that display change while we view
them (Moellering 1980). In addition, our perspective can be
changed at the push of a button from the two-dimensional
planimetrically accurate one necessary for measurement to a
three dimensional one useful in developing an overall
conception. [MacEachren A.M. 1987 p.106]
[e] We are still learning to use some visual interfaces
twenty years after they were designed:
Think of these computer models and the windows provided to
the models by the graphics systems as the basic primary
representation of information. Not many people have such
systems. They are light years away, not because we don’t
know how to build them, but because we don’t know how to
use them. [Evans D. 1973 p.7]
Conclusion: Another Geography
176
practice has been going on for a very long time outside of the confines
of science — in art. It is an interesting question as to when images,
created for scientific study, will be accepted as pieces of art, as the
purposes of both worlds are merged137 (Knowlton K. 1987, Robinson A.H.
1989, Varanka D.E. 1987).
We are rapidly moving into an age that will manufacture artificial
realities, from kaleidoscopes to computer games, to fictional fractal
lands, worlds and galaxies (McCormick B.H., DeFanti T.A. & Brown M.D. (eds)
1987, Lathrop O. 1988, DeFanti T.A., Brown M.D. & McCormick B.H. 1989, Haslett J.,
Wills G. & Unwin A. 1990). The imaginative escape from reality is
accelerating138. What we do know is that these new worlds will not be
like our own in nature. We must be careful that we do not ignore the
real one.
Different goals lie beneath the surface of the visualization revolution.
At a superficial level there is the aim to extract more money from the
137 [a] When science becomes art is a moot point in
cartography:
Art is usually not intended to be easily manipulated and
reproduced. Thus map-like art almost never takes the form of
a scientific tool. Map-like art can be designed in a scientific
manner. It sometimes comments on scientific themes. It is
possible that computer map-like art exists, but I haven’t found
any.
Yet, map-like art is highly effective. Its best use is in visual
form and communication. Examples have been examined that
quickly and directly describe complex ideas. It does so by
drawing upon the high levels of visual sophistication which
have been developed by artists. Artists have applied this
ability to the representation of geographical occurrences. They
have captured certain abstract situations which technical
cartographers hesitate to approach. Map-like art will
sometimes express locations and distributions which are not
necessarily problematic. This draws attention to facets of life
that create a richer picture of our environment. [Varanka D.E.
1987 p.85]
138 [a] A great future is being promised;
With graphics, we can create artificial realities, each a
computer-based “exploratorium” for examining objects and
phenomena in a natural and intuitive way that exploits our
highly developed skills in visual-pattern recognition. [Foley
J.D., Dam A. van, Feiner S.K. & Hughes J.F. 1990 p.21]
[b] With fame and fortune for today’s protagonists:
But now virtual reality is a popular topic in computer
graphics. The change in attitude in the research community,
according to Krueger, came after April 1989, when the New
York Times published a front-page article on artificial reality.
This “legitimized” the topic and triggered a surge of interest in
virtual reality, an interest that has led to some publicity for
Krueger’s work. Life even noted that Krueger was likely to be
among the most influential people of the next century.
[Haggerty M. 1991 pp.13-14]
[c] But how much do we really know of what is to come:
[b] Even the most eminent of the "old school" are
beginning to see the light:
For maps of larger scale, an artistic objective might well
lessen our insistence on a strict geometric framework for maps
and make room for the greater use of mental constructs of
social, cultural, and economic space (Watson, 1979;
Robinson, 1979). Such maps might well be considered the
cartographic equivalent of “mild” surrealistic art. [Robinson
A.H. 1989 p.97]
Tomorrow will be quite different. As the visualization
technologies continue to mature and we delve deeper into the
creation of artificial/virtual realities. [McAbee J.L. 1991
p.715]
Conclusion: Another Geography
177
academic funding agencies of America139. This is carried out with the
threat that the Japanese will win the new economic war, and a country
already sliding down the world scale must counter attack.
There is, however, far more behind visualization than new technology
and economic power. Maps and charts, by containing information,
have always been a key to power and social control140 (Boggs S.W. 1947,
Pred A. 1986, Frenkel K.A. 1988b, Harley J.B. 1988a, 1989, 1990a). Their origins in
military conquest are replicated today by efforts in (spy satellite)
image processing and (battle ground control) geographic information
systems. Just as the clock allowed the timing of people’s lives to be
controlled, so the map permitted regulation of spatial movement and
enclosure of land. Today, these are combined in information systems
which, with visual capability, create new possibilities for technocratic
control, through determining the accepted image of the world.
Another aspect of visualization is seen though the possibilities it holds
to reveal the injustice and inequalities in the world, to show these
pictures to more than just the bureaucrats and administrators. Images
are becoming the currency of the information age. We are now used to
receiving much of our understanding through the television screen.
139 [a] The threat Japanese supremacy is well worn:
[c] It is reported that some U.S. government agencies may
also be finding visualization useful:
Laurin Herr, an analyst with Pacific Interface, New York,
cautions, “In ‘83 we saw the first wave of Japanese vendors,
but they were second tier vendors.“ Although they did not
penetrate the U.S. market then, they are a threat and Japanese
devices are already inside our workstations, he says. [Frenkel
K.A. 1988 p.113]
Other agencies are asking how they can use graphics. The
DMA’s interest is in detailed topological simulations so that
flyers can train on 3-D rather than 2-D maps. The CIA, on the
other hand, would like to incorporate graphics into database
management. [p.120] Frenkel K.A. 1988
[b] The economic future of the United States is often
closely linked to visualization in bids for funding:
140 [a] Global control and domination, or a better
understanding?:
The products of technology are all this country has to sell. If a
product can be made elsewhere, it can be made better and
cheaper. The United States can survive economically only by
exploiting and strengthening our leadership in all forms of
technology. During the past ten years our technological
superiority has dwindled. In crucial areas our edge, which
used to be measured in years, has vanished. Unless we renew
our commitment to progress, our day is over. We will have to
be content to sit on the sidelines, admiring the achievements
of others. [Krueger M.W. 1983 p.244]
The information age has yet to deal with information transfer.
Visualization technologies can help lead the way to better
global understanding and communication. [DeFanti T.A.,
Brown M.D. & McCormick B.H. 1989 p.24]
[b] The history of the use of maps for social control is well
documented:
Maps impinged invisibly on the daily lives of ordinary people.
Just as the clock, as a graphic symbol of centralised political
authority, brought ‘time discipline’ into the rhythms of
industrial workers, so too the lines on maps, dictators of a new
Conclusion: Another Geography
178
Despite publishing more, we are currently reading less. To
communicate we must compete with others’ graphics. How better than
through our own?
The social conclusions of this research are that British society is
sharply divided141 (Prints CLXXVIII & CLXXIX). The divisions are
most obvious on a fine spatial scale, where people are socially herded
agrarian topography, introduced a dimension of ‘space
discipline’. In European peasant societies, former commons
were now subdivided and allotted, with the help of maps, and
in the ‘wilderness’ of former Indian lands in North America,
boundary lines on the map were a medium of appropriation
which those unlearned in geometrical survey methods found
impossible to challenge. [Harley J.B. 1988 p.285]
[c] Geographical knowledge has been long recognised for
the power it offers:
Such education as there was did not include the dangerous
subject of geography; even in the National and British schools
of the period (to say nothing of the workhouse schools) there
was such a prejudice against the teaching of geography that in
many cases the school master was forbidden to hang any maps
on the walls of the schoolroom. [Redford A. 1976 p.96]
[d] Technical change brings little that is fundamentally
different:
Are we returning to a new Dark Ages? Will the GIS
specialists become the new priestly class, determining our
image of the world just as surely as did the makers of the
MAPPAE MUNDI? [Harley J.B. 1990 p.15]
141 [a] The geographical polarization appears at every
level:
The immediate future appears to offer more of the same.
Though some success has been achieved by job creating
agencies in the North, job loss is still running at a high rate
and a continuation of high levels of redundancies and factory
closures seems a real prospect, particularly in view of the
limited progress made in these areas towards introducing new
commercial products. Meanwhile, the South appears to be
launched on an inflationary spiral in terms of costs of land,
housing and congestion which no politically feasible degree of
relaxation in planning constraints is likely to curb and which
can only be further stimulated by major infrastructural
developments like the establishment of London’s third airport
at Stansted and the construction of the Channel Tunnel. With
southerners becoming increasing reluctant to lose their toehold in the vibrant housing market there and with northerners
facing massive housing-cost penalties for moving in the
opposite direction, the divide between the two parts of Britain
seems to be looming larger and larger and threatens to cleave
the country into two blocks which are likely to pursue
increasingly separate paths of development, thereby proving a
challenge to their political unity in the longer term.
[Champion A.G., Green A.E., Owen D.W., Ellin D.J. &
Coombes M.G. 1987 p.112-113]
[b] Within a single London borough barriers are rising:
As the gap between them grows, something fundamental
happens to insiders’ sense of their place in society. The
outsiders become to look less and less like the kind of people
insiders mix with at work and socially. They become less
recognizable as members of the same society, with a similar
right to claim a decent standard of living. At best they are an
unsettling embarrassment to be treated with charity. At worst
they are an unwanted burden.[...] [Leadbeater C. 1989 p.51]
[c] The actual pattern of divisions is unceasing but ever
changing:
This is not to deny that Britain, when it comes to prosperity, is
an increasingly divided nation. But the main split is not
geographical but social. There is no Severn-Wash line
separating the haves from the have-nots. The poor,
predominantly recruited from young, ill-educated, often black
males, unskilled over 50-year-olds and the growing army of
unmarried mothers are certainly concentrated in the old, oneindustry towns and decaying inner cities of the North. But
they represent an equally intractable and numerically even
problem in the boroughs at the heart of London. Meanwhile
the relatively affluent majority, those enjoying jobs, cars,
home-ownership, videos and regular foreign holidays, are to
be found almost everywhere. [Wilsher and Cassidy, 1987]
[Lewis J. & Townsend A. 1989 p.3]
[d] The extent of British inequalities in wealth are truly
staggering:
Noble argues that: ‘About 500 000 people, one per cent of the
population, own just over a third of all private wealth in
contemporary Britain and receive just over half of all the
personal income derived from possession of wealth’. Within
this stratum the very rich 50 000, 0.1 per cent of the
population, are the most important group. [Scott J. 1989 p.74]
[e] To appreciate such huge differences requires more than
words alone can provide:
Britain is a deeply divided society, and the deepest division of
all is the inequality in the ownership of wealth. That the
inequalities have persisted for so long helps in itself to
legitimate them, to make them more acceptable; the status quo
is an influential public relations officer for the rich. And the
very extremities of wealth inequalities somehow deprive the
statistics of credibility or meaning. [Pond C. 1989 p.189]
[f] We need to see more clearly the social structure we are
trying to alter:
It may seem that the present world is not worth knowing —
only worth changing. But to change it one must know it.
[Warntz W. 1975 p.75]
Conclusion: Another Geography
179
into, or cannot escape from, many areas for many reasons. There is
little reason to think that these divisions will not widen in the future.
There appears to be nothing likely to prevent this, particularly when
the people in more prosperous places hold the political power. If we
cannot change what is happening, we can at least show it for what it
is. For nothing will ever change while people are blind to what is
happening. Here I have shown how people can be hidden in the detail
of conventional maps.
It is perhaps the extent of the problem of inequality which prevents its
full appreciation and dissuades action. The differences in personal
wealth in Britain are truly staggering, with five per cent of the
population receiving over half the income of the nation, while the
wealth of the poorer half is negligible. Images can open up the world
and depict the extent, imbalance and order with great accuracy and in
a more emotive way (Gilbert M. 1982, Wheate R. 1985a, Leadbeater C. 1989, Pond C.
1989b, Scott J. 1989). The images of the world we are shown, are the
foundations of our understanding.
There were times when the coloured areas on a colour ink-jet map were
suddenly obscured by white summer clouds which seemed to scud in from
nowhere between the map and the author’s eyes, and among which he could
glimpse the sparkle of the sea by the coast, the rivers which rolled down to
meet it, the towns and villages and people. Sometimes old people
materialised out of the map of Norrland and observed with melancholy the
exodus of the young towards the coast and the south. From the diagrams
which display households suddenly appeared a throng of people who with
muted voices told of their lives, of their loneliness, of their joy in their
children and of their hopes on their behalf.
[Szegö J. 1984 p.30]
Bibliography
180
Bibliography
Aangeenbrug R.T., 1976, Small area data as a constraint
to computer graphic analysis, Proceedings of the Workshop
on Automated Cartography and Epidemiology, National
Center for Health Statistics, U.S. Department of Health,
pp.59-66.
Abel D.J., 1986, Bit-interleaved keys as the basis for spatial
access in a front-end spatial database management system,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.163-177.
Alessi P.J., 1986, Overview of ISCC and ASTM committee
work on video displays, Color Research and Application
(Supplement), Vol.11 p.29.
Alexander F.E., Ricketts T.J., Williams J. & Cartwright
R.A., 1991, Methods of mapping and identifying small
clusters of rare diseases with applications to geographical
epidemiology, Geographical Analysis, Vol.23, No.2,
pp.158-173.
Abel D.J. & Mark D.M., 1990, A comparative analysis of
some two-dimensional orderings, International Journal of
Geographical Information Systems, Vol.4, No.1, pp.21-31.
Algazi V.R., Reutter B.W., van Warmerdam W.L.G., Liu
C.C., 1989, Three-dimensional image analysis and display
by space-scale matching of cross sections, Journal of the
Optical Society of America A-Optics and Image Science,
Vol.6, No.6, pp.890-899.
Abler R., 1976, The comparative atlas of America's great
cities: selection of variables and cartographic techniques,
Proceedings of the Workshop on Automated Cartography
and Epidemiology, National Center for Health Statistics,
U.S. Department of Health, p.52.
Alker H.R., 1969, A typology of ecological fallacies, M.
Dogan & S. Rokkan (eds) Quantitative Ecological Analysis
in the Social Sciences, The M.I.T. Press, Massachusetts.
Abrahams A.D., 1984, Channel networks: a
geomorphological perspective, Water Resources Research,
Vol.20, No.2, pp.161-168.
Adams J.M., 1969, Mapping with a third dimension, The
Geographical Magazine, Vol.42, No.1, pp.45-49.
Agnew J.A., 1987, Place and politics: the geographical
mediation of state and society, Allen and Unwin, Boston.
Agnew J.A., 1989, The devaluation of place in social
science, J.A. Agnew & J.S. Duncan (eds) The Power of
Place: Bringing together geographical and sociological
imaginations, Chapter 2, Unwin Hyman, Boston.
Agnew J.A. & Duncan J.S., 1989, The Power of Place:
bringing together geographical and sociological
imaginations, Unwin Hyman, Boston.
Agui J.C. & Hesselink L., 1988, Flow visualization and
numerical analysis of a coflowing jet: a three-dimensional
approach, Journal of Fluid Mechanics, Vol.191, pp.19-45.
Ahn J. & Freeman H., 1983, A program for automatic
name placement, AutoCarto 6 (Proceedings of the 6th
International Symposium on Computer-Assisted
Cartography), Vol.2, pp.444-453.
Al-Taha K.K. & Barrera R., 1990, Temporal data and
GIS: an overview, GIS / LIS '90 Proceedings, Vol.1,
pp.357-372.
Albert T.M., 1988, Knowledge-Based geographic
information systems (KBGIS): new analytic and data
management tools, Mathematical Geology, Vol.20, No.8,
pp.1021-1035.
Alderson M., 1987, The use of area mortality, Population
Trends, Vol.47, pp.24-33.
Allard L. & Hodgson M.J., 1987, Interactive graphics for
mapping location-allocation solutions, The American
Cartographer, Vol.14, No.1, pp.49-60.
Allen J. & Hamnett C. (eds), 1991, Housing and labour
markets: building the connections, Unwin Hyman, London.
Allen J.P. & Turner E.J., 1988, We the people: an atlas of
america's ethnic diversity, Macmillan Publishing Company,
New York.
Allsop J.G., Johnston R.J. & Pattie C.J., 1988, A nation
dividing, Longman, U.K..
Alpern B., Carter L. & Selker T., 1990, Visualizing
computer memory architectures, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.107-113, October, San Francisco, California.
Alpha T.R. & Winter R.E., 1971, Quantitative
physiographic method of landform portrayal, Canadian
Cartographer, Vol.8, No.2, pp.126-136.
Alt J., Särlvik B. & Crewe I., 1976, Partisanship and
policy choice: issue preferences in the British electorate,
February 1974, British Journal of Political Science, Vol.6,
pp.273-290.
Amanatides J., 1987, Realism in computer graphics: a
survey, IEEE Computer Graphics and Applications, Vol.7,
No.1, pp.44-56.
American Cartographic Association, 1989a, Geographers
and cartographers urge end to popular use of rectangular
maps, The American Cartographer, Vol.16, No.3, pp.222223.
American Cartographic Association, 1989b, The case
against rectangular world maps, The Cartographic Journal,
Vol.26, pp.156-157.
181
Amrhein C.G. & Flowerdew R. , 1989, The effect of data
aggregation on a poisson regression model of Canadian
migration, M. Goodchild and S. Gopal (eds), The Accuracy
of Spatial Databases, Taylor and Francis, London.
Anderson D.P., 1982, Hidden line elimination in projected
grid surfaces, ACM Transactions on Graphics, Vol.1, No.4,
pp.274-288.
Anderson E., 1960, A semigraphical method for the
analysis of complex problems, Technometrics, Vol.2, No.3,
pp.387-391.
Bibliography
Archer J.C., 1990, Political atlas of Illinois (review),
Cartography and Geographic Information Systems, Vol.17,
No.2, pp.172-173.
Archer J.C., Shelley F.M., Taylor P.J. & White E.R.,
1988, The geography of the US presidential elections,
Scientific American, Vol.259, No.1, pp.18-25.
Aretz A.J. & Reising J.M., 1987, Color computer graphics
in military cockpits, H.J. Durrett (ed) Color and the
Computer, Ch.8, pp.151-170.
Anderson G.C., 1989, Images worth thousands of bits of
data, The Scientist, Vol.3, No.3, pp.1,16-17.
Arlinghaus S.L., Arlinghaus W.C. & Nystuen J.D., 1990,
The hedetniemi matrix sum: an algorithm for shortest path
and shortest distance, Geographical Analysis, Vol.22, No.
4, pp.351-360. Ohio State University Press.
Anderson J. & Child J., 1987, Choropleth maps: window
dressing or workhorses, GIS'87 Technical papers, ASPRSACSM Second Annual International Conference, San
Francisco, California, pp.352-361.
Arnheim R., 1970, Visual thinking, Faber and Faber Ltd,
London.
Anderson J.M., 1988, Human cartography: mapping the
world of man (review), Cartographica, Vol.25, No.4, pp.8889.
Andreottola M.A., 1987, Color hard-copy devices, H.J.
Durrett (ed) Color and the Computer, Ch.12, pp.221-240.
Andrews D.F., 1972, Plots of high-dimensional data,
Biometrics, Vol.28, pp.125-136.
Andrews D.F., Fowlkes E.B. & Tukey P.A., 1988, Some
approaches to interactive statistical graphics, W.S.
Cleveland & M.E. McGill (eds) Dynamic Graphics For
Statistics, Wandsworth, California.
Angel S. & Hyman G.M., 1972, Transformations and
geographic theory, Geographical Analysis, Vol.4, pp.350367.
Angel S. & Hyman G.M., 1976, Urban fields: a geometry
of movement for regional science, Pion Limited, London.
Anjyo K., Ochi T., Usami Y. & Kawashima Y., 1987, A
practical method of constructing surfaces in threedimensional digitized space, The Visual Computer, Vol.3,
pp.4-12.
Anscombe F.J., 1973, Graphs in statistical analysis, The
American Statistician, Vol.27, No.1, pp.17-21.
Arnheim R., 1976, The perception of maps, The American
Cartographer, Vol.3, No.1, pp.5-10.
Asada A., 1988, Contour processing and 3-D image
processing of sea beam bathymetric data, International
Hydrographic Review, Vol.65, No.1, pp.65-80.
Aspaas H.R. & Lavin S.J., 1989, Legend designs for
unclassed, bivariate, choropleth maps, The American
Cartographer, Vol.16, No.4, pp.257-268.
Atkinson D.S., 1988, An evaluation of dual-hue
progressions and spectrally-ordered sequences for bi-polar
choropleth maps, M.A., Georgia State University.
Axelsen B. & Jones M., 1987, Are all maps mental maps?,
GeoJournal, Vol.14, No.4, pp.447-464 .
Bacharch M., 1970, Biproportional matrices and inputoutput change, University Press, Cambridge.
Bachi R., 1968, Graphical rational patterns: a new
approach to graphical presentation of statistics, Israel
Universities Press, Jerusalem.
Bachi R., 1975, Graphical methods for presenting statistical
data: progress and problems, Auto-carto 2 (Proceedings of
the International Symposium on Computer-Assisted
Cartography), pp.74-90.
Anson R.W. (ed), 1988, Basic cartography for students and
technicians volume 2, Elsevier, London.
Baecker R.M., 1973, Computer animation: an aid in
visualizing complex processes, Canadian Datasystems,
March, pp.30-32.
Anstis S. et al, 1986, Computer-generated screening test for
colorblindness, Color Research and Application
(Supplement), Vol.11 pp.63-66.
Baird J., Wagner M. & Noma E., 1982, Impossible
cognitive spaces, Geographical Analysis, Vol.14, No.3,
pp.204-216.
Anthony S.J. & Corr D.G., 1988, Data structures in an
integrated geographical information system, ESA Journal,
Vol.11, No.1, pp.69-72.
Bak P.R.G. & Mill A.J.B., 1989, Three dimensional
representation in a geoscientific resource management
system for the minerals industry, J. Raper (ed), GIS: Three
Dimensional Applications in Geographic Information
Systems, Taylor & Francis, London.
Applied Urbanetics INC, 1971, Reading the map: social
characteristics of Washington DC, Metropolitan Bulletin,
Vol.6, pp.4-5.
Arbia G., 1989, Statistical effect of spatial data
transformations:a proposed general framework, M.
Goodchild and S. Gopal (eds), The Accuracy of Spatial
Databases, Taylor and Francis, London.
Baker A., 1986, Computers bring map projections back to
life, Geography, Vol.71, pp.333-338.
Baker H.H., 1988, Building, visualizing, and computing on
surfaces of evolution, IEEE Computer Graphics and
Applications, Vol.8, No.7, pp.31-41.
Bibliography
Baker H.H., 1990, Computation and manipulation of 3D
surfaces from image sequences, G.M. Nielson, B. Shriver
& L.J. Rosenblum (eds) Visualization in Scientific
Computing, pp.109-127, IEEE Computer Society Press,
California.
Baker J.G.P., 1986, The "Dinomic" world map projection,
The Cartographic Journal, Vol.23, pp.66-67.
Baldock E.D., 1967, Production of terrain models,
Canadian Cartographer, Vol.4, No.2, pp.128-135.
Ballard B. & Norris P., 1983, User needs - an overview, D.
Rhind (ed) A Census User's Handbook, Methuen, London.
Balls E., 1991, The long shadow of unemployment, Financial
Times, June 10th.
Balogun O.Y., 1982, Communicating through statistical
maps, The International Yearbook of Cartography, Vol.22,
pp.23-41.
Bancrift G., Plessel T., Merritt F., Walatka P.P. &
Watson V., 1990, Scientific visualization in computational
aerodynamics at NASA Ames research center, G.M.
Nielson, B. Shriver & L.J. Rosenblum (eds) Visualization
in Scientific Computing, pp.237-244, IEEE Computer
Society Press, California.
Bancroft G.V., Merritt F.J., Plessel T.C., Kelaita P.G.,
McCabe R.K. & Globus A., 1990, A multi-processed
environment for visualization of computational fluid
dynamics, Visualization '90, Proceedings of the First IEEE
Conference on Visualization, pp.14-27, October, San
Francisco, California.
Band L.E., 1986, Topographic partition of watersheds with
digital elevation models, Water Resources Research,
Vol.22, No.1, pp.15-24.
Band L.E., 1989, A terrain-based watershed information
system, Hydrological Processes, Vol.3, pp.151-162.
Barabba V.P., 1975, A challenge to cartographers, Autocarto 2 (Proceedings of the International Symposium on
Computer-Assisted Cartography), pp.15-26.
Barb C.E., 1974, A spatial display and analysis
methodology for monitoring urban change, Public Data,
Vol.2, No.4, pp.21-27.
Barbour M., 1983, The use of maps: the topographic and
thematic traditions contrasted, The Cartographic Journal,
Vol.20, No.2, pp.76-86.
Barnes B., 1979, Household surveys in the United Kingdom,
Population Trends, Vol.16, pp.12-16.
Bashshur R.L., Shannon G.W. & Metzner C.A., 1970,
The application of three-dimensional analogue models to
the distribution of medical care facilities, Medical Care,
Vol.8, No.5, pp.395-407.
Batty M., 1989, Geography and the new geometry,
Geographical Review, Vol.2, No.4, pp.7-10.
BBC/ITN, 1983, The BBC/ITN guide to the new
parliamentary constituencies, Parliamentary Research
Services, Chichester.
Beacham R., 1984, Economic activity: Britain's workforce
1971-81, Population Trends, Vol.37, pp.6-15.
182
Beacham R., 1985, Travel to work: changing patterns 197181, Population Trends, Vol.39, pp.8-16.
Beard R., 1989, The atlas of British politics (review), The
Cartographic Journal, Vol.26, p.172.
Beatty J.C. & Ware C., 1986, Using colour to display
structures in multidimensional discrete data, Color
Research and Application (Supplement), Vol.11 pp.11-14.
Beatty J.C., Cowan W.B. & Schwarz M.W., 1987, An
experimental comparison of RGB, YIQ, LAB, HSV, and
opponent color models, ACM Transactions on Graphics,
Vol.6, No.2, pp.123-158.
Beatty J.C., Cowan W.B. & Stone M.C., 1986, A
description of the colour-reproduction methods used for
this issue of color research and application, Color
Research and Application (Supplement), Vol.11 pp.83-88.
Beaven P.J., 1988, A very low-cost microcomputer-based
image processor, J.P. Muller (ed) Digital Image Processing
in Remote Sensing, Taylor and Francis, London, pp.123133.
Beck J.M., Farouki R.T. & Hinds J.K., 1986, Surface
analysis methods, IEEE Computer Graphics and
Applications, Vol.6, No.12, pp.18-36.
Becker R.A. & Cleveland W.S., 1988, Brushing
scatterplots, W.S. Cleveland & M.E. McGill (eds) Dynamic
Graphics For Statistics, Wandsworth, California.
Becker R.A., Cleveland W.S. & Weil G., 1988, The use of
brushing and rotation for data analysis, W.S. Cleveland &
M.E. McGill (eds) Dynamic Graphics For Statistics,
Wandsworth, California.
Becker R.A., Cleveland W.S. & Wilks A.R., 1988,
Dynamic graphics for data analysis, W.S. Cleveland &
M.E. McGill (eds) Dynamic Graphics For Statistics,
Wandsworth, California.
Becker R.A., Eick S.G., Miller E.O. & Wilks A.R., 1990a,
Network visualization, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.1,
pp.285-294.
Becker R.A., Eick S.G., Miller E.O. & Wilks A.R., 1990b,
Dynamic graphics for network visualization, Visualization
'90, Proceedings of the First IEEE Conference on
Visualization, pp.93-96, October, San Francisco, California.
Beddoe D.P., 1978, An alternative cartographic method to
portray origin-destination data, MA , University of
Washington.
Beddow J., 1990, Shape coding of multidimensional data on
a microcomputer display, Visualization '90, Proceedings of
the First IEEE Conference on Visualization, pp.238-246,
October, San Francisco, California.
Begg I. & Eversley D., 1986, Deprivation in the inner city:
social indicators from the 1981 census, V.A. Hausner (ed)
Critical Issues in Urban Economic Development, Chapter 3,
Clarendon Press, Oxford.
Begg I. & Moore B., 1987, The changing economic role of
Britain's cities, V.A.Hauser (ed) Critical Issues in Urban
Economic Development, Chapter 3, Clarendon Press,
Oxford.
183
Bibliography
Bell S.B.M., 1986a, Tesseral addressing and arithmetic practical concerns, S.B.M. Bell and B.M. Diaz (eds)
Spatial Data Processing using Tesseral Methods, pp.11-16.
Beniger J.R. & Robyn D.L., 1978, Quantitative graphics in
statistics: a brief history, The American Statistician,
Vol.32, No.1, pp.1-11.
Bell S.B.M., 1986b, Tesseral numbers as matrices, S.B.M.
Bell and B.M. Diaz (eds) Spatial Data Processing using
Tesseral Methods, pp.127-128.
Bennett C.H., Toffoli T. & Wolfram S., 1986, Cellular
automata '86 conference, Laboratory for Computer
Science, Massachusetts Institute of Technology, MIT/LCS/
TM-317.
Bell S.B.M. & Holroyd F.C., 1986a, Constructive tesseral
algebra, S.B.M. Bell and B.M. Diaz (eds) Spatial Data
Processing using Tesseral Methods, pp.233-256.
Bell S.B.M. & Holroyd F.C., 1986b, Tesseral algorithms,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.331-344.
Bell S.B.M., Chadwick R.A., Cooper A.P.R., Mason D.C.,
O'Connell M. & Young P.A.V., 1990, Handling four
dimensional geo-coded data, Proceedings of the 4th
International Symposium on Spatial Data Handling, Zurich,
Vol.2, pp.918-927.
Bell S.B.M., Diaz B.M. & Holroyd F.C., 1986a, Possible
uses of tesserals in physics, S.B.M. Bell and B.M. Diaz
(eds) Spatial Data Processing using Tesseral Methods,
pp.423-425.
Bell S.B.M., Diaz B.M. & Holroyd F.C., 1986b, Tesseral
constructive inverses, S.B.M. Bell and B.M. Diaz (eds)
Spatial Data Processing using Tesseral Methods, pp.285312.
Bell S.B.M., Diaz B.M. & Holroyd F.C., 1986c, Tesseral
quaternions for 3-D, S.B.M. Bell and B.M. Diaz (eds)
Spatial Data Processing using Tesseral Methods, pp.265284.
Bell S.B.M., Diaz B.M. & Holroyd F.C., 1986d, Tesseral
square roots in 2-D, S.B.M. Bell and B.M. Diaz (eds)
Spatial Data Processing using Tesseral Methods, pp.365368.
Bell S.B.M., Diaz B.M. & Holroyd F.C., 1988, Capturing
image syntax using tesseral addressing and arithmetic, J.P.
Muller (ed) Digital Image Processing in Remote Sensing,
Taylor and Francis, London, pp.135-152.
Bell S.B.M., Diaz B.M. & Malcolm J.C., 1986, Tesseral
aspects of DILL (decimal interleaved latitude/longitude)
addressing, S.B.M. Bell and B.M. Diaz (eds) Spatial Data
Processing using Tesseral Methods, pp.149-161.
Bell S.B.M., Diaz B.M., Holroyd F.C. & Mason D.C.,
1986, FTM: a fast table-driven tesseral multiplication,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.345-352.
Berge E., 1988, Some comments on C. Hamnett's reading of
the data on sociotenurial polarisation in South East
England, Environment and Planning, Vol.20, pp.973-977.
Bergeron R.D. & Grinstein G.G., 1989, A reference model
for the visualization of multi-dimensional data,
Eurographics '89, Elsevier Science Publishers.
Berlekamp E.R., Conway J.H. & Guy R.K., 1982,
Winning ways for your mathematical plays, Academic
Press, Chapter 25: What is Life?.
Berry B.J.L., 1971, Problems of data organization and
analytical methods in geography, Journal of the American
Statistical Association, Vol.66, No.335, pp.510-523.
Berry J.K. & Sailor J.K., 1987, Use of a geographic
information system for storm runoff prediction from small
urban watersheds, Environmental Management, Vol.11,
No.1, pp.21-27.
Bertin J., 1978, Theory of communication and theory of 'the
graphic', The International Yearbook of Cartography,
Vol.18, pp.118-126.
Bertin J., 1981, Graphics and graphic informationprocessing, Walter de Gruyter, Berlin.
Bertin J. , 1983a, Semiology of graphics, (translation of
Semiologie graphique by W.J. Berg) The University of
Wisconsin Press, Madison, Wisconsin.
Bertin J., 1983b, A new look at cartography, D.R.F. Taylor
(ed) Progress in Contemporary Cartography Volume II:
Graphic Communication and Design in Contemporary
Cartography, pp.69-86.
Beruchashvili N.L., 1987, Certain problems in modern
cartography, Mapping Sciences and Remote Sensing,
Vol.24, No.3, pp.179-184.
Besag J., 1974, Spatial interaction and the statistical
analysis of lattice systems, Journal of the Royal Statistical
Society B, Vol.36, No.2, pp.192-225.
Besag J., 1986, On the statistical analysis of dirty pictures,
Journal of the Royal Statistical Society B, Vol.48, No.3,
pp.259-302.
Bemis P., 1988, The implementation of an industrial
strength cache based locally parallel reduced instruction
set computers - the apollo dn10000, Electronic Imaging '88,
International Electronic Imaging Exposition and
Conference, IGC, Massachusetts, USA.
Bezdek J.C. & Chiou E.W., 1988, Core zone scatterplots:
A new approach to feature extraction for visual display,
Computer Vision, Graphics, and Image Processing, Vol.42,
pp.186-209.
Benedetti R., 1991, On the modifiable unit problem: a
monte carlo approach, EGIS '91 Proceedings, pp.85-94,
Brussels, Belgium.
Bickmore D.P., 1975, The relevance of cartography, J.C.
Davies & M.J. McCullagh (eds) Display and Analysis of
Spatial Data, pp.328-351, John Wiley & Sons, London.
Beniger J.R., 1976, Science's "unwritten" history: the
development of quantitative and statistical graphics,
Abstracts of the Annual Meeting of the American
Sociological Association, New York.
Bird E. & Wert R.D., 1982, Atlases for US policymakers,
ACSM-ASP 42nd Annual Convention, March, Denver
Colorado, pp.348-353.
Bibliography
Birdsong R.B., Bickings C.K. & Goulette A.M., 1985,
Computer-assisted production of the 1980 population
distribution map, AutoCarto 7 (Proceedings of the 7th
International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.38-42.
Birkin M. & Clarke M., 1989, The generation of individual
and household incomes at the small area level using
synthesis, Regional Studies, Vol.23.6, pp.535-548.
Bishop G., Monger M. & Ramsey P., 1990, A visualization
programming environment for multicomputers,
Multicomputing, Vol.10, No.4, pp.50-58.
Bjorklund A. & Gustavsson N., 1987, Visualization of
geochemical data on maps: new options, Journal of
geochemical exploration, Vol.29, Nos.1-3, pp.89-103.
Bjørke & Aasgaard R., 1990, Cartographic zoom,
Proceedings of the 4th International Symposium on Spatial
Data Handling, Zurich, Vol.1, pp.345-353.
Blais J.A.R., Chapman M.A. & Kok A.L., 1985, Digital
terrain modelling and applications, Technical papers of the
ACSM-ASPRS Fall Convention, September, Indianapolis,
Indiana, pp.646-651.
Blakemore M., 1983, Generalisation and error in spatial
data bases, AutoCarto 6 (Proceedings of the 6th
International Symposium on Computer-Assisted
Cartography), Vol.1, pp.313-322.
Blakemore M., 1987a, Maps by computer: the 1981 census
atlas of Brighton and Hove (review), Geography, Vol.72,
No.316, pp.276-277.
Blakemore M., 1987b, Cartographic and geographic
information systems, Progress in Human Geography,
Vol.11, pp.590-606.
Blakemore M., 1988, Cartographic and geographic
information systems, Progress in Human Geography,
Vol.12, pp.525-537.
Blinn J.F., 1990, Wonderful world of video, IEEE Computer
Graphics and Applications, Vol.10, No.3, pp.83-87.
Blome D.A., 1963, A map transformation of the timedistance relationships in the Lansing tri-county area,
Institute for Community Development and Services,
Michigan State University.
Bloomfield P., 1976, Analysis of cancer mortality in Texas,
Proceedings of the Workshop on Automated Cartography
and Epidemiology, National Center for Health Statistics,
U.S. Department of Health, pp.27-33.
Blot W.J. & Fraumeni J.F., 1982, Geographical
epidemiology of cancer in the United States, D. Schutter &
J. Fraumeni (eds), Cancer Epidemiology and Prevention,
W.B. Sanders & Co., Philadelphia.
Blumenthal L.M., 1938, Distance Geometries - a study of
the development of abstract metrics, University of Missouri
Studies, University of Missouri, Columbia.
Board C., 1967, Maps as models, R.J. Chorley & P. Haggett
(eds) Models in Geography, Methuen & Co. Ltd, London,
Chapter 16.
184
Board C., 1978, How can theories of cartographic
communication be used to make maps more effective, The
International Yearbook of Cartography, Vol.18, pp.41-49.
Board C., 1981, Cartographic communication,
Cartographica, Monograph 27. L. Guelke (ed) Maps in
Modern Geography - Geographical Perspectives on the
New Cartography, Vol.18, No.2, pp.42-78.
Board C. & Buchanan A., 1974, Evaluating thematic maps
- current trends in the United Kingdom, The Cartographic
Journal, Vol.11, No.2, pp.124-129.
Bochel J.M. & Denver D.T., 1984, The Scottish District
elections 1984: results and statistics, Elections Studies,
Department of Political Science, University of Dundee.
Bochel J.M. & Denver D.T., 1986, The Scottish regional
elections 1986: results and statistics, Electoral Studies,
University of Dundee.
Bochel J.M. & Denver D.T., 1988, Scottish district
elections 1988: results and statistics, Electoral Studies,
University of Dundee.
Bochel J.M. & Denver D.T., 1990, The Scottish regional
elections 1990: results and statistics, Electoral Studies,
University of Dundee.
Boden P., 1989, The analysis of internal migration in the
UK using census and national health service central
register data, PhD, Leeds University.
Boden P., Stillwell J. & Rees P., 1987, Migration data from
the national health service central register and the 1981
census: further comparative analysis, Leeds University,
School of Geography, Working Paper 495.
Boden P., Stillwell J. & Rees P., 1988, Linking census and
NHSCR migration data, Working Paper 511, School of
Geography, University of Leeds.
Boggs S.W., 1947, Cartohypnosis, Library Journal, Vol. 72,
pp.433-435 & 446-447.
Bolton C., Carter J. & Ward M.E., 1987, Classless
choropleth mapping with millions of colors: a
developmental system, AutoCarto 8 (Proceedings of the 8th
International Symposium on Computer-Assisted
Cartography), pp.431-436 .
Borchert J.R., 1987, Maps, geography, and geographers,
The Professional Geographer, Vol.39, No.4, pp.387-389.
Borgefors G., 1989, Distance transformations on hexagonal
grids, Pattern Recognition Letters, Vol.9, No.2, pp.97-105.
Boston G., 1980, Classification of occupations, Population
Trends, Vol.20, pp.9-11.
Boudrialt G., 1987, Topology in the TIGER file, AutoCarto
8 (Proceedings of the 8th International Symposium on
Computer-Assisted Cartography), pp.258-263 .
Bowler S., 1991, Contextual models of politics: the political
impact of friends and neighbours, Political geography
quarterly, Vol.10, No.2, pp.91-96.
Boyle A.R., 1974, Geographic information systems in
hydrography and oceanography, Canadian Cartographer,
Vol.11, No.2, pp.125-141.
185
Bracewell R.J. & Henderson A., 1985, Towards the 1991
census of population: the views of the local authority users,
Burisa, Tyne and Wear countryside Unit.
Bracken I. & Martin D., 1989, The generation of spatial
population distributions from census centroid data,
Environment and Planning A, Vol.21, pp.537-543.
Brakenhoff G.J., van der Voort H.T.M., Baarslag M.W.,
Mans B., Oud J.L., Zwart R. &. van Driel R, 1988,
Visualization and analysis techniques for three dimensional
information acquired by confocal microscopy, Scanning
Microscopy, Vol.2, No.4, pp.1831-1838.
Bramley G., 1991, The demand for social housing in
England in the 1980s, M. Satsangi (ed) Changing Housing
Finance Systems, Chapter 2, Centre for Housing Research,
University of Glasgow.
Bramson M. & Griffeath, 1989, Flux and fixation in cyclic
particle systems, The Annals of Probability, Vol.17, No.1,
pp.26-45.
Brandes D., 1975, Choosing of colour sequences for
reproduction in monochrome halftone, The Cartographic
Journal, Vol.12, No.2, pp.109-111.
Brandes D., 1983, Sources for relief representation
techniques, The Cartographic Journal, Vol.20, No.2, pp.8794.
Brannon G., 1989, The artistry and science of map-making,
The Geographical Magazine, Vol.61, No.9, pp.38-40.
Brant J., 1984, Patterns of migration from the 1981 census,
Population Trends, Vol.35, pp.23-30.
Brass W., 1989, Is Britain facing the twilight of
parenthood?, H. Joshi (ed) The Changing Population of
Britain, Basil Blackwell, Oxford.
Brassel K., 1975, Neighborhood computations for large sets
of data points, Auto-carto 2 (Proceedings of the
International Symposium on Computer-Assisted
Cartography), pp.337-345.
Brassel K., 1977, A survey of cartographic display software,
The International Yearbook of Cartography, Vol.17, pp.6077.
Bibliography
Brindle D., 1991, Poll tax may be key to census shortfall,
The Guardian, Tuesday July 23, p.5.
Brinton W.C., 1914, Graphic methods of presenting data,
The Engineering Magazine, Vol.47, No.5, pp.651-666 and
817-829.
Brisson D.W. (ed), 1978, Hypergraphics: visualizing
complex relationships in art, science and technology,
AAAS Selected Symposia Series, Westview Press,
Colorado.
Brittain D.L., Aller J., Wilson M. & Wang S.L.C., 1990,
Design of an end-user data visualization system,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.323-328, October, San Francisco,
California.
Britton M., 1986, Recent population changes in perspective,
Population Trends, Vol.44, pp.33-41.
Broek J.O.M. & Webb J.W., 1968, A geography of
mankind, McGraw-Hill, pp.34-35 (by Mei-Ling Hsu).
Brooks D.G. & Verdini W.V., 1988, Computational
experience with generalized simulated annealing over
continuous variables, American Journal of Mathematical
and Management Sciences, Vol.8, Nos.3 & 4, pp.425-449.
Brooks W. & Sieffert J., 1985, TLDB: A global data base
designed for rapid digital image retrieval, AutoCarto 7
(Proceedings of the 7th International Symposium on
Computer-Assisted Cartography), Washington D.C., pp.4445.
Brooks W.D. & Pinzke K.G., 1971, A computer program
for three-dimensional presentation of geographical data,
Canadian Cartographer, Vol.8, No.2, pp.110-125.
Broome F.R., 1976, Techniques for statistical mapping via
automation, Proceedings of the Workshop on Automated
Cartography and Epidemiology, National Center for Health
Statistics, U.S. Department of Health, pp.67-68.
Brotman L.S. & Netravali A.N., 1988, Motion
interpolation by optimal control, Computer Graphics,
Vol.22, No.4, pp.309-315.
Browne G.S. (ed), 1974, Atlas of Europe, Bartholomew and
Warne, London.
Brassel K.E. & Kiriakakis Z., 1983, Orthogonal threedimensional views for thematic mapping, AutoCarto 6
(Proceedings of the 6th International Symposium on
Computer-Assisted Cartography), Vol.2, pp.416-425.
Browne T.J. & Millington A.C., 1983, An evaluation of the
use of grid squares in computerised choropleth maps, The
Cartographic Journal, Vol.20, No.2, pp.71-75.
Brassel K.E. & Utano J.J., 1979, Design strategies for
continuous-tone area mapping, The American
Cartographer, Vol.6, No.1, pp.39-50.
Brunsdon C., 1991, Estimating probability surfaces in GIS:
an adaptive technique, EGIS '91 Proceedings, pp.155-164,
Brussels, Belgium.
Braudel F., 1979, The Wheels of Commerce, Fontana Press,
London.
Brunsdon C., Coombes M., Munro M. & Symon P., 1991,
Housing and labour market interactions: an analysis of
house price inflation in British LLMAs, 1983/87, M.
Satsangi (ed) Changing Housing Finance Systems, Chapter
8, Centre for Housing Research, University of Glasgow.
Breen P.T., H.H. & P.E. Miller-Jacobs, 1987, Color
displays applied to command, control, and communication
systems, H.J. Durrett (ed) Color and the Computer, Ch.9,
pp.171-188.
Brewer C.A., 1989, The development of process-printed
munsell charts for selecting map colors, The American
Cartographer, Vol.16, No.4, pp.269-278.
Brusegard D. & Menger G., 1989, Real data and real
problems: dealing with large spatial databases, M.
Goodchild and S. Gopal (eds), The Accuracy of Spatial
Databases, Taylor and Francis, London.
Bibliography
Bryanston-Cross P.J., 1988, Holographic flow
visualization, The Journal of Photographic Science, Vol.36,
No.1, pp.8-13.
Buchanan K.M., 1964, Profiles of the third world, Pacific
Viewpoint, Vol.5, pp.97-126.
Buck N., Gordon I., Young K., Ermish J. & Mills L.,
1986, The London employment problem, Economic and
Social Research Council, Inner Cities Research Programme,
Clarendon Press, Oxford.
Buckley D.J., 1987, Three dimensional mapping as a tool
for the management of spatial information, MA ISBN:0315-41126-0, Department of Geography, Edmonton,
Alberta.
Budge I. & Farlie D.J., 1983, Explaining and predicting
elections: issue effects and party strategies in twenty-three
democracies, George Allen and Unwin, London.
Bui-Tuong P., 1975, Illumination for computer generated
pictures, Communications of the ACM, Vol.18, No.6,
pp.311-317.
Buit M. (ed), 1987, GIS news, Photogrammetric
Engineering and Remote Sensing, Vol.53, No.9-12.
Buja A., Asimov D., Hurley C. & McDonald J.A., 1988,
Elements of viewing pipeline for data analysis, W.S.
Cleveland & M.E. McGill (eds) Dynamic Graphics For
Statistics, Wandsworth, California.
Bulusu L., 1989, Migration in 1988, Population Trends,
Vol.58, pp.33-39.
Bulusu L., 1990, Internal migration in the UK, 1989,
Population Trends, Vol.62, pp.3-36.
Bunge W.W., 1964, Patterns of Location, Discussion Paper
No.3, Michigan Inter-University Community of
Mathematical Geographers, University of Michigan, Ann
Arbor.
Bunge W.W., 1966, Theoretical geography, Lund Studies in
geography, Series C, No.1, pp.1 -285.
Bunge W.W., 1968, Spatial Prediction, J.D. Nystuen (ed)
The Philosophy of Maps, Discussion Paper No.12,
Michigan Inter-University Community of Mathematical
Geographers, Wayne State University, pp.31-33.
Bunge W.W., 1973, The geography of human survival,
Annals of the Association of American Geographers,
Vol.63, No.3, pp.275-295.
Bunge W.W., 1975, Detroit humanly viewed: the American
urban present, R. Abler, D. Janelle, A. Philbrick & J.
Sommer (eds), Human Geography in a Shrinking World,
Duxbury Press, Massachusetts
Bunge W.W., 1988, Nuclear war atlas, Basil Blackwell,
Oxford.
Bunge W.W. & Bordessa R., 1975, The Canadian
alternative: survival, expeditions and urban change,
Department of Geography, Atkinson College, York
University, Toronto, Ontario, Canada.
186
Bureau of the Census, 1970, Distribution of older
Americans in 1970 related to year of maximum county
population, United States Maps, GE-70, No.2, U.S.
Department of Commerce, Washington D.C..
Bureau of the Census, 1973a, 1970 census of housing:
graphic summary of the 1970 housing census, U.S.
Department of Commerce, Social and Economic Statistics
Administration, Washington DC GPO.
Bureau of the Census, 1973b, 1970 census of population:
graphic summary of the 1970 population census, U.S.
Department of Commerce. Social and Economic Statistics
Administration, Washington DC GPO.
Burnham W.D., 1978, Great Britain: the death of the
collectivist consensus?, L.Maisel and J.Coopwe (eds),
Political Parties: Development and Decay, Sage, London.
Burrough P., 1987, Multiple sources of spatial variation
and how to deal with them, AutoCarto 8 (Proceedings of
the 8th International Symposium on Computer-Assisted
Cartography), pp.145-154 .
Burton C.F., 1970, Air transportation maps, Proceedings of
the International Conference on Transportation Maps,
pp.139-149, Budapest.
Busteed M.A., 1975, Geography of voting behaviour,
Oxford University Press, Oxford.
Butler D.E. & Kavanagh D., 1974, The British general
election of February 1974, Macmillan Press, London.
Butler D.E. & Kavanagh D., 1975, The British general
election of October 1974, Macmillan Press, London.
Butler D.E. & Kavanagh D., 1979, The British general
election of 1979, Macmillan Press, London.
Butler D.E. & Kavanagh D., 1984, The British general
election of 1983, Macmillan Press, London.
Butler D.E. & Kavanagh D., 1988, The British general
election of 1987, Macmillan Press, London.
Butler D.E. & King A., 1965, The British general election
of 1964, Macmillan & Co. Ltd., London.
Butler D.E. & King A., 1966, The British general election
of 1966, Macmillan, London.
Butler D.E. & Pinto-Duschinsky M., 1971, The British
general election of 1970, Macmillan, London.
Butler D.E. & Rose R., 1955, The British general election
of 1955, Frank Cass & Co. Ltd., London.
Butler D.E. & Rose R., 1960, The British general election
of 1959, Frank Cass & Co. Ltd., London.
Buttenfield B.P., 1983, Topology and the concept of the
statistical solid, AutoCarto 6 (Proceedings of the 6th
International Symposium on Computer-Assisted
Cartography), Vol.1, p.323.
Buttenfield B.P., 1985, Treatment of the cartographic line,
Cartographica, Vol.22, No.2, pp.1-26.
187
Buttenfield B.P. & Ganter J.H., 1990, Visualization and
GIS: what should we see? what might we miss?,
Proceedings of the 4th International Symposium on Spatial
Data Handling, Zurich, Vol.1, pp.307-316.
Buttenfield B.P. & Langran G., 1987, Formatting
geographic data to enhance manipulability, AutoCarto 8
(Proceedings of the 8th International Symposium on
Computer-Assisted Cartography), pp.201-210 .
Buttenfield B.P. & Mackaness W.A., 1991, Visualization,
D.J. Maguire, M. Goodchild & D.W. Rhind (eds)
Geographical Information Systems: Principles and
Applications, Longman, London.
Byl J., 1989, Self-reproduction in small cellular automata,
Physica D, Vol.34, pp.295-299.
Cabral B. & Hunter C., 1990, Visualization tools at
lawrence livermore national laboratory, G.M. Nielson, B.
Shriver & L.J. Rosenblum (eds) Visualization in Scientific
Computing, pp.221-230, IEEE Computer Society Press,
California.
Calkins H.W., 1984, Space-time data display techniques,
Proceedings of the International Symposium on Spatial
Data Handling, pp.324-331, August, Zurich, Switzerland.
Calle E.E., 1980, Computer mapping of cancer mortality by
census tract: Columbus, Ohio 1956-1974, AutoCarto IV
(Proceedings of the International Symposium on
Cartography and Computing: Applications in Health and
the Environment, Virginia), Vol.2, pp.180-187.
Callen M., James I., Mason D.C. & Quarmby N., 1986, A
test-bed for experiments on hierarchical data models in
integrated geographic information systems, S.B.M. Bell
and B.M. Diaz (eds) Spatial Data Processing using Tesseral
Methods, pp.193-212.
Campbell D. & Higgins J., 1988, Matrix method for finding
sets of contiguous non-zero elements in a 2-dimensional
array-II, Pattern Recognition, Vol.21, No.5, pp.451-453.
Campbell R.V. & Knight D.B., 1976, Political
territoriality in Canada / a choropleth and isopleth
analyses, The Canadian Cartographer, Vol.13, No.1, pp.110.
Canters F., 1989, New projections for world maps / a
quantitative-perceptive approach, Cartographica, Vol.26,
No.2, pp.53-71.
Card S.K., Pavel M. & Farrell J.E., 1985, Window-based
computer dialogues, Human-computer InteractionINTERACT '84, Elsevier Science Publications.
Carlbom I. & Potmesil M., 1988, Modelling and display of
empirical data, IEEE Computer Graphics and Applications,
Vol.8, No.7, pp.10-11.
Carlstein T., 1982, Time resources, society and ecology,
George Allen and Unwin, London.
Carlstein T., Parkes D. & Thrift N., 1978, Time and
regional dynamics, Edward Arnold, London.
Carlyle I.P. & Carlyle W.J., 1977, The ellipse / a useful
cartographic symbol, Canadian Cartographer, Vol.14,
No.1, pp.48-58.
Bibliography
Carr D.B. & Nicholson W.L., 1988, EXPLOR4: a program
for exploring four-dimensional data using stereo-ray
glyphs, dimensional constraints, rotation, and masking,
W.S. Cleveland & M.E. McGill (eds) Dynamic Graphics
For Statistics, Wandsworth, California.
Carr D.B., Littlefield R.J., Nicholson W.L. & Littlefield
J.S., 1987, Scatterplot matrix techniques for large N,
Journal of the American Statistical Association, Vol.82,
No.398, pp.424-435.
Carrara A., 1986, Drainage and divide networks derived
from high-fidelity digital terrain models, C.F. Chung, A.G.
Fabbri & R. Sinding-Lacson (eds) Quantitative Analysis of
Mineral and Energy Resources, NATO ASI series, D.
Reidel, Dordecht, Holland, pp.581-597.
Carrier N.H. & Jeffery J.R., 1953, External migration: a
study of the available statistics, General Register Office:
Studies on Medical and Population Subject No.6, Her
Majesty's Stationery Office, London.
Carruthers A.W., 1985, Mapping the population census of
Scotland, The Cartographic Journal, Vol.22, No.2, pp.8387.
Carstensen L.W., 1981, Time-distance mapping and timespace convergence: the Southern United States, 1950-1975,
Southeastern Geographer, Vol.21, No.2, pp.67-83.
Carstensen L.W., 1982, A continuous shading scheme for
two-variable mapping, Cartographica, Vol.19, No.3-4,
pp.53-70.
Carstensen L.W., 1984, Perceptions of similarity on
bivariate choroplethic maps, The Cartographic Journal,
Vol.21, pp.23-29.
Carstensen L.W., 1986a, Bivariate choropleth mapping: the
effects of axis scaling, The American Cartographer, Vol.13,
No.1, pp.27-42.
Carstensen L.W., 1986b, Hypothesis testing using
univariate and bivariate choropleth maps, The American
Cartographer, Vol.13, No.3, pp.231-251.
Carstensen L.W., 1987, A measure of similarity for cellular
maps, The American Cartographer, Vol.14, No.4, pp.345358.
Carswell R.J.B. & Wescott H.M., 1974, Comparative use
of a picture, and written material in social studies, The
Cartographic Journal, Vol.11, No.2, pp.122-123.
Carter J.R., 1988, Digital representations of topographic
surfaces, Photogrammetric Engineering and Remote
Sensing, Vol.54, No.11, pp.1577-1580.
Carty W.P., 1984, The new map makers, Americas, Vol.36,
No.5, pp.24-25.
Cassing D., 1989, Evaluating workstations for visualization
performance, Research and Development, Vol.31, No.3A,
pp.26-30.
Castner H.W., 1971, An Ontario regional development
atlas, H.W. Castner & G. McGraths (eds) Map Design and
the Map User, Cartographica, monograph No.2, pp.77-84.
Bibliography
Castner H.W., 1980, Special purpose mapping in 18th
century russia: A search for the beginnings of thematic
mapping, The American Cartographer, Vol.7, No.2,
pp.163-175.
Cauvin C., Schneider C. & Cherrier G., 1989,
Cartographic transformations and the piezopleth maps
method, The Cartographic Journal, Vol.26, pp.96-104.
Cebrian de Miguel J.A., 1983, Application of a model of
dynamic cartography to the study of the evolution of
population density in Spain from 1900 to 1981, AutoCarto
6 (Proceedings of the 6th International Symposium on
Computer-Assisted Cartography), Vol.2, pp.475-483.
Cebrian J.A., Mower J.E. & Mark D.M., 1985, Analysis
and display of digital elevation models within a quadtreebased geographic information system, AutoCarto 7
(Proceedings of the 7th International Symposium on
Computer-Assisted Cartography), Washington D.C., pp.5564.
Cederberg R.L.T., 1980, A data structure for a raster map
data base, AutoCarto IV (Proceedings of the International
Symposium on Cartography and Computing: Applications
in Health and the Environment, Virginia), Vol.2, pp.85-92.
Census Division, OPCS, 1976, Travelling to work: the 1971
census picture, Population Trends , Vol.4, pp.12-20.
Census Division, OPCS, 1981, The first results of the 1981
census of England and Wales, Population Trends, Vol.25,
pp.21-29.
188
Champion A.G. & Cogndon P., 1988, Recent trends in
Greater london's population, Population Trends, Vol.53,
pp.7-17.
Champion A.G. & Green A., 1989, Local economic
differentials and the "north-south divide" , J. Lewis & A.
Townsend (eds), The North-South divide: Regional change
in Britain in the 1980s, Paul Chapman, London.
Champion A.G., Green A.E., Owen D.W., Ellin D.J. &
Coombes M.G., 1987, Changing places: Britain's
demographic, economic and social complexion, Edward
Arnold, London.
Chan K.K.L., 1985, Locating 'lakes' on digital terrain
model, Technical papers of the ACSM-ASPRS Fall
Convention, September, Indianapolis, Indiana, pp.68-77.
Chang K.T., 1977, Visual estimation of graduated circles,
Canadian Cartographer, Vol.14, No.2, pp.130-138.
Chang K.T., 1980, Circle size judgment and map design,
The American Cartographer, Vol.7, No.2, pp.155-162.
Chang K.T., 1982, Multi-component quantitative mapping,
The Cartographic Journal, Vol.19, No.2, pp.95-103.
Charlton M., Openshaw S., Rainsbury M. & Osland C.,
1989, Spatial analysis by computer movie, North East
Regional Research Laboratory Research Report 89/8,
University of Newcastle upon Tyne.
Census Research Unit, 1980, People in britain - a census
atlas, H.M.S.O., London.
Chen K.P., Wu H.Y., Yeh C.C. & Cheng Y.J., 1979,
Color atlas of cancer mortality by administrative and other
classified districts in Taiwan area: 1968-1976, National
Science Council, Taupei, Taiwan.
Cerny J.W., 1972, Ontogenetic and phylogenetic
perspectives on cartograms, The Monadnock, Vol.46,
pp.47-52.
Chen M., Mountford S.J. & Sellen A., 1988, A study in
interactive 3-D rotation using 2-D control devices,
Computer Graphics, Vol.22, No.4, pp.121-129.
Cervelle B., Chorowicz J. & Riazanoff S., 1988, Ridge and
valley line extraction from digital terrain models,
International Journal of Remote Sensing, Vol.9, No.6,
pp.1175-1183.
Chen Z.T., 1987a, Contour generalization by a 3dimensional spatial low-pass filtering, GIS'87 Technical
papers, ASPRS-ACSM Second Annual International
Conference, San Francisco, California, pp.375-386.
Chambers J., Cleveland W., Kleiner B. & Tukey P.,
1983, Graphical methods for data analysis, WadsworthDuxbury Press
,.
Chen Z.T., 1987b, Quadtrees and quadtree spatial spectra
in large scale geographic information systems- the
hierarchical handling of spatial data, PhD, no. 8727824,
University of California, santa Barbara.
Champion A.G., 1981, Population trends in rural Britain,
Population Trends, Vol.26, pp.20-23.
Champion A.G., 1983a, Population trends in the 1970s,
J.B. Goddard & A.G. Champion (eds) The Urban and
Regional Transformation of Britain, Methuen, London.
Champion A.G., 1983b, England and Wales 1981, The
Geographical Association, Sheffield.
Champion A.G., 1989, Internal migration and the spatial
distribution of population, H. Joshi (ed) The Changing
Population of Britain, Basil Blackwell, Oxford.
Champion A.G., 1991, Population deconcentration in
Europe: the research challenge, Draft submission to
working group 3, ESF RURE 1st General Conference,
Lisbon, Feb 1991.
Champion A.G. & Brunsdon C., 1988, The variation of
house prices between local labour market areas: a
preliminary analysis of building society data, C.U.R.D.S.,
Discussion Paper no. 90, Newcastle University.
Chen Z.T. & Guevara J.A., 1987, Systematic selection of
very important points (VIP) From digital terrain models for
constructing triangular irregular networks, AutoCarto 8
(Proceedings of the 8th International Symposium on
Computer-Assisted Cartography), pp.50-56.
Chen Z.T. & Peuquet D., 1985, Quad tree spatial spectra
guide: a fast spatial heuristic search in a large GIS,
AutoCarto 7 (Proceedings of the 7th International
Symposium on Computer-Assisted Cartography),
Washington D.C., pp.75-82.
Chen Zi-Tan & Tobler W.R., 1986, Quadtree
representations of digital terrain, Auto Carto, London,
pp.475-484.
Chengrui L. (ed), 1987, The Population atlas of china,
Oxford University Press, Oxford.
189
Chernoff H., 1973, The use of faces to represent points in kdimensional space graphically, Journal of the American
Statistical Association, Vol.68, No.342, pp.361-368.
Chernoff H., 1978, Graphical representations as a
discipline, Wang P.C.C. (ed) Graphical Representation of
Multivariate Data, Academic Press, New York.
Chernoff H. & Rizvi M.H., 1975, Effect on classification
error of random permutations of features in representing
multivariate data by faces, Journal of the American
Statistical Association, Vol.70, No.351, pp.548-554.
Chesnais M., 1970, An attempt to map some analytical
features of the French railway traffic, Proceedings of the
International Conference on Transportation Maps, pp.101102, Budapest.
Chinese Academy of Medical Sciences, 1981, Atlas of
cancer mortality in the people's republic of China, China
Map Press, Shanghai.
Chiriin K. (ed), 1977, The national atlas of Japan,
Geographical Survey Institute, Tokyo.
Choynowski M., 1959, Maps based on probabilities,
Journal of the American Statistical Association, Vol.54,
No.286, pp.385-388.
Chrisman N.R., 1975, Topological information systems for
geographic representation, Auto-carto 2 (Proceedings of
the International Symposium on Computer-Assisted
Cartography), pp.346-351.
Chrisman N.R., 1988, The risks of software innovation: a
case study of the Harvard lab, The American Cartographer,
Vol.15, No.3, pp.291-300.
Christensen A.H.J., 1983, Uniform grids from raster data,
IEEE Computer Graphics and Applications, Vol.3, No.9,
pp.44-53.
Christensen A.H.J., 1987, Fitting a triangulation to contour
lines, AutoCarto 8 (Proceedings of the 8th International
Symposium on Computer-Assisted Cartography), pp.57-67.
Chua L.O. & Yang L., 1988a, Cellular neural networks:
applications, IEEE Transactions on Circuits and Systems,
Vol.35, No.10, pp.1273-1290.
Bibliography
Clarke J.I., 1975, Population and scale, Census Research
Unit Working paper No.1, University of Durham.
Clarke J.I., 1978, Population geography, Progress in human
geography, Vol.2, pp.163-169.
Clarke J.I. & Rhind D.W., 1975, The relationship between
the size of areal units and the characteristics of population
structure, Census Research Unit Working paper No.5,
University of Durham.
Clarke K.C., 1987, Scale-based simulation of topography,
AutoCarto 8 (Proceedings of the 8th International
Symposium on Computer-Assisted Cartography), pp.680688 .
Clarke K.C., 1988, Scale-based simulation of topographic
relief, The American Cartographer, Vol.15, No.2, pp.173181.
Clauss C., 1970, Transportation maps for the purposes of
agriculture and food-processing industry, Proceedings of
the International Conference on Transportation Maps,
pp.121-123, Budapest.
Cleveland W.S., 1985, The elements of graphing data,
AT&T Bell Laboratories, .
Cleveland W.S. & McGill R., 1987, Graphical perception:
the visual decoding of quantitative information on
graphical displays of data, Journal of the Royal Statistical
Society A, Vol 150, No.3, pp.192-210.
Cleveland W.S. et al, 1987, Research in statistical
graphics, Journal of the American Statistical Association,
Vol.82, No.398, pp.419-423.
Cliff A.D. & Haggett P., 1988, Atlas of disease
distributions: analytic approaches to epidemiological data,
Basil Blackwell Ltd, Oxford.
Cliff A.D. & Haggett P., 1989, Plotting Disease, The
Geographical Magazine, Vol.61, No.6, pp.26-29.
Cliff A.D. & Ord J.K., 1975, The choice of a test for spatial
autocorrelation, J.C. Davies & M.J. McCullagh (eds)
Display and Analysis of Spatial Data, pp.54-77, John Wiley
& Sons, London.
Chua L.O. & Yang L., 1988b, Cellular neural networks:
theory, IEEE Transactions on Circuits and Systems, Vol.35,
No.10, pp.1257-1271.
Coates B.E., 1968, The distribution of the overseas-born
population of the British Isles, Institute of British
Geographers, Transactions no.43, April 1968, George
Philip & Son, London.
Clark A.M. & Thomas F.G., 1990, The geography of the
1991 census, Population Trends, No.60, pp.9-15.
Cochrane A. & Anderson J. (eds), 1989, Restructuring
Britain: Politics in Transition, Sage Publications, London.
Clark D.M., 1977, Battle for the counties: guide to the
county council elections May 1977, Redrose Publications,
Newcastle.
Cocks K.D., Walker P.A. & Parvey C.A., 1988, Evolution
of a continental-scale geographical information system,
International Journal of Geographical Information Systems,
Vol.2, No.3, pp.263-280.
Clark J.W., 1977, Time-distance transformations of
transportation networks, Geographical Analysis, Vol.9,
No.2, pp.195-205.
Clarke A.L., Gruen A. & Loon J.C., 1982, The application
of contour data for generating high fidelity grid digital
elevation models, AutoCarto 5 (Proceedings of the 5th
International Symposium on Computer-Assisted
Cartography), pp.213-222.
Cole J.P., 1972, The geography of world affairs, 5th edition,
Penguin Books, p.72 & 235.
Cole J.P. & King C.A.M., 1968, Quantitative geography,
John Wiley and Sons, pp.196-226.
Cole W.G., 1988, Three graphic representations to aid
Bayesian inference, Methods of Information in Medicine,
Vol.27, No.3, pp.125-132.
Bibliography
Collins N.E., Eglese R.W. & Golden B.L., 1988, Simulated
annealing - an annotated bibliography, American Journal
of Mathematical and Management Sciences, Vol.8, Nos.3
& 4, pp.209-307.
Collins S.H., 1980, A unified system for terrain analysis and
mapping from DEM and DTM, AutoCarto IV (Proceedings
of the International Symposium on Cartography and
Computing: Applications in Health and the Environment,
Virginia), Vol.2, pp.124-131.
Comeau M.A. & Holbaek-Hanssen E., 1983, Compression
and compaction of binary raster images, AutoCarto 6
(Proceedings of the 6th International Symposium on
Computer-Assisted Cartography), Vol.1, pp.362-371.
Compton P.A., 1982, The changing population, R.J.
Johnston and J.C. Doornkamp (eds), The Changing
Geography of the United Kingdom , Methuen and co.,
London.
Congdon P., 1989, An analysis of population and social
change in London wards in the 1980s, Trans. Inst. of
British Geographers, Vol.14, pp.478-491.
Congdon P. & Champion A., 1989, Trends and structure in
London's migration and their relation to employment and
housing markets, P.Congdon & P.Batey (eds) Advances in
Regional Demography: Information, Forecasts, Models,
Belhaven Press London.
Cooke D.F., 1980, Thematic and reference maps, The
American Cartographer, Vol.7, No.2, p.176.
Coombes M.G., 1978, Contiguity: analysis of a concept for
spatial studies, Discussion Paper No.15, C.U.R.D.S.,
University of Newcastle upon Tyne.
Coombes M.G., 1983, Functional regions, C.U.R.D.S.,
University of Newcastle-upon-Tyne, Factsheet 1.
Coombes M.G., Champion A.G. & Monro M., 1991,
House price inflation and local labour market influences in
Britain, J.Allen & C.Hamnett (eds) Housing and Labour
Markets: Building the Connections, Chapter 6, Unwin
Hyman, London.
Coombes M.G., Dixon J.S., Goddard J.B., Openshaw S.
& Taylor P.J., 1978, Towards a more rational
consideration of census areal units: daily urban systems in
Britain, Environment and Planning A, Vol.10, pp.11791185.
Coombes M.G., Dixon J.S., Goddard J.B., Openshaw S.
& Taylor P.J., 1979, Daily urban systems in Britain: from
theory to practise, Environment and Planning A, Vol.11,
pp.565-574.
Coombes M.G., Green A.E. & Owen D.W., 1985, Local
labour market areas for different social groups,
C.U.R.D.S., Newcastle University, Discussion Paper,
No. 74 .
Cooper A.K., 1988, Temporal terrain shading, Technical
papers of the ACSM-ASPRS Annual Convention, St. Louis,
Missouri, Vol.2, pp.282-290.
Coppock J.T., 1975, Maps by line printer, J.C. Davies &
M.J. McCullagh (eds) Display and Analysis of Spatial Data,
pp.137-154, John Wiley & Sons, London.
190
Coppock J.T., 1988, The analogue to digital revolution: a
view from an unreconstructed geographer, The American
Cartographer, Vol.15, No.3, pp.263-275.
Coquillart S. & Gangnet M., 1984, Shaded display of
digital maps, IEEE Computer Graphics and Applications,
Vol.4, No.7, pp.35-42.
Corbett J.P., 1975, Topological principles in cartography,
Auto-carto 2 (Proceedings of the International Symposium
on Computer-Assisted Cartography), pp.61-65.
Cornwell B. & Robinson A.H., 1966, Possibilities for
computer animated films in cartography, The Cartographic
Journal, Vol.3, pp.79-82.
Couclelis H., 1985, Cellular worlds: a framework for
modelling micro-macro dynamics, Environment and
Planning A, Vol.17, pp.585-596.
Couclelis H., 1988, Of mice and men: what rodent
populations can teach us about complex spatial dynamics,
Environment and Planning A, Vol.20, pp.99-109.
Coulson M.R.C., 1977, Political truth and the graphic
image, The Canadian Cartographer, Vol.14, No.2, pp.101111.
Coulson M.R.C., 1978, "Potential for variation": a concept
for measuring the significance of variations in size and
shape of areal units, Geografiska Annaler, Series B,
Vol.60, No.1, pp.48-64.
Coulson M.R.C., 1988, Michigan political atlas (review),
Cartographica, Vol.25, No.3, pp.144-145.
Coulson M.R.C., 1989a, Atlas of industrializing Britain:
review, Cartographica, Vol.26, No.2, pp.117-119.
Coulson M.R.C., 1989b, Political atlas of Illinois: review,
Cartographica, Vol.26, No.2, pp.115-117.
Coulson M.R.C., 1989c, The Netherlands in fifty maps: an
annotated atlas: review, Cartographica, Vol.26, No.2,
pp.104-105.
Courgeau D., 1976, Quantitative, demographic, and
geographic approaches to internal migration, Environment
and Planning A, Vol.8, pp.261-269.
Cowan W.B. & Rowell N., 1986, On the gun independence
and phosphor constancy of colour video monitors, Color
Research and Application (Supplement), Vol.11 pp.34-38.
Cowen D.J., 1984, Using color bivariate mapping
procedures to model spatial processes, Proceedings of the
International Symposium on Spatial Data Handling, pp.349370, August, Zurich, Switzerland.
Cowen D.J., Hodgson M. & Wojcik J., 1987, A versatile
cartographic output system for a grid cell GIS, GIS/LIS'87
Technical papers, ASPRS-ACSM Annual Convention,
Volume 5, Baltimore, Maryland, pp.75-81.
Cox C.W., 1975, Psychophysical research and map reading
analysis, Auto-carto 2 (Proceedings of the International
Symposium on Computer-Assisted Cartography), pp.233237.
Cox C.W., 1976, Anchor effects and the estimation of
graduated circles and squares, The American
Cartographer, Vol.3, No.1, pp.65-74.
191
Bibliography
Cox C.W., 1980, The effects of background on the equal
value gray scale, Cartographica, Vol.17, No.1, pp.53-71.
Craig J., 1986, The most densely populated areas of
England and Wales, Population Trends, Vol.45, pp.34-41.
Cox K.R., 1968, Suburbia and voting behavior in the
London metropolitan area, Annals of the Association of
American Geographers, Vol.58, pp.111-127.
Craig J., 1987a, Population potential and some related
measures, Area, Vol.19.2, pp.141-146.
Cox L.H., 1975, Applications of lattice theory to automated
coding and decoding, Auto-carto 2 (Proceedings of the
International Symposium on Computer-Assisted
Cartography), pp.519-522.
Cozens P. & Swaddle K., 1987, The British general
election of 1987, Electoral Studies, Vol.6, No.3, pp.263288.
Craig F.W.S., 1971, British parliamentary election results
1950-1970, Political Reference Publications, Chichester.
Craig F.W.S., 1977, How Britain votes 1: a handbook a
parliamentary election results 1974-1977, Parliamentary
Research Services, Chichester.
Craig F.W.S., 1980, How Britain votes 2: British
parliamentary election results 1974-1979, Parliamentary
Research Services, Chichester.
Craig F.W.S., 1981, British parliamentary election results
1950-1973, Parliamentary Research Services, Chichester.
Craig F.W.S., 1984, British parliamentary election results
1974-1983, Parliamentary Research Services, Chichester.
Craig F.W.S., 1984, How Britain votes 3: British
parliamentary election results 1983, Parliamentary
Research Services, Chichester.
Craig F.W.S., 1989, How Britain votes 4: British
parliamentary election results 1983-1987, Parliamentary
Research Services, Chichester.
Craig J., 1976, 1971 census grid squares, Population
Trends, Vol.6, pp.14-15.
Craig J., 1977a, Densely populated areas, Population
Trends, Vol.9, pp.16-19.
Craig J., 1977b, Population scale mapping, Population
Trends, Vol.8, pp.18-19.
Craig J., 1979, Population density: changes and patterns,
Population Trends, Vol.17, pp.12-16.
Craig J., 1980a, Areas of rapid population growth 1961-71,
Population Trends, Vol.21, pp.14-19.
Craig J., 1980b, Comparing counties, Population Trends,
Vol.19, pp.22-28.
Craig J., 1981, Migration patterns of Surrey, Devon and
South Yorkshire, Population Trends, Vol.23, pp.16-21.
Craig J., 1982, The effectiveness of different area
groupings, Population Trends, Vol.28, pp.18-22.
Craig J., 1985a, Better measures of population density,
Population Trends, Vol.39, pp.16-21.
Craig J., 1985b, Ward population trends 1971 to 1981,
Population Trends, Vol.42, pp.30-35.
Craig J., 1987b, An urban-rural categorisation for wards,
and local authorities, Population Trends, Vol.47, pp.6-11.
Craig J., 1987c, Changes in the population composition of
England and Wales since 1841, Population Trends, Vol.48,
pp.27-36.
Craig J., 1988, Population density and concentration in
England and Wales 1971 and 1981, Studies on Medical and
Population Subjects No.52, Office of Population Censuses
and Surveys.
Craig J., 1989, Changing age distribution among young
adults: their demographic effects, Population Trends,
Vol.56, pp.24-30.
Craig J., 1990, Effects of changing age distributions on fiveyear age-specific rates, Population Trends, Vol.59, pp.3031.
Crawford S.L. & Fall T.C., 1990, Projection pursuit
techniques for the visualization of high dimensional
datasets, G.M. Nielson, B. Shriver & L.J. Rosenblum (eds)
Visualization in Scientific Computing, pp.94-108, IEEE
Computer Society Press, California.
Crawley B.G., 1975, Automatic contouring on the Gestalt
Photomapper: testing and evaluation, American Society of
Photogrammetry, Proceedings of Orthophoto Workshop III,
San Antonio, Texas, pp.1-11.
Crettez J.P., 1980, A pseudo-cosine transform for
hexagonal tessellation with an heptarchical organization,
IEEE Computer Society, Proceedings 5th International
Conference on Pattern Recognition, Vol.1, pp.192-194.
Crewe I., 1988, Voting patterns since 1959, Contemporary
Record, Vol.2, No.4, pp.2-6.
Crewe I., Särlvik B. & Alt J., 1977, Partisan Dealignment
in Britain 1964-1974, British Journal of Political Science,
Vol.7, pp.129-190.
Cromley R.G., 1983, A procedure for shading class interval
regions, AutoCarto 6 (Proceedings of the 6th International
Symposium on Computer-Assisted Cartography), Vol.2,
pp.435-442.
Cromley R.G., 1986, The commonality of computer-assisted
procedures for constructing choropleth and isarithmic
maps, The Cartographic Journal, Vol.23, pp.35-41.
Cromley R.G., Raitz K. & Ulack R., 1981, Automated
cognitive mapping, Cartographica, Vol.18, No.4, pp.36-50.
Crompton R. & Jones G., 1989, Clerical
'proletarianization': myth or reality?, L. McDowell, P.Sarre
and C. Hamnett (eds), Divided Nation: Social and Cultural
Change in Britain, Hodder and Stoughton &The Open
University, London.
Cross D., 1988, Conterurbanisation in England and Wales:
the migration evidence, Occasional Paper No.29,
Department of Geography, King's College London.
Bibliography
Crothers J.H., 1987, On the graphical presentation of
quantitative data: a postscript, Field Studies, Vol.6,
pp.685-693.
Crow F., 1987, Abstracts from the 1986 workshop on
interactive 3D graphics, Computer Graphics (USA),
Vol.21, No.1, pp.3-7.
Csillag F., 1987, Mapping the variability of soils: a
Cartographer's approach, AutoCarto 8 (Proceedings of the
8th International Symposium on Computer-Assisted
Cartography), pp.155-164 .
Cuff D.J., 1972, Value versus chroma in color schemes on
quantitative maps, Canadian Cartographer, Vol.9, No.2,
pp.134-140.
Cuff D.J., 1974, Impending conflict in color guidelines for
maps of statistical surfaces, Canadian Cartographer,
Vol.11, No.1, pp.54-58.
Cuff D.J., 1975, Conflicting goals in choosing colors for
quantitative maps, Auto-carto 2 (Proceedings of the
International Symposium on Computer-Assisted
Cartography), pp.286-288.
Cuff D.J., 1989, Human cartography (review), The
American Cartographer, Vol.16, No.1, pp.52-53.
Cuff D.J. & Bieri K.R., 1979, Ratios and absolute amounts
conveyed by a stepped statistical surface, The American
Cartographer, Vol.6, No.2, pp.157-168.
Cuff D.J. & Mattson M.T., 1982, Thematic maps: their
design and production, Methuen, New York.
Cuff D.J., Pawling J.W. & Blair E.T., 1984, Nested valueby-area cartograms for symbolizing land use And other
proportions, Cartographica, Vol.21, No.4, pp.1-8.
Cunningham S., Brown J.R. & McGrath M., 1990,
Visualization in science and engineering education, G.M.
Nielson, B. Shriver & L.J. Rosenblum (eds) Visualization
in Scientific Computing, pp.48-57, IEEE Computer Society
Press, California.
Curran J.P., 1967, Cartographic relief portrayal, Canadian
Cartographer, Vol.4, No.1, pp.28-38.
192
Dale R.S., 1989, The relationship of mental rotation to the
effectiveness of three-dimensional computer models and
planar projections for visualizing topography, PhD No.
8921422, University of Pittsburgh.
Dalton J., Billingsley J., Quann J. & Bracken P., 1979,
Interactive color map displays of domestic information,
ACM Computer Graphics, Vol.13, No.2, pp.226-233.
Dangermond J., 1976, A classification of alternative
automated mapping techniques, Proceedings of the
Workshop on Automated Cartography and Epidemiology,
National Center for Health Statistics, U.S. Department of
Health, pp.34-42.
Dangermond J. & Morehouse S., 1987, Trends in
hardware for geographic information systems, AutoCarto 8
(Proceedings of the 8th International Symposium on
Computer-Assisted Cartography), pp.380-385 .
Dangermond J. & Smith L.K., 1988, Geographic
information systems and the revolution in cartography: The
nature of the role played by a commercial organization,
The American Cartographer, Vol.15, No.3, pp.301-310.
Dannatt L.K., 1981, The evaluation of color schemes for bipolar choropleth maps, Msc, Department of Geography,
University of South Carolina.
Davidson C., 1988, Super graphics applications on the
macintosh, Electronic Imaging '88, IGC, Massachusetts.
Davies H., 1990, 1991 Census-digitisation, BURISA,
Vol.92, pp.13-15.
Davis J.C., 1975, Contouring algorithms, Auto-carto 2
(Proceedings of the International Symposium on ComputerAssisted Cartography), pp.352-359.
Davis N. & Walker C., 1975, Migrants entering and
leaving the UK, 1964-74, Population Trends, Vol.1, pp.2-5.
Davis P.J., 1974, Visual geometry, computer graphics and
theorems of perceived type, Proceedings of Symposia in
Applied Mathematics Vol.20, The Influence of Computing
on Mathematical Research and Education, American
Mathematical Society, Providence, Rhode Island.
Curtice J., 1988, North and South: the growing divide,
Contemporary Record, Vol.2, No.4, pp.7-8.
Dawsey C.B., 1989, Two-variable color mapping on a
microcomputer, Auto Carto 9, pp.15-20, Baltimore,
Maryland.
Curtis S. & Mohan J., 1989, The geography of ill health
and health care, J. Lewis & A. Townsend (eds), The NorthSouth divide: Regional change in Britain in the 1980s, Paul
Chapman, London.
De Gree M., & McCausland R.G., 1985, Digital elevation
model image display and editing, AutoCarto 7 (Proceedings
of the 7th International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.142-151.
Cuzick J. & Edwards R., 1990, Spatial clustering for
inhomogeneous populations, Journal of the Royal
Statistical Society (B), Vol.52, No.1, pp.73-104.
De Weert C.M.M., 1986, Superimposition of colour
information, Color Research and Application (Supplement),
Vol.11 pp.21-26.
D'Arcy J., 1985, Learning pascal after basic, B.Shackel
(ed), Human-Computer Interaction- Interact '84,
Proceedings of the IFIP Conference,, Elsevier Science
Publishers, Oxford.
Dean A.G., 1976, Population-based spot maps: an
epidemiologic technique, The American Journal of Public
Health, Vol.66, No.10, pp.988-989.
Dale L., 1971, Cartographic representation of journey-towork movements - 1971 canadian census, Working paper
(Demographic and Socio-economic Series) No.8, Statistics
Canada, Ottawa.
DeFanti T.A., Brown M.D. & McCormick B.H., 1989,
Visualization: expanding scientific and engineering
research opportunities, Computer, Vol.22, No.8, pp.12-25.
193
DeFanti T.A., Brown M.D. & McCormick B.H., 1990,
Visualization: expanding scientific and engineering
research opportunities, G.M. Nielson, B. Shriver & L.J.
Rosenblum (eds) Visualization in Scientific Computing,
pp.32-47, IEEE Computer Society Press, California.
Degani A., 1971, Towards an automated atlas of population,
PhD, University of Minnesota.
DeLucia A.A. & Snyder J.P., 1986, An innovative world
map projection, The American Cartographer, Vol.13, No.2,
pp.165-167.
Denby B., 1988, Neural networks and cellular automata in
experimental high energy physics, Computer Physics
Communications, Vol.49, No.3, pp.429-448.
Denes A., 1979, Isometric systems in isotropic space: map
projections: from the study of distortions series 1973-1979,
Visual Studies Workshop Press, New York.
Denham C.M., 1986, Tesseral arithmetic in hardware,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.133-135.
Denham C.M., Holroyd F.C. & Johnston J.H., 1986,
Tessellations and pixel addressing systems for image
processing: tentative results, S.B.M. Bell and B.M. Diaz
(eds) Spatial Data Processing using Tesseral Methods,
pp.87-98.
Denham J.C. & Rhind D., 1983, The 1981 Census and its
results, D. Rhind (ed) A Census User's Handbook,
Methuen, London.
Dent B.D., 1972a, Visual organization and thematic map
communication, Association of American Geographers,
Vol.62, pp.79-93.
Dent B.D., 1972b, A note on the importance of shape in
cartogram communication, The Journal of Geography,
Vol.71, No.7, pp.393-401.
Dent B.D., 1975, Communication aspects of value-by-area
cartograms, The American Cartographer, Vol.2, No.2,
pp.154-168.
Dent B.D., 1977, The nature of maps: essays toward
understanding maps and mapping (review), The American
Cartographer, Vol.4, No.2, pp.174-176.
Dent B.D., 1985, Principles of thematic map design,
Addison - Wesley, Reading, MA..
Dent B.D., 1987, Continental shapes on world projections:
the design of a poly-centred oblique orthographic world
projection, The Cartographic Journal, Vol.24, pp.117-124.
Denver D., 1989, Elections and voting behaviour in Britain,
Philip Allan, New York.
Denver D., 1990, The return of two party politics,
Contemporary Record, Vol.4, No.1, pp.2-5.
Department of the environment, Welsh Office, 1974,
Local government in England and Wales: a guide to the
new system, H.M.S.O., London.
Department of Transport, 1981, TAM: Traffic appraisal
manual, Department of Transport, methods division,
HMSO, London.
Bibliography
Depuydt F., 1983, The equivalent quintuple projection, The
International Yearbook of Cartography, Vol.23, pp.63-74.
Depuydt F., 1988, Optimization of the cartographic
communication process in choropleths by computer
assistance, International Yearbook of Cartography,
Vol.XXVIII, pp.203-211.
Devaney R.L., 1989, Film and video as a tool in
mathematical research, The Mathematical Intelligencer,
Vol.11, No.2, pp.33-38.
Devis T., 1983, People changing address: 1971 and 1981,
Population Trends, Vol.32, pp.15-20.
Devis T., 1984, Population movements measured by the
NHS central register, Population Trends, Vol.36, pp.19-24.
Devis T. & Mills I., 1986, A comparison of migration data
from the NHS central register and the 1981 census,
Population Statistics Division, Occasional Paper 35, Office
of Population Censuses and Surveys.
Devis T. & Southworth N.R., 1984, The study of internal
migration in Great Britain, A.J. Boyce (ed), Migration and
Mobility: Biosocial aspects of human movement, Taylor &
Francis, London.
Dewdney A.K., 1990, Computer recreations: the cellular
automata programs that create wireworld, rugworld and
other diversions, Scientific American, Vol.262, No.1,
pp.136-139.
Dewdney J.C., 1968, Age-structure maps of the British
Isles, Institute of British Geographers, Transactions no.43,
April 1968, George Philip & Son, London.
Dewdney J.C., 1983, Censuses past and present, D. Rhind
(ed) A Census User's Handbook, Methuen, London.
Di Fraia L. & Ronchi L.R., 1986, Visual depth of focus
measured for various colored displays, Color Research and
Application (Supplement), Vol.11 pp.52-56.
Diamond I., 1989, Education and changing numbers of
young people, H. Joshi (ed) The Changing Population of
Britain, Basil Blackwell, Oxford.
Diamond I. & Clarke S., 1989, Demographic patterns
amongst Britain's ethnic groups, H. Joshi (ed) The
Changing Population of Britain, Basil Blackwell, Oxford.
Diaz B.M., 1986a, Rules and hardware implementation,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.129-133.
Diaz B.M., 1986b, Tabular tesseral methods and
calculators, S.B.M. Bell and B.M. Diaz (eds) Spatial Data
Processing using Tesseral Methods, pp.99-109.
Diaz B.M., 1986c, Tesseral addressing and arithmetic overview, S.B.M. Bell and B.M. Diaz (eds) Spatial Data
Processing using Tesseral Methods, pp.1-10.
Diaz B.M. & Bell S.B.M., 1986, Spatial data processing
using tesseral methods, (Collected papers from Tesseral
Workshops 1 and 2) NERC, NERC Unit for Thematic
Information Systems.
Dickinson G.C., 1973, Statistical mapping and the
presentation of statistics, Edward Arnold, New York.
Bibliography
Dickinson R.E., 1957a, The geography of commuting in
West Germany, Annals of the Association of American
Geographers, Vol.49, pp.443-456.
Dickinson R.E., 1957b, The geography of commuting: the
Netherlands and Belgium , The Geographical Review,
Vol.47, No. 4, pp.521-538.
Dickinson R.R., 1989, Interactive 4-D visualization of
fields, Technical Report CS-89-5, Department of Computer
Science, University of Waterloo, Ontario, Canada.
Dietrich F., 1987, Visions of mind, Computer Graphics
(USA), Vol.21, No.1, pp.11-12.
194
Dobson M.W., 1983b, Human factors in the cartographic
design of real-time, color displays, AutoCarto 6
(Proceedings of the 6th International Symposium on
Computer-Assisted Cartography), Vol.1, pp.421-426.
Dobson M.W., 1984, Effective color display for map task
performance in a computer environment, Proceedings of
the International Symposium on Spatial Data Handling,
pp.332-348, August, Zurich, Switzerland.
Dobson M.W., 1985, The future of perceptual cartography,
Cartographica, Vol.22, No.2, pp.27-43.
Doctor L.J. & Torborg J.G., 1981, Display techniques for
octree-encoded objects, IEEE Computer Graphics and
Applications, Vol.1, No.7, pp.29-38.
Dikau R., 1989, The application of a digital relief model to
landform analysis in geomorphology, J. Raper (ed), GIS:
Three Dimensional Applications in Geographic Information
Systems, Taylor & Francis, London.
Domany E., 1988, Neural networks: a biased overview,
Journal of Statistical Physics, Vol.51, Nos.5/6, pp.743-775.
Dincer T., Child S. & Khupe B., 1987, A simple
mathematical model of a complex hydrological system Okavango swamp, Botswana, Journal of Hydrology,
Vol.93, pp.41-65.
Donoho A.W., Donoho D.L. & Gasko M., 1988, Macspin:
dynamic graphics on a desktop computer, W.S. Cleveland
& M.E. McGill (eds) Dynamic Graphics For Statistics,
Wandsworth, California.
Dixon O.M., 1972, Methods and progress in choropleth
mapping of population density, The Cartographic Journal,
Vol.9, No.1, pp.19-29.
Donoho D.L., Huber P.J., Ramos E. & Thoma H.M.,
1988, Kinematic display of multivariate data, W.S.
Cleveland & M.E. McGill (eds) Dynamic Graphics For
Statistics, Wandsworth, California.
Dixon O.M., 1979, A map of population distribution in the
Portsmouth area, The Cartographic Journal, Vol.16, No.1,
pp.9-13.
Dobkin D.P. & Panduranga E.S., 1990, A numerical
method for rendering spherical reflections, Visualization
'90, Proceedings of the First IEEE Conference on
Visualization, pp.289-297, October, San Francisco,
California.
Dobson M.W., 1974, Refining legend values for
proportional circle maps, Canadian Cartographer, Vol.11,
No.1, pp.45-53.
Dobson M.W., 1975a, The map - in the mind's eye, Autocarto 2 (Proceedings of the International Symposium on
Computer-Assisted Cartography), pp.225-231.
Dobson M.W., 1975b, Symbol-subject matter relationships
in thematic cartography, The Canadian Cartographer,
Vol.12, No.1, pp.52-67.
Dobson M.W., 1979, Visual information processing during
cartographic communication, The Cartographic Journal,
Vol.16, No.1, pp.14-20.
Dobson M.W., 1980a, Benchmarking the perceptual
mechanism for map-reading tasks, Cartographica, Vol.17,
No.1, pp.88-100.
Dobson M.W., 1980b, Unclassed choropleth maps: a
Comment, The American Cartographer, Vol.7, No.1, pp.7880.
Dobson M.W., 1983a, A high resolution microcomputer
based color system for examining the human factors
aspects of cartographic displays in a real-time user
environment, AutoCarto 6 (Proceedings of the 6th
International Symposium on Computer-Assisted
Cartography), Vol.1, pp.352-360.
Dooley D. & Cohen M.F., 1990, Automatic Illustration of
3D geometric models: surfaces, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.307-314, October, San Francisco, California.
Dougenik J.A., Chrisman N.R. & Niemeyer D.R., 1985,
An algorithm to construct continuous area cartograms,
Professional Geographer, Vol.37, No.1, pp.75-81.
Dougenik J.A., Niemeyer D.R. & Chrisman N.R., 1983, A
computer algorithm to build continuous area cartograms,
Harvard Computer Graphics Week, Hardvard University
Graduate School.
Douglas D.H., 1986, Experiments to locate ridges and
channels to create a new type of digital elevation model,
The Canadian Surveyor, Vol.41, No.3, pp.373-406.
Downing J.A. & Zoraster S., 1982, An adaptive grid
contouring algorithm, AutoCarto 5 (Proceedings of the 5th
International Symposium on Computer-Assisted
Cartography), pp.249-256.
Downs R.M. & Stea D., 1977, Maps in minds: reflections
on cognitive mapping, Harper and Row, .
Doytsher Y. & Shmutter B., 1982, Grids of elevations and
topographic maps, AutoCarto 5 (Proceedings of the 5th
International Symposium on Computer-Assisted
Cartography), pp.257-265.
Drebin R.A., Carpenter L. & Hanrahan P., 1988, Volume
rendering, Computer Graphics, Vol.22, No.4, pp.65-74.
van Dreil J.N., 1989, Three-dimensional display of geologic
data, J. Raper (ed), GIS: Three Dimensional Applications
in Geographic Information Systems, Taylor & Francis,
London.
195
Drewett R., Goddard J., Spence N., Connock C.,
Pinkham R. & Kennett S., 1976, British cities: urban
population and employment trends 1951-1971, Urban
Affairs and Commercial Property Directorate, Department
of the Environment.
Drummond J., 1987, A framework for handling error in
geographic data manipulation, ITC Journal, Vol.1, pp.7382.
Dubuc O. & Goodchild M., 1987, A model of error for
choropleth maps, with applications to geographic
information systems , AutoCarto 8 (Proceedings of the 8th
International Symposium on Computer-Assisted
Cartography), pp.165-174 .
Dudycha D.J., 1981, The impact of computer cartography,
Cartographica, Monograph 27. L. Guelke (ed) Maps in
Modern Geography - Geographical Perspectives on the
New Cartography, Vol.18, No.2, pp.116-150.
Duncan O.D., Cuzzort R.P., & Duncan B., 1961,
Statistical geography: problems in analyzing areal data,
The Free Press of Glencoe, Illinois.
Duncan P., 1980, Illustration of oceanic data (I): scalers,
Cartographica, Monograph 25. A.J. Kerr (ed) The
Dynamics of Oceanic Cartography, Vol.17, No.2, pp.25-36.
Duncan P. & Oldson J., 1980, Illustration of oceanic data
(II): vectors, Cartographica, Monograph 25. A.J. Kerr (ed)
The Dynamics of Oceanic Cartography, Vol.17, No.2,
pp.37-57.
Dungan W., 1979, A terrain and cloud computer image
generation model, Computer Graphics, Vol.13, No.2,
pp.143-147.
Dunleavy P., 1983, Voting and the electorate, Drucker H.
(ed) Developments in British Politics, Macmillan, London.
Dunn C.F., Diaz B.M. & Taylor M.J., 1986, Preliminary
work on the development of a hardware tesseral graphics
workstation, S.B.M. Bell and B.M. Diaz (eds) Spatial Data
Processing using Tesseral Methods, pp.137-148.
Dunn R., 1987a, Variable-width framed rectangle charts for
statistical mapping, American Statistician, Vol.41, No.2,
pp.153-156.
Bibliography
Dutton G., 1982, Bivariate construction of equal-class
thematic maps, AutoCarto 5 (Proceedings of the 5th
International Symposium on Computer-Assisted
Cartography), pp.285-296.
Dutton G., 1983, Geodesic modelling of planetary relief,
AutoCarto 6 (Proceedings of the 6th International
Symposium on Computer-Assisted Cartography), Vol.2,
pp.186-201.
Dutton G., 1989, Plannetary modelling via hierarchical
tessellation, Auto Carto 9, pp.462-471, Baltimore,
Maryland.
Dutton G., 1990, Locational properties of quaternary
triangular meshes, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.2,
pp.901-910.
Eagles M. & Erfle S., 1989, Community cohesian and voter
turnout in English parliamentary constituencies, British
Journal of Political Science, Vol.19, pp.115-125.
Easterfield M., Newell R.G. & Theriault D., 1991,
Modelling spatial and temporal information, EGIS '91
Proceedings, pp.294-303, Brussels, Belgium.
Eastman J.R., 1985a, Algorithms for graphics and image
processing (review), Cartographica, Vol.22, No.1, pp.113115.
Eastman J.R., 1985b, Graphic organization and memory
structures for map learning, Cartographica, Vol.22, No.1,
pp.1-20.
Eastman J.R., 1989, Pushbroom algorithms for calculating
distances in raster grids, Auto Carto 9, pp.288-297,
Baltimore, Maryland.
Eastman J.R. & Warren S., 1987, IDRISI: a collective
geographic analysis project, AutoCarto 8 (Proceedings of
the 8th International Symposium on Computer-Assisted
Cartography), pp.421-430 .
Eastman J.R., Nelson W. & Shields G., 1981, Production
considerations in isodensity mapping, Cartographica,
Vol.18, No.1, pp.24-30.
Eco U., 1986, Travels in hyperreality, Harcourt, Brace and
Jovanovich, San Diego.
Dunn R., 1987b, Analysing spatial time series of local
unemployment: a graphical approach using principle
components analysis and seasonal adjustment procedures,
Environment and Planning A, Vol.19, No.2, pp.225-246.
Editura Academiei Republicii Socialiste Romania, 1975,
Atlasul republicii socialiste romania, Romanian National
Atlas, Bucharest.
Dunn R., 1988, Framed rectangle charts or statistical maps
with shading, American Statistician, Vol.42, No.2, pp.123129.
El-Leithy N. & Newcomb R.W., 1989, Neural networks
(editorial), IEEE Transactions on Circuits and Systems,
Vol.36, No.5, pp.641-642.
Durrett H.J., 1987, Color and the computer, Academic
Press, Inc., Orlando, Florida.
Elcock E.W., Gargantini I. & Walsh T.R., 1986,
Triangular decomposition, S.B.M. Bell and B.M. Diaz
(eds) Spatial Data Processing using Tesseral Methods,
pp.69-85.
Durrett H.J. & Stimmel D.T., 1987, Color and the
instructional use of the computer, H.J. Durrett (ed) Color
and the Computer, Ch.13, pp.241-254.
Dutton G., 1978, American graph fleeting: United States
population growth 1790-1970, Laboratory for Computer
Graphics and Spatial Analysis, Graduate School of Design,
Hardvard University.
Elroi D., 1988, GIS and schematic maps: a new symbiotic
relationship, GIS/LIS'88 proceedings - Accessing the
World, San Antonio - Texas, pp.220-229.
Elsis R.E., 1987, The perception of three-dimensional
cartographic representations, M.A., New York City
University.
Bibliography
England N., 1988, Converting a workstation to a super
graphics workstation, Electronic Imaging '88, IGC,
Massachusetts.
Entrikin J.N., 1989, Place, region and modernity, J.A.
Agnew & J.S. Duncan (eds) The Power of Place: Bringing
together geographical and sociological imaginations,
Chapter 3, Unwin Hyman, Boston.
196
Falcidieno B., Gambaro C. & Sinigaglia P., 1983,
Automatic colouring of maps according to the elevation,
AutoCarto 6 (Proceedings of the 6th International
Symposium on Computer-Assisted Cartography), Vol.2,
pp.426-434.
Falk T. & Abler R., 1980, Intercommunications, distance,
and geographical theory, Geografiska Annaler, Series B,
Vol.62, pp.59-67.
Eriksson G.A., 1970, Methods in traffic planning: the
growing demand for transport presupposes traffic
planning, Proceedings of the International Conference on
Transportation Maps, pp.25-35, Budapest.
Farah M.J., 1988, Is visual imagery really visual?
overlooked evidence from neuropsychology, Psychological
Review, Vol.95, No.3, pp.307-317.
Ermisch J., 1989, Divorce: economic antecedents and
aftermath, H. Joshi (ed) The Changing Population of
Britain, Basil Blackwell, Oxford.
Farhoosh H. & Schrack G., 1986, CNS-HLS mapping
using fuzzy sets, IEEE Computer Graphics and
Applications, Vol.6, No.6, pp.28-35.
Ermisch J. & Maclennan D., 1987, Housing policies,
markets, and urban economic change, V.A.Hauser (ed)
Critical Issues in Urban Economic Development, Chapter 6,
Clarendon Press, Oxford.
Farrell E.J. & Zappulla R.A., 1989, Three-dimensional
data visualization and biomedical applications, CRC
Critical Reviews in Biomedical Engineering, Vol.16, No.4,
pp.323-363.
Ertl T., Geyer F., Herold H., Kraus U., Niemeier R.,
Nollert H.P., Rebetsky A., Ruder H. & Zeller G., 1989,
Visualization in Astrophysics, Eurographics '89, Hamburg,
pp.149-158.
Farrell E.J. (ed), 1990, Extracting meaning from complex
data: processing, display, interaction, SPIE Proceedings:
The International Society for Optical Engineering , 14-16
February, Santa Clara, California.
Evans D., 1973, Keynote address, Why is Computer
Graphics Always a Year Away?, Computer Graphics,
Quarterly report of Siggraph-ACM, Vol.8, No.1, pp.5-11.
Fay D.Q.M. & Hann E.C., 1986, Applications of measure
polytope models to computer vision, S.B.M. Bell and B.M.
Diaz (eds) Spatial Data Processing using Tesseral Methods,
pp.411-422.
Evans I.S., 1983a, Univariate analysis: presenting and
summarizing single variables, D. Rhind (ed) A Census
User's Handbook, Methuen, London.
Evans I.S., 1983b, Bivariate and multivariate analysis:
relationships between census variables, D. Rhind (ed) A
Census User's Handbook, Methuen, London.
Evans I.S., Catterall J.W. & Rhind D.W., 1975, Specific
transformations are necessary, Census Research Unit
Working paper No.2, University of Durham.
Everitt B.S., 1978, Graphical techniques for multivariate
data, Heinemann Educational Books, London.
Ewing G., 1974, Multidimensional scaling and time-space
maps, Canadian Geographer, Vol.18, No.2, pp.161-167.
Ewing G.O. & Wolfe R., 1977, Surface feature
interpolation on two-dimensional time-space maps,
Environment and Planning A, Vol.9, pp.429-437.
Exler R.D., Selden D.D., Johnson A., Marder D. &
Beaubier J., 1985, Mapping cancer mortality rates with
computer graphics, Technical papers of the ACSM-ASPRS
Fall Convention, September, Indianapolis, Indiana, pp.4553.
Eyton J.R., 1986, Computer generated templates for the
construction of landform relief models, The American
Cartographer, Vol.13, No.4, pp.345-351.
Eyton J.R., 1991, Rate-of-change maps, Cartography and
Geographic Information Systems, Vol.18, No.2, pp.87-103.
Fabsitz R.R. & Feinleib M., 1976, NHLBI mapping project,
Proceedings of the Workshop on Automated Cartography
and Epidemiology, National Center for Health Statistics,
U.S. Department of Health, pp.69-73.
Feinberg B.M. & Franklin C.A., 1975, Social graphics
bibliography, Bureau of Social Science Research, Inc.,
Washington D.C..
Fekete G., 1990, Rendering and managing spherical data
with sphere quadtrees, Visualization '90, Proceedings of the
First IEEE Conference on Visualization, pp.176-186,
October, San Francisco, California.
Feldman J.A., Fanty M.A. & Goddard N.H., 1988,
Computing with structured neural networks, Computer,
Vol.21, No.3, pp.91-103.
Feldman J.J., 1976, Geographic differentials: analytic
concerns, Proceedings of the Workshop on Automated
Cartography and Epidemiology, National Center for Health
Statistics, U.S. Department of Health, pp.43-47.
Fellows J.D. & Ragan R.M. , 1985, The role of cell size in
hydrology orientated geographic information systems,
Hydrological Applications of Space Technology
(Proceedings of Workshop) , pp.453-460.
Fels J.E., 1990, Bivariate color mapping techniques for
topographic visualization and their applications in site
appraisal, Proceedings GIS/LIS '90, pp.780-788, Anaheim
California.
Ferns D.C. & Press N.P., 1988, Microcomputers and mass
storage devices for image processing, J.P. Muller (ed)
Digital Image Processing in Remote Sensing, Taylor and
Francis, London, pp.105-121.
Fielding A.J., 1989, Inter-regional migration and social
change: a study of South East England based upon data
from the longtitudinal study, Trans. Inst. of British
Geographers, Vol.14, pp.24-36.
197
Bibliography
Fielding A.J., 1991, Migration and social change, Stillwell,
Rees and Boden (eds), Migration Processes and Patterns:
vol. 2, Population Redistribution in the 1980s, Belhaven
Press, London.
Foley T.A. & Lane D.A., 1990, Visualization of irregular
multivariate data, Visualization '90, Proceedings of the
First IEEE Conference on Visualization, pp.247-254,
October, San Francisco, California.
Fienberg S.E., 1979, Graphical methods in statistics, The
American Statistician, Vol.33, No.4, pp.165-178.
Foley T.A., Lane D.A., Nielson G.M. & Ramaraj R.,
1990, Visualizing functions over a sphere, IEEE Computer
Graphics and Applications, Vol.10, No.1, pp.32-40.
Finamore P.M., 1982, The value of cartograms as
communication devices, Msc No.60933, Indiana University,
Bloomington.
Findlay A.M., 1991, Population geography, Progress in
Human Geography, Vol.15, No.1, pp.64-72.
Fisher H.T., 1982, Mapping information: the graphic
display of quantitative information, Abt Books, Cambridge,
Massachusetts.
Fisherkeller M.A., Friedman J.H. & Tukey J.W., 1988,
PRIM-9: an interactive multidimensional data display and
analysis system, W.S. Cleveland & M.E. McGill (eds)
Dynamic Graphics For Statistics, Wandsworth, California.
Fitzpatrick J.M., 1988, A method for calculating fluid flow
in time dependent density images, Electronic Imaging '88,
Anaheim, California, pp.347-352.
Flannery J.J., 1971, The relative effectiveness of some
common graduated point symbols in the presentation of
quantitative data, Canadian Cartographer, Vol.8, No.2,
pp.96-109.
Flowerdew R. & Green M., 1989a, Statistical methods for
inference between incompatible zonal systems, M.
Goodchild and S. Gopal (eds), The Accuracy of Spatial
Databases, Taylor and Francis, London.
Flowerdew R. & Green M., 1989b, Statistical methods for
inference between incompatible zonal systems, Research
Report No.1, North West Regional Research Laboratory,
Lancaster University.
Flowerdew R. & Green M., 1990, Inference between
incompatible zonal systems using the EM algorithm,
Research Report No.6, North West Regional Research
Laboratory, Lancaster University.
Flowerdew R. & Salt J., 1979, Migration between labour
market areas in Great Britain, 1970-1971, Regional
Studies, Vol.13, pp.211-231.
Flury B. & Riedwyl H., 1981, Graphical representation of
multivariate data by means of asymmetrical faces, Journal
of the American Statistical Association, Vol.76, No.376,
pp.757-765.
Foley D.L., 1953, Census tracts and urban research, Journal
of the American Statistical Association, Vol.48, No. 264,
pp.733-742.
Foley J.D., van Dam A., Feiner S.K. & Hughes J.F., 1990,
Computer graphics: principles and practise, AddisonWesley , Reading.
Foley J.E. & Cohen A.J., 1982, Representation of the
vertical dimension in cognitive maps, EDRA:
Environmental Design Association, Vol.13, pp.100-104.
Forbes J., 1984, Problems of cartographic representation of
patterns of population change, The Cartographic Journal,
Vol.22, pp.93-102.
Forbes J. & Robertson I., 1979, Changes in population
distribution in Glasgow 1958-1975, Scottish Geographical
Magazine, Vol.95, No.3, pp.154-158.
Forbes J. & Robertson I.M.L., 1967, Population
enumeration on a grid square basis: the census of Scotland,
a test case, The Cartographic Journal, Vol.4, No.1, pp.2937.
Forer P., Poiker T., Penny J. & Deeker G., 1990, New
technology and new concepts for map use, Proceedings of
the 4th International Symposium on Spatial Data Handling,
Zurich, Vol.1, pp.335-344.
Forrest D., 1985, Contouring with small computers: a
review, AutoCarto 7 (Proceedings of the 7th International
Symposium on Computer-Assisted Cartography),
Washington D.C., pp.190.
Forrest D. & Castner H.W., 1985, The design and
perception of point symbols for tourist maps, The
Cartographic Journal, Vol.22, pp.11-19.
Forster F., 1966, Use of a demographic base map for the
presentation of areal data in epidemiology, British Journal
of Preventative and Social Medicine, Vol.20, pp.165-171.
Forster F., 1972, Use of a demographic base map for the
presentation of areal data in epidemiology, N.D.
McGlashan (ed) Medical Geography: Techniques and Field
Studies, Methuen & Co. Ltd., London.
Fothergill S. & Gudgin G., 1982, Unequal growth: urban
and regional change in the UK, Heinemann Books,
London.
Fothergill S. & Gudgin G., 1983, Trends in regional
manufacturing employment: the main influences, J.B.
Goddard & A.G. Champion (eds) The Urban and Regional
Transformation of Britain, Methuen, London.
Fotheringham A.S., 1989, Scale-independent spatial
analysis, M. Goodchild & S. Gopal (eds) The Accuracy of
Spatial Databases, Chapter 19, Taylor & Francis, New
York.
Fournier A. & Milligan T., 1985, Frame buffer algorithms
for stochastic models, IEEE Computer Graphics and
Applications, Vol.5, No.10, pp.40-46.
Fox A.J., 1990, The work of the national health service
central register, Population Trends, Vol.62, pp.29-32.
Fraczek I., 1984, Cartographic studies on graduated symbol
map perception, The International Yearbook of
Cartography, Vol.24, pp.75-84.
Franke H.W., 1987, Computer graphics as artistic
expression, Computer Graphics (USA), Vol.21, No.1, pp.811.
Bibliography
Frederick C. & Schwartz E.L., 1990, Conformal image
warping, Vision, Vol.10, No.2, pp.54-61.
Freeman H., 1988, An expert system for the automatic
placement of names on a geographic map, Information
Sciences, Vol.45, pp.367-378.
Freeman S., 1991, Envisioning information: review, IEEE
Computer Graphics and Applications, Vol.11, No.1,
pp.113-114.
Frenkel K.A., 1988a, Computers and elections,
Communications of the ACM, Vol.31, No.10, pp.11761183.
Frenkel K.A., 1988b, The art and science of visualizing
data, Communications of the ACM, Vol.31, No.2, pp.110121.
Friedman J.H., McDonald J.A. & Stuetzle W., 1988, An
introduction to real time graphical techniques for analyzing
multivariate data, W.S. Cleveland & M.E. McGill (eds)
Dynamic Graphics For Statistics, Wandsworth, California.
Friis H.R., 1974, Statistical cartography in the United
States prior to 1870 and the role of Joseph C.G. Kennedy
and the U.S. census office, The American Cartographer,
Vol.1, No.2, pp.131-157.
Frost M.E. & Spence N.A., 1981, Unemployment,
structural economic change and public policy in British
regions, Progress in Planning, vol 16, pp. 1-130, Pergamon
Press, Oxford.
Frost M.E. & Spence N.A., 1983, Unemployment change,
J.B. Goddard & A.G. Champion (eds) The Urban and
Regional Transformation of Britain, Methuen, London.
Fuchs H., Levoy M. & Pizer S.M., 1990, Interactive
visualization of 3D medical data, G.M. Nielson, B. Shriver
& L.J. Rosenblum (eds) Visualization in Scientific
Computing, pp.140-146, IEEE Computer Society Press,
California.
Fuhrmann D.R., 1988, Quadtree traversal algorithms for
pointer-based and depth-first representations, IEEE
Transactions on Pattern Analysis and Machine Intelligence,
Vol.10, No.6, pp.955-960.
Fujimoto A. & Iwata K., 1983, Jag-free images on raster
displays, IEEE Computer Graphics and Applications,
Vol.3, No.9, pp.26-34.
Fujimoto A., Tanaka T. & Iwata K., 1986, ARTS:
accelerated ray-tracing system, IEEE Computer Graphics
and Applications, Vol.6, No.4, pp.16-26.
Fujiwara T., Nishihara S. & Hirose K., 1988, Color flowvisualization photography and digital image processing
techniques, JSME International Journal Series II, Vol.31,
No.1, pp.39-46.
Fullard H., Darby H.C., Willett B.M. & Gaylard D. (eds),
1981, The University Atlas (21st edition), George Philip &
Son, Britain.
Fuson R., 1970, Fundamental place-name geography,
WM.C.Brown Company, Dubuque, Iowa.
Gahegan M. & Hogg J., 1986, A pilot geographical
information system based on linear quadtrees and a
relational database for regional analysis, S.B.M. Bell and
B.M. Diaz (eds) Spatial Data Processing using Tesseral
Methods, pp.213-232.
198
Galbraith J.W. & Rae N.C., 1989, A test of the importance
of tactical voting: Great Britain, 1987, British Journal of
Political Science, Vol.19, pp.126-136.
Ganter J.H., 1988, Interactive graphics: linking the human
to the model, GIS/LIS'88 proceedings - Accessing the
World, San Antonio - Texas, pp.230-239.
Gardner G.Y., 1985, Visual simulation of clouds, Computer
Graphics (USA), Vol.19, No.3, pp.297-303.
Gardner J.L. & Berenson R.J. (eds), 1989, Reader's digest
atlas of the world, Reader's Digest, Britain.
Gardner M.J., Winter P.D. & Barker D.J.P., 1984, Atlas
of mortality from selected diseases in England and Wales
1968-1978, John Wiley & Sons, Chichester.
Gardner M.J., Winter P.D., Taylor C.P. &Acheson E.D.,
1983, Atlas of Cancer Mortality in England and Wales,
1968-1978, John Wiley & Sons, Chichester.
Gargantini I., 1982, An effective way to represent
quadtrees, Communications of the ACM, Vol.25, No.12,
pp.905-910.
Gargantini I., Schrack G. & Atkinson H.H., 1988, On the
visibility of octree-based three-dimensional objects,
Electronic Imaging '88, Anaheim, California, pp.210-215.
Gargantini I., Walsh T.R. & Wu O.L., 1986, Viewing
transformations of voxel-based objects via linear octrees,
IEEE Computer Graphics and Applications, Vol.6, No.10,
pp.12-21.
Garmiz I.V., Manichev S.A., Mironov Y.A., 1988, The
readability of the map: theoretical principles, Mapping
Sciences and Remote Sensing, Vol.25, No.4, pp.276-283.
Garneau R.W., 1989, Interactive adaptive analysis of
graphic displays, PhD No.DA8820146, Boston University.
Gatrell A.C., 1974, On the complexity of maps, Papers in
Geography No.11, Department of Geography, Pennsylvania
State University.
Gatrell A.C., 1983, Distance and space: a geographical
perspective, Clarendon Press, Oxford.
Gatrell A.C., 1989, On the spatial representation and
accuracy of address-based data in the United Kingdom,
International Journal of Geographical Information Systems,
Vol.3, No.4, pp.335-348.
Gauthier C., Gravel G. & Roy A., 1987, Measuring the
dimension of surfaces: a review and appraisal of different
methods , AutoCarto 8 (Proceedings of the 8th International
Symposium on Computer-Assisted Cartography), pp.68-77.
Geddes P., 1923, A note on graphic methods, ancient and
modern, Sociological Review, Vol.15, pp.227-235.
Gelberg L.M. & Stephenson T.P., 1987, Supercomputing
and graphics in the earth and planetary sciences, IEEE
Computer Graphics and Applications, Vol.7, No.7, pp.2633.
Gerard R., 1958, Commuting and the labor market area,
Journal of Regional Science, Vol.1, No.1, pp.124-130.
199
Gerling R.W., 1990, Classification of triangular and
honeycomb cellular automata, Physica A, Vol.162, pp.196209.
Gerlot J-P., 1988, Concepts and methods in progressive
cartography, International Yearbook of Cartography,
Vol.XXVIII, pp.53-71.
German Cartographic Society, 1985, The so-called Peters
projection, The Cartographic Journal, Vol.22, pp.108-110.
Gershon N.D., 1990, Visualization and three-dimensional
image processing of positron emission tomography (PET)
brain images, Visualization '90, Proceedings of the First
IEEE Conference on Visualization, pp.144-149, October,
San Francisco, California.
Getis A., 1974, Representation of Spatial point pattern
processes by polya models, Proceedings of the 1972
meeting of the IGU commission on Quantitative
Geography, ed M.Yeates, McGill-Queen's University Press.
Gibson A., 1988, We the people: an atlas of America's
ethnic diversity (review), Cartographica, Vol.25, No.4,
pp.86-88.
Gibson L. & Lucas D., 1982, Spatial data processing using
generalized balanced ternary, IEEE Computer Society,
Proceedings of Conference on Pattern Recognition and
Image Processing, Las Vegas, Nevada, pp.566-571.
Gilbert M., 1982, Atlas of the holocaust, Michael Joseph,
London.
Gill G.A., 1988, Experiments in the ordered perception of
coloured cartographic line symbols, Cartographica, Vol.25,
No.4, pp.36-49.
Gillard Q., 1979, Places in the news: use of cartograms in
introductory geography courses, Journal of Geography,
Vol.78, No.3, pp.114-115.
Gillespie A.E., 1983, Population and employment
decentralization and the journey to work, J.B. Goddard &
A.G. Champion (eds) The Urban and Regional
Transformation of Britain, Methuen, London.
Gilman C.R., 1981, The manual/photomechanical and other
methods for relief shading, The American Cartographer,
Vol.8, No.1, pp.41-53.
Gilmartin P. & Shelton E., 1989, Choropleth maps on high
resolution CRTs / the effect of number of classes and hue
on communication, Cartographica, Vol.26, No.2, pp.40-52.
Gilmartin P.P, 1981, The interface of cognitive and
pschophysical research in cartography, Cartographica,
Vol.18, No.3, pp.9-20.
Gilmartin P.P., 1978, Evaluation of thematic maps Using
the semantic differential test, The American Cartographer,
Vol.5, No.2, pp.133-139.
Gilmartin P.P., 1988, The design of choropleth shadings for
maps on 2- and 4-bit color graphics monitors,
Cartographica, Vol.25, No.4, pp.1-10.
Gimblett R.H. & Itami R.M., 1988, Linking GIS with video
technology to simulate environmental change, GIS/LIS'88
proceedings - Accessing the World, San Antonio - Texas,
pp.208-219.
Bibliography
Gitelman D.R. & Toga A.W., 1989, A mathematical model
and image analysis technique for calculating regional
cerebral blood flow, Computer Methods and Programs in
Biomedicine, Vol.29, No.1, pp.59-69.
Glaser S.L., 1990, Spatial clustering of hodgkin's disease in
the San Francisco bay area, American Journal of
Epidemiology, Vol.132, No.1, pp.S167-S177.
Glassner A.S., 1988, Spacetime ray tracing for animation,
IEEE Computer Graphics and Applications, Vol.8, No.3,
pp.60-70.
Glassner A.S., 1989, How to derive a spectrum from an
RGB triplet, IEEE Computer Graphics and Applications,
Vol.9, No.4, pp.95-99.
Glattre E., Finne T.E., Olesen O. & Langmark F., 1985,
Atlas of cancer incidence in Norway 1970-1979,
Landsforeningen mot kreft, Oslo.
Glickman A.S. & McShan D.L., 1987, Color displays for
medical imaging, H.J. Durrett (ed) Color and the Computer,
Ch.10, pp.189-204.
Gnanadesikan R., 1977, Methods for statistical data
analysis of multivariate observations, John Wiley and Sons,
New York.
Goddard J.B., 1983, Structural change in the British space
economy, J.B. Goddard & A.G. Champion (eds) The Urban
and Regional Transformation of Britain, Methuen, London.
Goddard J.B. & Champion A.G., 1983, The urban and
regional transformation of Britain, Methuen, London.
Goddard J.B. & Coombes M.G., 1987, The north-south
divide: local perspectives, Centre for urban and regional
Development Studies, Newcastle university.
Golay M.J.E., 1969, Hexagonal parallel pattern
transformations, IEEE Transactions on Computers, Vol.C18, No.8, pp.733-740.
Gold C.M., 1980, Triangulation-based terrain modelling where are we now?, AutoCarto IV (Proceedings of the
International Symposium on Cartography and Computing:
Applications in Health and the Environment, Virginia),
Vol.2, pp.104-111.
Gold C.M., 1983, Some problems in the mapping of
geological surfaces, AutoCarto 6 (Proceedings of the 6th
International Symposium on Computer-Assisted
Cartography), Vol.2, p.414.
Gold C.M., 1989a, Spatial adjacency - a general approach,
Auto Carto 9, pp.298-312, Baltimore, Maryland.
Gold C.M., 1989b, Surface interpolation, spatial adjacency
and GIS, J. Raper (ed), GIS: Three Dimensional
Applications in Geographic Information Systems, Taylor &
Francis, London.
Goldstein L. & Waterman M., 1988, Neighborhood size in
the simulated annealing algorithm, American Journal of
Mathematical and Management Sciences, Vol.8, Nos.3 & 4,
pp.409-423.
Goldthorpe J.H. & Hope K., 1974, The social grading of
occupations: a new approach and scale, Oxford University
Press, Oxford.
Bibliography
Golikov A.P., 1988, Mapping statistical surfaces in spatial
analysis: the example of water resource management,
Mapping Sciences and Remote Sensing, Vol.25, No.4,
pp.347-351.
Goodchild M.F., 1982, The fractional brownian process as
a terrain simulation model, Modelling and Simulation,
Vol.13, pp.113-1137.
Goodchild M.F., 1988a, Geographic information systems,
Progress in Human Geography, Vol.12, pp.560-566.
Goodchild M.F., 1988b, Stepping over the line:
technological constraints and the new cartography, The
American Cartographer, Vol.15, No.3, pp.311-319.
Goodchild M.F. & Lam N.S.N., 1980, Areal interpolation:
a variant of the traditional spatial problem, GeoProcessing, Vol.1, pp.297-312.
Goodchild M.F. & Shiren Y., 1990, A hierarchical data
structure for global geographic information systems,
Proceedings of the 4th International Symposium on Spatial
Data Handling, Zurich, Vol.2, pp.911-917.
Gordon D., 1987, On the computational power of totalistic
cellular automata, Mathematical Systems Theory, Vol.20,
No.1, pp.43-52.
Gordon I., 1982, The analysis of motivation-specific
migration streams, Environment and Planning, Vol.14,
pp.5-20.
Gorton C.D. & Wainwright P., 1980, Map projections in
marine science, Cartographica, Monograph 25. A.J. Kerr
(ed) The Dynamics of Oceanic Cartography, Vol.17, No.2,
pp.1-24.
Goulette A.M., 1985, Cartographic use of dithered patterns
on 8-color computer monitors, AutoCarto 7 (Proceedings of
the 7th International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.205-209.
Goulette A.M. & Wanie T.L., 1987, Automation and its
effect on maps at the U.S. bureau of the census, The
American Cartographer, Vol.14, No.3, pp.227-232.
Granberg H.B., 1988, A forest shading model using bitmapped graphics, Agricultural and Forest Meteorology,
Vol.43, pp.225-234.
Grassie D.N.D., 1982, Contouring by computer: some
observations, D.Rhind & T.Adams (eds) Computers in
Cartography, pp.93-116, British Cartographic Society,
Special Publication No.2, London.
Green A.E., 1983, Long-term unemployment in local labour
markets: trends through the current recession, C.U.R.D.S.,
Newcastle University, Discussion Paper 51.
Green A.E., 1986, The likelihood of becoming and
remaining unemployed in Great Britain, 1984, Transactions
of the Institute of British Geographers, Vol.11, pp.37-56.
Green A.E., 1988, The north-south divide in Great Britain:
an examination of the evidence, Trans. Inst. of British
Geographers, Vol.13, pp.179-198.
200
Green A.E. & Owen D., 1990, Linking large-scale spatially
referenced data sets to investigate migration in Britain,
Technical Reports in Geo-Information Systems, Computing
And Cartography, No.21, Wales and South West Regional
Research Laboratory, University of Wales, Cardiff.
Green A.E. & Owen D.W., 1984, The spatial
manifestations of the changing socio-economic composition
of employment in manufacturing, 1971-81, C.U.R.D.S.,
Newcastle University, Discussion Paper No. 56.
Green A.E., Owen D., Champion A., Goddard J. &
Coombes M., 1985, What contribution can labour
migration make to reducing unemployment?, C.U.R.D.S.,
Discussion Paper no. 73, Newcastle University.
Green A.E., Owen D.W., Champion A.G., Goddard J.B.
& Coombes M.G., 1986, What contribution can labour
migration make to reducing unemployment, P. E. Hart (ed),
Unemployment and Labour Market Policies, Gower
Publishing Co., Aldershot.
Greenacre M. & Hastie T., 1987, The geometric
interpretation of correspondence analysis, Journal of the
American Statistical Association, Vol.82, No.398, pp.437447.
Greenberg D.P., 1988, Coons award lecture,
Communications of the ACM, Vol.31, No.2, pp.122-129.
Greenberg D.P., 1989, Light reflection models for computer
graphics, Science, Vol.244, pp.166-173.
Greenberg D.P. & Meyer G.W., 1986, Color education
and color synthesis in computer graphics, Color Research
and Application (Supplement), Vol.11 pp.39-44.
Greenberg D.P. & Meyer G.W., 1987, Perceptual color
spaces for computer graphics, H.J. Durrett (ed) Color and
the Computer, Ch.4, pp.83-100.
Greenberg H.M., Evatt B.S. & Schneider J.B., 1982,
Integrated graphics shows patterns in time and space,
Computer Graphics News, Vol.2, p.6.
Greene N., 1986, Environmental mapping and other
applications of world projections, IEEE Computer
Graphics and Applications, Vol.6, No.11, pp.21-29.
Greene N. & Heckbert P.S., 1986, Creating raster
omnimax images from multiple perspective views using the
elliptical weighted average filter, IEEE Computer Graphics
and Applications, Vol.6, No.6, pp.21-27.
Greene R.P., 1989, Poverty concentrations in large
American cities: factors accounting for the emergence and
changing spatial patterns of extreme poverty areas , Ph.D.
No.9019065, University of Minnesota.
Greenlee D.D., 1987, Raster and vector processing for
scanned linework, Photogrammetric Engineering and
Remote Sensing, Vol.53, No.10, pp.1383-1387.
Grelot J.P., 1987, Punctograms (dot symbols),
Mappemonde, Vol.87, No.2, pp.23-25.
Grice A., 1991, Robin Hood dual sets pace for polls, The
Sunday Times, April 21st, p.4.
201
Griffin T.L.C., 1980, Cartographic transformation of the
thematic map base, Cartography, Vol.11, No.3, pp.163-174.
Griffin T.L.C., 1983, Recognition of areal units on
topological cartograms, The American Cartographer,
Vol.10, No.1, pp.17-29.
Griffin T.L.C., 1990, The importance of visual contrast for
graduated circles, Cartography, Vol.19, No.1, pp.21-30.
Grimes J. & Potel M., 1991, What is multimedia?, IEEE
Computer Graphics and Applications, Vol.11, No.1, pp.4952.
Groop R.E. & Cole D., 1978, Overlapping graduated
circles / magnitude estimation and method of portrayal,
Canadian Cartographer, Vol.15, No.2, pp.114-122.
Groop R.E. & Smith P., 1982, A dot matrix method of
portraying continuous statistical surfaces, The American
Cartographer, Vol.9, No.2, pp.123-130.
Groop R.E., Lipsey J.M., Medley K., Press C., Stein L. &
VerBurg K., 1984, Michigan political atlas, Centre for
Cartographic Research and Spatial Analysis, Department of
Geography, Michigan State University.
Grotch S.L., 1983, Three-dimensional and stereoscopic
graphs for scientific data display and analysis, IEEE
Computer Graphics and Applications, Vol.3, No.11, pp.3143.
Gudgin G. & Taylor P.J., 1973, Electoral bias and the
distribution of party voters, Seminar Papers, no. 22 ,
Department of Geography, Newcastle University.
Guelke L., 1976, Cartographic communication and
geographic understanding, Canadian Cartographer, Vol.13,
No.2, pp.107-122.
Guelke L., 1979, Perception, meaning and cartographic
design, Canadian Cartographer, Vol.16, No.1, pp.61-69.
Gupta S.D., 1961, The third dimension in colour
cartography, Geographical Review of India, Vol.23, No.1,
pp.30-33.
Gupta S.D. & McCabe D.H., 1987, Personal computer
displays, IEEE Computer Graphics and Applications,
Vol.7, No.10, pp.17-23.
Guptill S.C. & Starr L.E., 1984, The future of cartography
in the information age, L.E. Starr (ed) Computer-Assisted
Cartography Research and Development Report, pp.1-15.
Gyford J., Leach S. & Game C., 1989, The changing
politics of local government, Unwin Hyman, London.
Haber J., 1987, Graphic results, The Guardian, Thursday,
June 11 .
Haber R.B., 1990, Scientific visualization and the rivers
project at the national center for supercomputing
applications, G.M. Nielson, B. Shriver & L.J. Rosenblum
(eds) Visualization in Scientific Computing, pp.231-236,
IEEE Computer Society Press, California.
Haber R.B. & McNabb D.A., 1990, Visualization idioms: a
conceptual model for scientific visualization systems, G.M.
Nielson, B. Shriver & L.J. Rosenblum (eds) Visualization
in Scientific Computing, pp.74-93, IEEE Computer Society
Press, California.
Bibliography
Hagen C.B., 1982, Maps: an overview of the producer-user
interaction, ACSM-ASP 42nd Annual Convention, March,
Denver Colorado, pp.325-338.
Hagen H., Schreiber Th. & Gschwind E., 1990, Methods
for surface interrogation, Visualization '90, Proceedings of
the First IEEE Conference on Visualization, pp.187-193,
October, San Francisco, California.
Haggerty M., 1991, The art of artificial reality, IEEE
Computer Graphics and Applications, Vol.11, No.1, pp.814.
Haines E.A. & Greenberg D.P., 1986, The light buffer: a
shadow-testing accelerator, IEEE Computer Graphics and
Applications, Vol.6, No.9, pp.6-16.
Hakim C., 1979, The population census and its by-products:
data bases for research, International Journal of Social
Science, Vol.31, pp.342-352.
Halliday S.M., 1987, Two-variable choropleth maps: an
investigation of four alternative designs, MSc ISBN:0-31537026-2, Department of Geography, Memorial University
of Newfoundland.
Halsey A.H., 1989, Social trends since World War II, L.
McDowell, P. Sarre and C. Hamnett (eds), Divided Nation:
Social and Cultural Change in Britain, Hodder and
Stoughton & The Open University, London.
Hamnett C., 1984, Housing the two nations: socio-tenurial
polarization in England and Wales, 1961-81, Urban
Studies, Vol.43, pp.389-405.
Hamnett C., 1986, The changing socio-economic structure
of London and the South East, 1961-81, Regional Studies,
Vol.20.5, pp.391-406.
Hamnett C., 1987, A tale of two cities: sociotenurial
polarisation in London and the South-East, 1966-81,
Environment and Planning A., Vol.19, pp.537-556, Pion,
G.B..
Hamnett C., 1989a, Sociotenurial polarisation in London
and the South East: a reply to Berge's comments,
Environment and Planning, Vol.21, pp.545-548, Pion, G.B..
Hamnett C., 1989b, The owner-occupied housing market in
Britain: a north-south divide, J. Lewis & A. Townsend
(eds), The North-South divide: Regional change in Britain
in the 1980s, Paul Chapman, London.
Hamnett C. & Randolph B., 1983, The changing tenure
structure of the Greater London housing market, 19611981, The London Journal, Vol.9, No.2, pp.153-164.
Hampden-Turner C., 1981, Maps of the mind, Mitchell
Beazley, London.
Hand D.J., 1987, The application of expert systems in
statistics, B. Phelps (ed) Interactions in Artificial
Intelligence and Statistical Methods, Gower Technical
Press, Aldershot, pp.3-17.
Hansen Y.M., 1989, A formative evaluation study:
development of a graphic handbook to promote visual
thinking in adults, Ed.D. No.90222444, The Fielding
Institute.
Bibliography
Hanson A.J., Heng P.A. & Kaplan B.C., 1990, Techniques
for visualizing fermat's last theorum: a case study,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.97-106, October, San Francisco,
California.
Harding R.O., 1986, Communication effectiveness of CRTdisplayed bivariate choropleth maps, M.A., University of
Nebraska.
Hardy R.L., 1988, Concepts and results of mapping in three
dimensional space, Technical papers of the ACSM-ASPRS
Annual Convention, St. Louis, Missouri, Vol.2, pp.106-115.
Harju E.S., 1987, Methods of depicting land surface forms,
then and now, The International Yearbook of Cartography,
Vol.27, pp.25-27.
Harley J.B., 1988a, Maps, knowledge, and power, D.
Cosgrove & S. Daniels (eds) The Iconography of
Landscape, Cambridge University Press, New York.
Harley J.B., 1988b, Silences and secrecy: the hidden
agenda of cartography in early modern europe, Imago
Mundi, Vol.40, pp.57-76.
Harley J.B., 1989, Deconstructing the map, Cartographica,
Vol.26, No.2, pp.1-20.
Harley J.B., 1990a, Cartography, ethics and social theory,
Cartographica, Vol.27, No.2, pp.1-27.
Harley J.B., 1990b, Text and contexts in the interpretation
of early maps, D. Buisseret (ed) From Sea Charts to
Satellite Images: Interpreting North American History
through Maps, University of Chicago Press, Chicago.
Harley J.B. & Woodward D. (eds), 1987, The history of
cartography: volume one, University of Chicago Press,
Chicago.
Haro A.S., 1968, Area cartogram of the SMSA population of
the United States, Annals of the Association of American
Geographers, Vol.58, pp.452-460.
Harrell Allen T., 1978, New methods in social science
research: policy sciences and futures research, Praeger
Publishers, London.
Harris C.D., 1954, The market as a factor in the
localization of industry in the United States, Annals of the
Association of American Geographers, Vol.44, pp.315-348.
Harris C.D. & McDowell G.B., 1955, Distorted maps, a
teaching device, Journal of Geography, Vol.54, pp.286-289.
Harrop M., 1986, Voting and the electorate, H. Drucker, P.
Dunleavy, A. Gamble & G. Peele (eds) Developments in
British Politics 2, Macmillan, London.
Hart J.C., Kauffman L.H. & Sandin D.J., 1990,
Interactive visualization of quaternion Julia sets,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.209-218, October, San Francisco,
California.
Hartmann J.L., 1991, Visualization techniques for GIS,
EGIS '91 Proceedings, pp.406-412, Brussels, Belgium.
Harvey D., 1990, Between space and time: reflections on
the geographical imagination, Annals of the Assoc. of
American Geographers, Vol.80(3), pp.418-434.
202
Haslett J., Wills G. & Unwin A., 1990, SPIDER - an
interactive statistical tool for the analysis of spatially
distributed data, International Journal of Geographical
Information Systems, Vol.4, No.3, pp.285-296.
Hassan M.M., 1985, Spectral analysis of terrain profiles for
the digital filtering of DEM data, Technical papers of the
ACSM-ASPRS Fall Convention, September, Indianapolis,
Indiana, pp.792-800.
Hassett J., Kinn G. & Silfer A., 1987, A geographic
information system utilizing the triangulated irregular
network as a basis for hydrologic modeling, AutoCarto 8
(Proceedings of the 8th International Symposium on
Computer-Assisted Cartography), pp.129-136 .
Haughton J.P. (ed), 1979, Atlas of Ireland, Royal Irish
Academy, Dublin.
Haynes R.M., 1978, A note on dimensions and relationships
in human geography, Geographical Analysis, Vol.10, No.3,
pp.288-293.
Hazelton N.W.J., Leahy F.J. & Williamson I.P., 1990, On
the design of temporally-referenced, 3-D geographical
information systems: development of 4-Dimensional GIS,
GIS / LIS '90 Proceedings, Vol.1, pp.357-372.
Heath A. & McDonald S-K., 1989, Social change and the
future of the left, L. McDowell, P. Sarre and C. Hamnett
(eds), Divided Nation: Social and Cultural Change in
Britain, Hodder and Stoughton & The Open University,
London.
Heil R.J., 1980, The digital terrain model as a data base for
hydrological and geomorphological analyses, AutoCarto
IV (Proceedings of the International Symposium on
Cartography and Computing: Applications in Health and
the Environment, Virginia), Vol.2, pp.132-138.
Helman J.L. & Hesselink L., 1990a, Representation and
display of vector field topology in fluid flow data sets, G.M.
Nielson, B. Shriver & L.J. Rosenblum (eds) Visualization
in Scientific Computing, pp.61-73, IEEE Computer Society
Press, California.
Helman J.L. & Hesselink L., 1990b, Surface
representations of two- and three- dimensional fluid flow
topology, Visualization '90, Proceedings of the First IEEE
Conference on Visualization, pp.6-13, October, San
Francisco, California.
Hendee W.R., 1989, The perception of visual data: a
summary of the ACR meeting on visualization science in
engineering, computing, and medicine, The American
Journal of Roentgenology, Vol.152, No.6, pp.1313-1315.
Henry N., 1987, Review: statistical graphics: design
principles and practices, The American Cartographer,
Vol.14, No.1, p.89.
Henry N.F. & Montz B.E., 1982, Interpreting proportional
circle maps, ACSM-ASP 42nd Annual Convention, March,
Denver Colorado, pp.100-107.
Herbert M., 1987, A walk on the Clydeside, CADCAM
International, Vol.6, No.4, pp.41-42.
Herdeg W., 1974, Graphics diagrams: the graphic
visualization of abstract text, The Graphics Press, Zurich,
Switzerland.
203
Herman G.T. & Levkowitz H., 1986, Color in
multidimensional multiparameter medical imaging, Color
Research and Application (Supplement), Vol.11 pp.15-20.
Herr L., 1990, Volume visualization for biology - a new
dimension in computer graphics, Bioscience, Vol.40, No.3,
pp.19-202.
Hershey R.R. & Whitehead D.C., 1990, An evaluation of
desktop mapping packages for the apple macintosh,
Research Paper, Geographical Information Research
Laboratory, Department of Geography, London School of
Economics.
Herzog A., 1989, Modeling reliability on statistical surfaces
by polygon filtering, M. Goodchild & S. Gopal (eds) The
Accuracy of Spatial Databases, Chapter 18, Taylor &
Francis, New York.
Hess K., 1990, Supercomputer images of electron device
physics, Physics Today, Vol.43, No.2, pp.34-42.
Hesselink L., 1988, Digital image processing in flow
visualization, Annual Review of Fluid Mechanics, Vol.20,
pp.421-485.
Hesselink L., Pender J., Jaffey S.M. & Dutta K., 1983,
Quantitative three-dimensional flow visualization,
Proceedings of the Third International Symposium on Flow
Visualization, Washington D.C., pp.295-300.
Hibbard W. & Santek D., 1990a, Visualizing large
meteorological data sets, G.M. Nielson, B. Shriver & L.J.
Rosenblum (eds) Visualization in Scientific Computing,
pp.147-152, IEEE Computer Society Press, California.
Hibbard W. & Santek D., 1990b, The VIS-5D system for
easy interactive visualization, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.28-35, October, San Francisco, California.
Higgs B. & Wright A., 1991, The 1991 census & beyond - a
GIS approach to demographic analysis in Dudley, Mapping
Awareness, Vol.5, No.5, pp.38-42.
Hill B.T., King D.P. & Nash T.L., 1985, Graduated
polygons as a cartographic technique, Technical papers of
the ACSM-ASPRS Fall Convention, September,
Indianapolis, Indiana, pp.59-67.
Hill G.B., 1976, Maps of cancer incidence, Proceedings of
the Workshop on Automated Cartography and
Epidemiology, National Center for Health Statistics, U.S.
Department of Health, pp.48-51.
Hirschy L.M., 1990, Scientific visualization for the bench
chemist, American Laboratory, Vol.22, No.5, pp.84-90.
Hirsh N. & Brown B.L., 1990, A holistic method for
visualizing computer performance measurements, G.M.
Nielson, B. Shriver & L.J. Rosenblum (eds) Visualization
in Scientific Computing, pp.190-208, IEEE Computer
Society Press, California.
Hobcraft J., 1989, People and services: central assessment
of local needs, H. Joshi (ed) The Changing Population of
Britain, Basil Blackwell, Oxford.
Hobcraft J. & Joshi H., 1989, Population matters, H. Joshi
(ed) The Changing Population of Britain, Basil Blackwell,
Oxford.
Bibliography
Hodgson M.E., 1985, Constructing shaded maps with the
DIME topological structure: an alternative to the polygon
approach, AutoCarto 7 (Proceedings of the 7th
International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.275-282.
Hodgson M.J., 1990, A flow-capturing location-allocation
model, Geographical Analysis, Vol.22, No. 3, pp.270-279.
Hogeweg P., 1988, Cellular automata as a paradigm for
ecological modeling, Applied Mathematics and
Computation, Vol.27, No.1, pp.81-100.
Hollingsworth T.H., 1964, The political colour of britain by
numbers of voters, The Times, October 19th, p.18.
Hollingsworth T.H., 1966, The political colour of britain by
winning parties, The Times, April 4th, p.8.
Hollingsworth T.H., 1968, Internal migration statistics
from the central register for Scotland of the national health
service, Journal of the Royal Statistical Society, series A,
Vol.131, part 4, pp.340-383 .
Hollingsworth T.H., 1970, Migration: a study based on
Scottish experience between 1939 and 1964, Social and
Economic Studies, Glasgow University, Occasional Paper
No.12, Oliver & Boyd.
Hollingsworth T.H., 1976, Repeated migration and past
migration frequency, University of Glasgow, Dept. of
Social and Economic research, Discussion paper No.14.
Holly B.P., 1978, The problem of scale in time-space
research, T. Carlstein, D. Parkes & N. Thrift (eds), Time
and Regional Dynamics, Edward Arnold, London.
Holmes J.M., 1974, Isodemographic map of Canada
(review), Cartography, Vol.8, No.4, pp.217-219.
Holmes J.M., 1984, Cognitive processes used to recognize
perspective three-dimensional map surfaces, M.A.,
University of South Carolina.
Holroyd F.C., 1986, Joining tiles in hierarchies: a survey,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.17-35.
Honablew J.A., Schlueter J.J. & Noma A.A., 1982,
Software for three dimensional topographic scenes,
AutoCarto 5 (Proceedings of the 5th International
Symposium on Computer-Assisted Cartography), pp.397406.
Honeycutt D.M. & Wojcik J., 1990, Development of a
population density surface for the conterminous United
States, GIS / LIS '90 Proceedings, Vol.2, pp.484-496.
Hooper D., 1985, Raster-based contour plotting from digital
elevation models using mini- or microcomputers, Technical
papers of the ACSM-ASPRS Fall Convention, September,
Indianapolis, Indiana, pp.196-202.
Hoover E.M., 1948, The location of economic activity,
McGraw-Hill, New York, p.88.
Hopgood F.R.A., 1991, Use of Colour, Uniras UK User
Group Newsletter, No.9, pp.8-11.
Hopps H.C., 1969, Computer-produced distribution maps of
disease, Annals of the New York Academy of Sciences,
Vol.161, pp.779-799.
Bibliography
204
Hopps H.C., 1972, Display of data with emphasis on the
map form, Annals of the New York Academy of Sciences,
Vol.199, pp.325-334.
Huber P.J., 1987, Experiences with three-dimensional
scatterplots, Journal of the American Statistical
Association, Vol.82, No.398, pp.448-453.
Horner A.A., 1988, 150 years of mapping Ireland's
population distribution, The Society of University
Cartographers Bulletin, Vol.22, No.1, pp.1-8.
Hudson R. & Williams A.M., 1989, Divided Britain,
Belhaven Press, London.
Horner A.A., Walsh J.A. & Harrington V.P., 1987,
Population in Ireland: a census atlas, Department of
Geography, University College Dublin.
Howe G.M., 1979a, Statistical and cartographic methods of
analysing spatial patterns of mortality, Geoforum, Vol.10,
No.3, pp.311-322.
Howe G.M., 1979b, Mortality from selected malignant
neoplasms in the British Isles: the spatial perspective, The
Geographical Journal, Vol.145, No.3, pp.401-415.
Howe G.M., 1986a, International variations in cancer
incidence and mortality, G.M. Howe (ed) Global
Geocancerology: A World Geography of Human Cancers,
Churchill Livingstone, Edinburgh.
Howe G.M., 1986b, The United Kingdom, G.M. Howe (ed),
Global Geocancerology: A World Geography of Human
Cancers, Churchill Livingstone, Edinburgh.
Huggins W., 1973, What's needed now?, Why is Computer
Graphics Always a Year Away?, Computer Graphics,
Quarterly report of Siggraph-ACM, Vol.8, No.1, pp.32-48.
Hughes C.A. & Savage E.E., 1967, The 1955 federal
redistribution, The Australian Journal of Politics and
History, Vol.13, pp.8-20.
Humpherys G., 1968, A map of housing quality in the
United Kingdom, Institute of British Geographers,
Transactions no.43, April 1968, George Philip & Son,
London.
Hunt A.J., 1968, Problems of population mapping: an
introduction, Institute of British Geographers, Transactions
no.43, April 1968, George Philip & Son, London.
Hunter J.M. & Meade M.S., 1971, Population models in
the high school, The Journal of Geography, Vol.70, pp.95104.
Howe G.M., 1986c, Disease mapping, M. Pacione (ed),
Medical Geography: Progress and Prospect, Croom Helm,
London.
Hunter J.M. & Young J.C., 1968, A technique for the
construction of quantitative cartograms by physical
accretion models, The Professional Geographer, Vol.20,
No.6, pp.402-407.
Howe G.M., 1986d, Does it matter where I live?,
Transactions of the Institute of British Geographers, Vol.11,
pp.387-414.
Hunter J.M. & Young J.C., 1971, Diffusion of influenza in
England and Wales, Annals of the Association of American
Geographers, Vol.61, No.4, pp.637-653.
Howe G.M. (ed), 1970, National atlas of disease mortality
in the United Kingdom, Thomas Nelson and sons, London.
Hurst M., 1986, The electoral system, J. Langton & R.J.
Morris (eds), Atlas of Industrializing Britain 1780-1914,
Methuen, London.
Hsiung P.K., Thibadeau R.H., Cox C.B., Dunn R.H.P.,
Wu M. & Olbrich P.A., 1990, Wide-band relativistic
doppler effect visualization, Visualization '90, Proceedings
of the First IEEE Conference on Visualization, pp.83-92,
October, San Francisco, California.
Hussey K.J., 1988, Scientific data visualization:
applications of an emerging field, Electronic Imaging '88,
International Electronic Imaging Exposition and
Conference, Boston, Massachusetts, p.1101.
Hsu M.L., 1979, The cartographer's conceptual process and
thematic symbolization, The American Cartographer, Vol.6,
No.2, pp.117-127.
Hyman G.M. & Mayhew L.D., 1983, On the geometry of
emergency service medical provision in cities, Environment
and Planning, Vol.15, pp.1669-1690.
Hsu M.L., 1981, The role of projections in modern map
design, Cartographica, Monograph 27. L. Guelke (ed) Maps
in Modern Geography - Geographical Perspectives on the
New Cartography, Vol.18, No.2, pp.151-186.
Härö E.S., 1989, Area Cartogram of the Finnish
Population, Terra, Vol.101, No.2, pp.173-180.
Hsu M.L., 1982, Map projections in cartographic design,
ACSM-ASP 42nd Annual Convention, March, Denver
Colorado, p.108.
Hsu P. & Staudhammer J., 1990, Superposing images with
shadow casting, Visualization '90, Proceedings of the First
IEEE Conference on Visualization, pp.298-306, October,
San Francisco, California.
Hsu S.Y., 1980, The pattern recognition problem / man,
machine and their interaction, Cartographica, Vol.17, No.1,
pp.101-117.
Hubbuck J. & Coombes M.G., 1989, 1991 census: local
labour market areas for socio-economic and racial
minority groups, C.U.R.D.S. discussion paper no. 92,
Newcastle University.
IEE sponsored, 1988, Visualisation of fields in three
dimensions, Science, Education and Technology Division
Colloquium, London.
Inselburg A. & Dimsdale B., 1990, Parallel coordinates: a
tool for visualizing multi-dimensional geometry,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.361-378, October, San Francisco,
California.
International Geographic Union, 1983, Computer software
for spatial data handling, U.S. Department of the Interior
Geological Survey, Vols.1-3.
Irwin D., 1962, The representation of geographical data by
volume symbols, M.A., Southern Illinois University.
205
Irwin D., 1976, The historical development of terrain
representation in American cartography, The International
Yearbook of Cartography, Vol.16, pp.70-83.
Itami R.M., 1988, Cellular automatons as a framework for
dynamic simulations in geographic information systems,
GIS/LIS'88 proceedings - Accessing the World, San
Antonio - Texas, pp.590-597.
Iwaniw M.A., Harrold T. & Ogilvie J.R., 1989, A
visualization study of flow patterns in a model of a
modified-open-front (MOF) swine barn, Canadian
Agricultural Engineering, Vol.31, No.1, pp.65-71.
Jackson M.J., Bell S.B.M., Stevens A., Freedman I. &
Dickman P.W., 1986, Efficiency of tesseral arithmetic for
2.5D image manipulation on serial computers and
transputer arrays, S.B.M. Bell and B.M. Diaz (eds) Spatial
Data Processing using Tesseral Methods, pp.353-364.
Jackson P.H., 1988, IAX- An algebraic image processing
language for research, J.P. Muller (ed) Digital Image
Processing in Remote Sensing, Taylor and Francis, London,
pp.79-103.
Jacob R.J.K., Egeth H.E. & Bevin W., 1976, The face as a
data display, Human Factors, Vol.18, No.2, pp.189-200.
Jacobs N.E., 1987, The executive decision: selecting a
business graphics system, H.J. Durrett (ed) Color and the
Computer, Ch.16, pp.285-295.
James M., 1987, Life revisited, Program Now, pp.32-34.
Jelliman M., 1967, A cartographic analysis of the Glasgow
1961 census, The Cartographic Journal, Vol.4, No.1, pp.4449.
Jen E., 1986, Global properties of cellular automata,
Journal of Statistical Physics, Vol.43, Nos.1/2, pp.219-242.
Jenks G.F., 1953, "Pointillism" as a cartographic
technique, The Professional Geographer, Vol.5, No.5, pp.46.
Jenks G.F., 1973, Visual integration in thematic mapping:
fact or fiction, The International Yearbook of Cartography,
Vol.13, pp.27-35.
Jenks G.F., 1974, Statistical mapping and the presentation
of statistics (review), The American Cartographer, Vol.1,
No.2, pp.175-176.
Jenks G.F., 1975a, Contemporary statistical maps evidence
of spatial and graphic ignorance, Auto-carto 2
(Proceedings of the International Symposium on ComputerAssisted Cartography), pp.51-60.
Jenks G.F., 1975b, Undergeneralization and figure-ground
relationships in statistical mapping, Auto-carto 2
(Proceedings of the International Symposium on ComputerAssisted Cartography), pp.149-154.
Jenks G.F., 1975c, The evaluation and prediction of visual
clustering in maps symbolized with proportional circles,
J.C. Davies & M.J. McCullagh (eds) Display and Analysis
of Spatial Data, pp.311-327, John Wiley & Sons, London.
Jenks G.F., 1976, Contemporary statistical maps - evidence
of spatial and graphic ignorance, The American
Cartographer, Vol.3, No.1, pp.11-19.
Bibliography
Jenks G.F. & Brown D.A., 1966, Three-dimensional map
construction, Science, Vol.154, No.3750, pp.857-864.
Jenks G.F. & Caspall F.C., 1971, Error on choroplethic
maps: definition, measurement, reduction, Annals of the
Association of American Geographers, Vol.61, No.2,
pp.217-244.
Jensen J.R., 1978, Three-dimensional choropleth maps /
development and aspects of cartographic communication,
Canadian Cartographer, Vol.15, No.2, pp.123-141.
Jensen J.R., 1980, Stereoscopic statistical maps, The
American Cartographer, Vol.7, No.1, pp.25-37.
Jenson S.K., 1985, Automated derivation of hydrologic
basin characteristics from digital elevation model data,
AutoCarto 7 (Proceedings of the 7th International
Symposium on Computer-Assisted Cartography),
Washington D.C., pp.301-310.
Jenson S.K. & Domingue J.O., 1988, Extracting
topographic structure from digital elevation data for
geographic information system analysis, Photogrammetric
Engineering and Remote Sensing, Vol.54, No.11, pp.15931600.
Jenson S.K. & Trautwein C., 1987, Methods and
applications in surface depression analysis, AutoCarto 8
(Proceedings of the 8th International Symposium on
Computer-Assisted Cartography), pp.137-144 .
Jern M., 1990, Visualization of scientific data, Tutorial, 8th
Eurographics Conference, University of Bath, 9-11 April.
Jimenez J., Cogollos M. & Bernal L.P., 1985, A
perspective view of the plane mixing layer, Journal of Fluid
Mechanics, Vol.152, pp.125-143.
Jobse R.B. & Musterd S., 1989, Changes in migration
within the Netherlands, 1975-85 , Tijdschrift voor Econ. en
Soc. Geografie, Vol.80, pp.244-250.
Johnson J.H., 1984, Inter-urban migration in Britain: a
geographical perspective, A.J. Boyce (ed), Migration and
Mobility: Biosocial aspects of human movement, Taylor &
Francis, London.
Johnson J.H., 1986, Hierarchical structure in digital
pictures, S.B.M. Bell and B.M. Diaz (eds) Spatial Data
Processing using Tesseral Methods, pp.37-67.
Johnson J.H., Salt J. & Wood P.A., 1974, Housing and the
migration of labour in England and Wales, Saxon House/
Lexington Books, England.
Johnston M.E., 1988, Simulated annealing, American
Journal of Mathematical and Management Sciences, Vol.8,
Nos.3 & 4, pp.205-207.
Johnston R. & Pattie C., 1989, Voting in Britain since
1979: a growing north-south divide, J. Lewis & A.
Townsend (eds), The North-South divide: Regional change
in Britain in the 1980s, Paul Chapman, London.
Johnston R.J., 1979, Regional variation in the 1979 general
election results for England, Area, Vol.11, No.4, pp.294297.
Johnston R.J., 1981, Regional variations in British voting
trends-1966-1979: tests of an ecological model, Regional
Studies, Vol.15, pp.23-32, Pergamon Press.
Bibliography
206
Johnston R.J., 1982, Short-term electoral change in
England: estimates of its spatial variation, Political
Geography Quarterly, Vol.1, No. 1, pp.41-55.
Jonchay I. du, 1970, Long-distance aviation and mass
transport, Proceedings of the International Conference on
Transportation Maps, pp.164-165, Budapest.
Johnston R.J., 1983, The feedback component of the pork
barrel: tests using the results of the 1983 general election
in Britain, Environment and Planning, Vol.15, pp.16911696.
Jones C.B., 1989, Cartographic name placement with
Prolog, IEEE Computer Graphics and Applications, Vol.9,
No.5, pp.36-47.
Johnston R.J., 1985, The geography of English politics,
Croom Helm, Appendix 1, pp.297-323.
Johnston R.J. , 1986a, Research policy and review 9. A
space for place (or a place for space) in British
psephology: a review of recent writings with especial
reference to the general election of 1983, Environment and
Planning, Vol.18, pp.573-598.
Jones D.B. (ed), 1972, Oxford economic atlas of the world
(4th ed.), Oxford University Press, London.
Jones G.E., 1986, Color use, abuse in presentations,
Computer Graphics World, May, pp.119-122.
Joshi H., 1989, The changing form of women's economic
dependancy, H. Joshi (ed) The Changing Population of
Britain, Basil Blackwell, Oxford.
Johnston R.J., 1986b, Information flows and votes: an
analysis of local campaign spending in England, 1983,
Geoforum, Vol.17, No. 1, pp.69-79.
Journel A.G. & Alabert F.G., 1990, New methods for
reservoir mapping, Journal of Petroleum Technology,
Vol.42, No.2, pp.212-218.
Johnston R.J., 1986c, Information provision and individual
behavior: a case study of voting at an English general
election, Geographical Analysis, Vol.18, No.2, pp.129-140.
Judd D.D., 1986, Processing techniques for the production
of an experimental computer-generated shaded-relief map,
The American Cartographer, Vol.13, No.1, pp.72-79.
Johnston R.J., 1989, Commentary- an environmentalist
upsurge in Great Britain , Environment and Planning,
Vol.21, pp.851-852.
Jupe D., 1987, The new technology: will cartography need
the cartographer?, The Canadian Surveyor, Vol.14, No.3,
pp.341-346.
Johnston R.J. & Pattie C.J., 1987, A dividing nation? an
initial exploration of the changing electoral geography of
Great Britain, 1979-1987, Environment and Planning,
Vol.19, pp.1001-1013.
Kadmon N., 1968, The mapping of distribution parameters,
The Cartographic Journal, Vol.5, No.1, pp.64-69.
Johnston R.J. & Pattie C.J., 1988a, Attitudes and votes at
the 1983 general election: explorations of the geography of
'deviations', Area, Vol.20.2, pp.111-119.
Johnston R.J. & Pattie C.J., 1988b, Changing voter
allegiances in Great Britain, 1979-1987: an exploration of
regional patterns, Regional Studies, Vol.22.3, pp.179-192.
Johnston R.J. & Pattie C.J., 1989, A growing north-south
divide in British voting patterns, 1979-87, Geoforum,
Vol.20, No.1, pp.93-106.
Johnston R.J. & Pattie C.J., 1990a, Class, attitude, and
retrospective voting: exploring the regional variations in
the 1983 general election in Great Britain, Environment
and Planning, Vol.22, pp.893-908.
Johnston R.J. & Pattie C.J., 1990b, The regional impact of
thatcherism: attitudes and votes in Great Britain in the
1980s, Regional Studies, Vol.24. No.6, pp.479-493.
Johnston R.J. & Pattie C.J., 1991, Tactical voting in Great
Britain in 1983 and 1987: an alternative approach, British
Journal of Political Science, Vol.21, pp.95-108.
Johnston R.J., Hay A.M. & Taylor P.J., 1982, Estimating
the source of spatial change in election results: a
multiproportional matrix approach, Environment and
Planning, Vol.14, pp. 951-961.
Johnston R.J., Openshaw S., Rhind D.W., Rossiter D.J.,
1984, Spatial scientists and representational democracy:
the role of information-processing technology in the design
of parliamentary and other constituencies, Environment
and Planning C, Vol.2, pp.57-66.
Kadmon N., 1975, Data-bank derived hyperbolic-scale
equitemporal town maps, The International Yearbook of
Cartography, Vol.15, pp.47-54.
Kadmon N., 1977, A novel approach to teaching automated
thematic cartography, Canadian Cartographer, Vol.14,
No.2, pp.112-119.
Kadmon N., 1982, Cartograms and topology,
Cartographica, Vol.19, No.3, pp.1-17.
Kadmon N. & Shlomi E., 1978, A polyfocal projection for
statistical surfaces, The Cartographic Journal, Vol.15,
No.1, pp.36-41.
Kakumoto S., Iwamura K. & Emura Y., 1990, High speed
analysis method of geographical information using a quasi
n-dimensional table, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.2,
pp.941-950.
Kaufman A. & Bandopadhay A., 1989, Forest of
Quadtrees: an object representation for 3D graphics,
Eurographics '89, Elsevier Science Publishers, Oxford.
Kaufman A., Yagel R., Bakalash R. & Spector I., 1990,
Volume visualization in cell biology, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.160-167, October, San Francisco, California.
Kaufman L., 1975, The uses of color, Auto-carto 2
(Proceedings of the International Symposium on ComputerAssisted Cartography), pp.270-276.
Ke Y. & Panduranga E.S., 1990, A journey into the fourth
dimension, Visualization '90, Proceedings of the First IEEE
Conference on Visualization, pp.219-229, October, San
Francisco, California.
207
Bibliography
Kelley A.D., Malin M.C. & Nielson G.M., 1988 , Terrain
simulation using a model of stream erosion, Computer
Graphics (USA), Vol.22, No.4, pp.263-268.
King J.E.O., 1985, The communication of profile
perspective maps: a perceptual study, M.Sc., Virginia State
University.
Kelly J., 1987, Constructing an area-value cartogram for
New Zealand's population, New Zealand Cartographer,
Vol.17, No.1, pp.3-10.
King R. & Shuttleworth I., 1989, The Irish in Coventry:
the social geography of a relict community, Irish
Geography, Vol.22, No.2, pp.64-78.
Kennedy M., 1991, Labour tide rises on the middle ground,
The Guardian, April 22nd, p.5.
King-Smith P.E. & Vingrys A.J., 1986, Factors in using
color video monitors for assessment of visual thresholds,
Color Research and Application (Supplement), Vol.11
pp.57-62.
Kennedy S., 1989, The small number problem and the
accuracy of spatial databases, M. Goodchild and S. Gopal
(eds), The Accuracy of Spatial Databases, Taylor and
Francis, London.
Kinnear C., 1987, The TIGER structure, AutoCarto 8
(Proceedings of the 8th International Symposium on
Computer-Assisted Cartography), pp.249-257 .
Kennett S.R., 1983, Migration within and between labour
markets, J.B. Goddard & A.G. Champion (eds) The Urban
and Regional Transformation of Britain, Methuen, London.
Kinnear M., 1968, The British voter, Cornell University
Press, pp.66-69.
Kennie T.J.M. & McLaren R.A., 1988, Modelling for
digital terrain and landscape visualisation,
Photogrammetric Record, Vol.12, No.72, pp.711-745.
Kirby A.M. & Tarn D., 1976, Some problems of mapping
the 1971 census by computer, Environment and PlanningA,
Vol.8, pp.507-513.
Kerlick G.D., 1990, Moving iconic objects in scientific
visualization, Visualization '90, Proceedings of the First
IEEE Conference on Visualization, pp.124-130, October,
San Francisco, California.
Kirkby M.J., 1986, A two-dimensional simulation model for
slope and stream evolution, A.D. Abrahams (ed) Hillslope
Processes, Allen & Unwin, Mass. USA.
Kern R. & Rushton G., 1969, MAPIT: a computer program
for production of flow maps, dot maps and graduated
symbol maps, The Cartographic Journal, Vol.6, No.2,
pp.131-137.
Kerstens P.J.M. & Rockwell D., 1988, Ensembleaveraging and correlation for flow visualization images,
Experiments in Fluids, Vol.6, pp.409-419.
Kidron M. & Segal R., 1981, The state of the world atlas,
Pan Books, London.
Kidron M. & Segal R., 1984, The new state of the world
atlas, Pluto Press, London.
Kidron M. & Smith D., 1983, The war atlas, Heinemann,
London.
Kiernan K. E., 1989, The family: formation and fission, H.
Joshi (ed) The Changing Population of Britain, Basil
Blackwell, Oxford.
Kim Y.M. & Park S.B., 1989, New method for representing
linear quadtree, Electronics Letters, Vol.25, No.2, pp.137139.
Kimerling A.J., 1975, A cartographic study of equal value
gray scales for use with screened gray areas, The
American Cartographer, Vol.2, No.2, pp.119-127.
Kimerling A.J., 1985, A comparison of equal-value gray
scales, The American Cartographer, Vol.12, No.2, pp.132142.
King A., 1986a, Who'll win a 1987 election?, P. Martin (ed)
The World in 1987, The Economist Publications Ltd,
London, pp.15-19.
King A., 1986b, Who'll win a 1987 election?, The Economic
Intelligence Unit, The Economist Publications Ltd.
Klasky R.S., 1989, Computer animation for visualizing
terrain data, IEEE Computer Graphics and Applications,
Vol.9, No.3, pp.12-13.
Klawe J.J., 1973, Population mapping, Canadian
Cartographer, Vol.10, No.1, pp.44-50.
Klein R.W. & Dubes R.C., 1989, Experiments in projection
and clustering by simulated annealing, Pattern Recognition,
Vol.22, No.2, pp.213-220.
Kleiner B. & Hartigan J.A., 1981, Representing points in
many dimensions by trees and castle, Journel of the
American Statistical Association, Vol.76, No.374, pp.260276.
Klove R.C., 1975, Census statistical mapping and the users,
Auto-carto 2 (Proceedings of the International Symposium
on Computer-Assisted Cartography), pp.175-182.
Kluijtmans P. & Collin C., 1991, 3D computer technology
for environmental impact studies, EGIS '91 Proceedings,
pp.547-550, Brussels, Belgium.
Kluksdahl N.C., Kriman A.M. & Ferry D.K., 1990, The
role of visualization in the simulation of quantum electronic
transport in semiconductors, G.M. Nielson, B. Shriver &
L.J. Rosenblum (eds) Visualization in Scientific
Computing, pp.153-159, IEEE Computer Society Press,
California.
Knowlton K., 1987, Why it isn't art yet, Computer Graphics
(USA), Vol.21, No.1, p.11.
Knox G., 1964, Epidemiology of childhood leukaemia in
Northumberland and Durham, British Journal of
Preventative and Social Medicine, Vol.18, pp.17-24.
Koeman C., 1979, La graphique et le traitement graphique
de l'information, The International Yearbook of
Cartography, Vol.19, pp.159-160.
Kolbusz J., 1988, The visual representation of scientific
data, B.Sc. Dissertation, University College London.
Bibliography
Kosslyn S.M., 1983, Ghost in the mind's machine: creating
and using images in the brain, W.W.Norton & Company,
New York.
Kraak M.J., 1989, Computer-assisted cartographical 3D
imaging techniques, J. Raper (ed), GIS: Three Dimensional
Applications in Geographic Information Systems, Taylor &
Francis, London.
Krause E.F., 1975, Taxicab geometry, Addison-Wesley,
Philippines.
208
Langran C.G., 1989, A review of temporal database
research and its use in GIS applications, International
Journal of Geographical Information Systems, Vol.3, No.3,
pp.215-232.
Langran C.G. & Chrisman N.R., 1988, A framework for
temporal geographic information, Cartographica, Vol.25,
No.3, pp.1-14.
Langren C.G., 1990, Temporal GIS design tradeoffs,
Journel of the Urban and Regional Information Systems
Association, Vol.2, No.2, pp.16-25.
Kretschmer I., 1978, The pressing problems of theoretical
cartography, International Yearbook of Cartography,
Vol.18, pp.33-39.
Langton C.G., 1986, Studying artificial life with cellular
automata, Physica D, Vol.22, pp.120-149.
Krueger M.W., 1983, Artificial Reality, Addison-Wesley,
Reading, Massachusetts.
Langton C.G., 1989, Artificial life, C.G. Langton (ed),
Artificial Life, vol.VI, Addison-Wesley, USA.
Krueger W., 1990, The application of transport theory to
visualization of 3D scalar data fields, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.273-280, October, San Francisco, California.
Lapaine M. & Francula N., 1990, General cylindrical
perspectives in cartography, Proceedings of the 4th
International Symposium on Spatial Data Handling, Zurich,
Vol.1, pp.371-380.
Kruskal W., 1975, Visions of maps and graphs, Auto-carto
2 (Proceedings of the International Symposium on
Computer-Assisted Cartography), pp.27-36.
Lasser D., 1990, Visualization of free form volumes,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.379-386, October, San Francisco,
California.
Kubo S., Takamura S. & Yoshino S., 1990, Multimedia
GIS on PC, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.1,
pp.363-370.
Kumler M.P., 1988, Mapping smooth surfaces with
continuous tone maps, M.A. No.1334782, Department of
Geography, Michigan State University.
Kumler M.P., 1990, A quantitative comparison of regular
and irregular digital terrain models, GIS / LIS '90
Proceedings, Vol.1, pp.357-372.
Kvalheim O.M. & Telnaes N., 1986, Visualizing
information in multivariate data: applications to petroleum
geochemistry - part 1. projection methods, Analytica
Chimica Acta, Vol.191, pp.87-96.
La Breque M., 1989, The scientific uses of visualization,
American Scientist, Vol.77, No.6, pp.525-527.
La Violette P.E., 1988, Processing satellite infrared and
visible imagery for oceanographic analysis, J.P. Muller
(ed) Digital Image Processing in Remote Sensing, Taylor
and Francis, London, pp.213-241.
Lacko L., 1967, The form and the contents of economic
maps, Tijdschrift voor Econ. en Soc. Geografie, Nov/Dec,
pp.324-330.
Lafargue B., 1988, VIP: visualizing in perspective, GIS/
LIS'88 proceedings - Accessing the World, San Antonio Texas, pp.100-110.
Lai P.C., 1983, A new solution to travel time map
transformations, PhD ISBN 0-315-13480-1, The University
of Waterloo (Canada).
Lamb D. & Bandopadhay A., 1990, Interpreting a 3D
object from a rough 2D line drawing, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.59-66, October, San Francisco, California.
Laszlo M.J., 1990, Techniques for visualizing 3-dimensional
manifolds, Visualization '90, Proceedings of the First IEEE
Conference on Visualization, pp.342-352, October, San
Francisco, California.
Lathrop O., 1988, Data visualization: the emergence of a
new industry, Electronic Imaging '88, International
Electronic Imaging Exposition and Conference, Boston,
Massachusetts, pp.1091.
Lathrop O., 1989, The current state of data visualization as
a business, Conference Proceedings, 10th Annual
Conference and Exposition Dedicated to Computer
Graphics, NCGA (April), Philadelphia.
Lavin S., 1986, Mapping continuous geographical
distributions using dot density shading, The American
Cartographer, Vol.13, No.2, pp.140-150.
Lavin S. & Archer J.C., 1984, Computer-produced
unclassed bivariate choropleth maps, The American
Cartographer, Vol.11, No.1, pp.49-57.
Lavin S.J. & Cerveny R.S., 1987, Unit-vector density
mapping, The Cartographic Journal, Vol.24, No.2, pp.131141.
Lawton R., 1968a, A map of overcrowding in the British
Isles, Institute of British Geographers, Transactions no.43,
April 1968, George Philip & Son, London.
Lawton R., 1968b, The journey to work in Britain: some
trends and problems, Regional Studies, Vol.2, pp.27-40.
Lawton R., 1986c, Population, J. Langton & R.J. Morris
(eds), Atlas of Industrializing Britain 1780-1914, Methuen,
London.
Leadbeater C., 1989, In the land of the dispossessed, L.
McDowell, P.Sarre and C. Hamnett (eds), Divided Nation:
Social and Cultural Change in Britain, Hodder and
Stoughton & The Open University, London.
209
Learmonth A.T.A. & Pal M.N., 1959, A method of plotting
two variables (such as mean incidence and variability from
year to year) on the same map, using isopleths, Erdkunde,
Vol.13, No.2, pp.145-150.
Leaverton P.E., 1976, Automated cartography and
epidemiology introduction, Proceedings of the Workshop
on Automated Cartography and Epidemiology, National
Center for Health Statistics, U.S. Department of Health,
pp.7-8.
LeBlanc J., Ward M.O. & Wittels N., 1990, Exploring Ndimensional databases, Visualization '90, Proceedings of
the First IEEE Conference on Visualization, pp.230-237,
October, San Francisco, California.
Lee C., 1986, Regional structure and change, J. Langton &
R.J. Morris (eds), Atlas of Industrializing Britain 17801914, Methuen, London.
Bibliography
Lewis J. & Townsend A., 1989b, The north-south divide:
regional change in Britain in the 1980s, Paul Chapman,
London.
Lewis P.F., 1969, Impact of negro migration on the
electoral geography of Flint, Michigan, 1932-1962, R.E.
Kasperson and J.V.Minghi (eds) The Structure of Political
Geography, University of London Press.
Ley D., 1989, Modernism, post-modernism, and the struggle
for place, J.A. Agnew & J.S. Duncan (eds) The Power of
Place: Bringing together geographical and sociological
imaginations, Chapter 4, Unwin Hyman, Boston.
Lindenberg R.E., 1986, The effect of color on quantitative
map symbol estimation, PhD, University of Kansas.
Lindenberg R.E., 1988, The use of student subjects in
psychophysical tests of map symbols, Technical papers of
the ACSM-ASPRS Annual Convention, St. Louis,
Missouri, Vol.2, pp.76-82.
Leenaers H., Burrough P.A. & Okx J.P., 1989, Efficient
mapping of heavy metal pollution on floodplains by cokriging from elevation data, J. Raper (ed), GIS: Three
Dimensional Applications in Geographic Information
Systems, Taylor & Francis, London.
Lindenburg M.B., 1987, Dot map similarity: visual and
quantitative, PhD, no. 8813422, University of Kansas,
USA.
Leete R. & Fox J., 1977, Registrar general's social classes:
origins and uses, Population Trends, Vol.8, pp.1-7.
Linton M., 1991, Poll tax puts brake on council spending,
The Guardian, March 19th, p.6.
Legates D.R. & Willmott C.J., 1986, Interpolation of point
values From isoline maps, The American Cartographer,
Vol.13, No.4, pp.308-323.
Lloyd P.E. & Dicken P., 1983, The components of change
in metropolitan areas: events in their corporate context,
J.B. Goddard & A.G. Champion (eds) The Urban and
Regional Transformation of Britain, Methuen, London.
Leifer L. & Mark D., 1987, Recursive approximations of
topographic data using quadtrees and orthogonal
polynomials, AutoCarto 8 (Proceedings of the 8th
International Symposium on Computer-Assisted
Cartography), pp.650-659 .
Lelewer D.A. & Hirschberg D.S., 1988, Data compression,
Association for Computer Machinery Computing Surveys,
Vol.19(3), pp.261-296.
Lloyd R. & Steinke T., 1985, Comparison of quantitative
point symbols / the cognitive process, Cartographica,
Vol.22, No.1, pp.59-77.
Lo C.P., 1973, Cartographic presentation of threedimensional urban information, The Cartographic Journal,
Vol.10, No.2, pp.77-84.
Lockwood A., 1969, Diagrams, Watson-Guptill, New York.
Lenz R., Gudmundsson B., Lindskog B. & Danielsson
P.E., 1986, Display of density volumes, IEEE Computer
Graphics and Applications, Vol.6, No.7, pp.20-29.
Leonard J.J., 1987, The equal value grey scale applied to
laser printer output, M.Sc, University of WisconsinMadison.
Leonard J.J. & Buttenfield B.P., 1989, An equal value
grey scale for laser printer mapping, The American
Cartographer, Vol.16, No.2, pp.97-107.
Levison M.E. & Haddon W., 1965, The area adjusted map:
an epidemiological device, Public Health Reports, Vol.80,
No.1, pp.55-59.
Levkowitz H., 1988, Color in computer graphic
representation of two-dimensional parameter distributions,
PhD No.DA8824759, University of Pennsylvania.
Levoy M., 1988, Display of surfaces from volume data,
IEEE Computer Graphics and Applications, Vol.8, No.5,
pp.29-37.
Lewis J. & Townsend A., 1989a, Introduction, J. Lewis &
A. Townsend (eds), The North-South divide: Regional
change in Britain in the 1980s, Paul Chapman, London.
Lohse J., Rueter H., Biolsi K. & Walker N., 1990,
Classifying visual knowledge representation: a foundation
for visualization research, Visualization '90, Proceedings of
the First IEEE Conference on Visualization, pp.131-138,
October, San Francisco, California.
Long M.B., Lyons K. & Lam J.K., 1990, Acquisition and
representation of two- and three- dimensional data from
turbulent, G.M. Nielson, B. Shriver & L.J. Rosenblum
(eds) Visualization in Scientific Computing, pp.132-139,
IEEE Computer Society Press, California.
Loon J.C., Patias P.G. & Mandel B.A., 1985, Evaluation
procedures for testing contour-to-grid interpolation
methods using synthetic surfaces, AutoCarto 7
(Proceedings of the 7th International Symposium on
Computer-Assisted Cartography), Washington D.C., p.342.
Lorenz M.O., 1905, Methods of measuring the
concentration of wealth, Publications of the American
Statistical Association, Vol.9, No.90, pp.209-219.
Lorre J.J., 1988, Image processing in optical astronomy,
J.P. Muller (ed) Digital Image Processing in Remote
Sensing, Taylor and Francis, London, pp.243-269.
Bibliography
Lovejoy S. & Schertzer D., 1988, Extreme variability,
scaling and fractals in remote sensing: analysis and
simulation, J.P. Muller (ed) Digital Image Processing in
Remote Sensing, Taylor and Francis, London, pp.177-212.
Loxton J., 1985, The Peters phenomenon, The Cartographic
Journal, Vol.22, pp.106-108.
Lubinsky D. & Pregibon D., 1987, Data analysis as
search, B. Phelps (ed) Interactions in Artificial Intelligence
and Statistical Methods, Gower Technical Press, Aldershot,
pp.18-35.
Lukatela H., 1987, HIPPARCHUS geopositioning model:
an overview, AutoCarto 8 (Proceedings of the 8th
International Symposium on Computer-Assisted
Cartography), pp.87-96 .
Lukatela H., 1989a, Hipparchus data structures: points,
lines and regions in spherical voronoi grid, Auto Carto 9,
pp.164-170, Baltimore, Maryland.
Lukatela H., 1989b, GIS future: automated cartography or
geo-relational solid modelling?, Auto Carto 9, pp.341-347,
Baltimore, Maryland.
Lusby-Taylor C., 1986, A rectangular tessellation with
computational and database advantages, S.B.M. Bell and
B.M. Diaz (eds) Spatial Data Processing using Tesseral
Methods, pp.391-402.
Lycan R., 1980, Alternatives to the population pyramid for
mapping age-sex characteristics of metropolitan census
tracts, AutoCarto IV (Proceedings of the International
Symposium on Cartography and Computing: Applications
in Health and the Environment, Virginia), Vol.2, pp.172179.
Lyutyy A.A., 1989, Current problems of geographical
cartography, Mapping Sciences and Remote Sensing,
Vol.26, No.1, pp.1-13.
Löytönen M., 1991, The spatial diffusion of human
immunodeficiency virus type 1 in Finland, 1982-1997,
Annals of the Association of American Geographers,
Vol.81, No.1, pp.127-151.
MacDonald C.L. & Crain I.K., 1985, Applied computer
graphics in a geographic information system: problems and
successes, IEEE Computer Graphics and Applications,
Vol.5, No.10, pp.34-39.
MacEachren A.M., 1979, The evolution of thematic
cartography / a research methodology and historical
review, Canadian Cartographer, Vol.16, No.1, pp.17-33.
MacEachren A.M., 1982a, Thematic map accuracy: the
influence of enumeration units size and compactness,
ACSM-ASP 42nd Annual Convention, March, Denver
Colorado, pp.512-521.
MacEachren A.M., 1982b, Choropleth map accuracy:
characteristics of the data, AutoCarto 5 (Proceedings of the
5th International Symposium on Computer-Assisted
Cartography), pp.499-507.
MacEachren A.M., 1982c, Map complexity: comparison
and measurement, The American Cartographer, Vol.9,
No.1, pp.31-46.
210
MacEachren A.M., 1985, Accuracy of thematic maps /
implications of choropleth symbolization, Cartographica,
Vol.22, No.1, pp.38-58.
MacEachren A.M., 1986, A linear view of the world: strip
maps as a unique Form of cartographic representation,
The American Cartographer, Vol.13, No.1, pp.7-25.
MacEachren A.M., 1987, The evolution of computer
mapping and its implications for geography, Journal of
Geography, Vol.86, No.3, pp.100-108.
MacEachren A.M. & Davidson J.V., 1987, Sampling and
isometric mapping of continuous geographic surfaces, The
American Cartographer, Vol.14, No.4, pp.299-320.
Machover C., 1973, Future of computer graphics, Why is
Computer Graphics Always a Year Away?,Computer
Graphics, Quarterly report of Siggraph-ACM, Vol.8, No.1,
pp.49-63.
MacKay D.B. & Villarreal A., 1987, Performance
differences in the use of graphic and tabular displays of
multivariate data, Decision Sciences, Vol.18, No.4, pp.535546.
Mackinlay J., 1986, Automating the design of graphical
presentations of relational information, ACM Transactions
on Graphics, Vol.5, No.2, pp.110-141.
MacLane S., 1980, Mathematical models of space,
American Scientist, Vol.68, pp.184-191.
MacLeod D.I.A., 1986, Computer controlled color displays
in vision research: possibilities and problems, Color
Research and Application (Supplement), Vol.11 pp.45-46.
MacMahon B., 1957, Geographic variation in leukaemia
mortality in the United States, Public Health Reports,
Vol.72, No.1, pp.39-46.
Madgwick P.J. & Balsom D., 1980, Voting patterns, H.
Carter (ed) National Atlas of Wales, University of Wales
Press.
Maeder A.J., 1990, Animation techniques for chain-coded
objects, Visualization '90, Proceedings of the First IEEE
Conference on Visualization, pp.67-73, October, San
Francisco, California.
Magnenat-Thalmann N. & Thalmann D., 1987, An
indexed bibliography on image synthesis, IEEE Computer
Graphics and Applications, Vol.7, No.8, pp.27-38.
Magnenat-Thalmann N. & Thalmann D., 1989, Computer
graphics and animation, Computer Physics Reports,
Vol.11, Nos.1-6, pp.221-291.
Maling D.H., 1984, Mathematical cartography, Basic
Cartography for Students and Technicians volume I
(chapter 2), International Cartographic Association.
Mandelbrot B.B., 1980, Fractal aspects of the iteration of
z –> lz (1 - z), Annals of the New York Academy of
Sciences, Vol.357, pp.249-259.
Mandelbrot B.B., 1983, The fractal geometry of nature,
W.H.Freeman & Co., San Francisco.
211
Mandl P., 1990, A shell for a geographical data
visualization system (GDVS) on PC using some commercial
available programs, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.1,
pp.443-449.
Manuel T., 1988, Graphics Visualization is a real eye
opener, Electronics, Vol.61, No.6, pp.91-92.
Mao X., Kunii T.L., Fujishiro I. & Noma T., 1987,
Hierarchical representations of 2D/3D gray-scale images
and their 2D/3D two-way conversion, IEEE Computer
Graphics and Applications, Vol.7, No.12, pp.37-44.
Bibliography
Martin D., 1991, Understanding socioeconomic geography
from the analysis of surface form, EGIS '91 Proceedings,
pp.691-699, Brussels, Belgium.
Martin R., 1989, The political economy of Britain's northsouth divide, J. Lewis & A. Townsend (eds), The NorthSouth divide: Regional change in Britain in the 1980s, Paul
Chapman, London.
Martis K.C. (ed), 1989, The historical atlas of political
parties in the united states congress 1789-1989, Macmillan
Publishing Company, New York.
Marchand B., 1973, Deformation of a transportation
surface, Annals of the Association of American
Geographers, Vol.63, No.4, pp.507-521.
Mashoka Z., Bloemer H.H.L. & Pickles J., 1986, Dot
maps vs. proportional circle maps: an assessment of
readability, legibility and preference , The Society of
University Cartographers Bulletin, Vol.20, No.1, pp.1-6.
Marcotte L. & Tessier Y., 1987, Applied research and
instructional atlas design / the 'ten commandments' of
l'interatlas, Cartographica, Vol.24, No.1, pp.101-117.
Mason D.C. & Bell S.B.M., 1986, Hierarchical texture by
tesseral address, S.B.M. Bell and B.M. Diaz (eds) Spatial
Data Processing using Tesseral Methods, pp.403-410.
Mark D.M., 1983, Automated detection of drainage
networks from digital elevation models, AutoCarto 6
(Proceedings of the 6th International Symposium on
Computer-Assisted Cartography), Vol.2, pp.288-298.
Mason D.C., Bell S.B.M., Cross A., Farnell E. &
Harrison A., 1986, Image segmentation using a
hierarchical data structure, S.B.M. Bell and B.M. Diaz
(eds) Spatial Data Processing using Tesseral Methods,
pp.313-325.
Mark D.M., 1987, Recursive algorithm for determination of
proximal (Thiessen) polygons in any metric space,
Geographical Analysis, Vol.19, No.3, pp.264-272.
Mark D.M. & Goodchild M.F., 1986, On the ordering of
two-dimensional space: introduction and relation to
tesseral principles, S.B.M. Bell and B.M. Diaz (eds) Spatial
Data Processing using Tesseral Methods, pp.179-192.
Mark D.M. & Lauzon J.P., 1984, Linear quadtrees for
geographic information systems, Proceedings of the
International Symposium on Spatial Data Handling, pp.412430, August, Zurich, Switzerland.
Mark D.M. & Lauzon J.P., 1985, Approaches for
quadtree-based geographic information systems at
continental or global scales, AutoCarto 7 (Proceedings of
the 7th International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.355-364.
Markova E.E., 1970, Utilization of transport maps for
regional planning, Proceedings of the International
Conference on Transportation Maps, p.24, Budapest.
Marquand J., 1983, The changing distribution of service
employment, J.B. Goddard & A.G. Champion (eds) The
Urban and Regional Transformation of Britain, Methuen,
London.
Marr D., 1982, Vision, W.H. Freeman & Co., New York.
Marshall R., Wilson R. & Carlson W., 1980, Procedure
models for generating three-dimensional terrain, Computer
Graphics, Vol.14, No.3, pp.154-159.
Martin D., 1989, Mapping population data from zone
centroid locations, Transactions of the Institute of British
Geographers, Vol.14, pp.90-97.
Martin D., 1990, Automated mapping of 1991 census data
without boundary digitization, BURISA, Vol.96, pp.4-6.
Masser I., 1976, The design of spatial systems for internal
migration analysis, Regional Studies, Vol.10,pp.39-52.
Massey D.S. & Stephan G.E., 1977, The size-density
hypothesis in Great Britain: analysis of a deviant case,
Demography, Vol.14, No.3, pp.351-361.
Massey D.S., Tedrow L.M. & Stephan G.E., 1980,
Regional population density and county size: a note on the
problem of tautology in size-density relationships,
Geographical Analysis, Vol.12, No.2, pp.184-188.
Massey J.S., O'Shea J.B. & Poliness J.S., 1984,
Choropleth maps of census data on microfiche,
Cartography, Vol.13, No.4, pp.280-293.
Mastin G.A., Watterberg P.A. & Mareda J.F., 1987,
Fourier synthesis of ocean scenes, IEEE Computer
Graphics and Applications, Vol.7, No.3, pp.16-23.
Matsuba I., 1989, Optimal simulated-annealing method
based on stochastic-dynamic programming, Physical
Review A, Vol.39, No.5, pp.2635-2642.
Matthews G.J., 1970, Problems of mapping transportation
data in South Onterio, Proceedings of the International
Conference on Transportation Maps, pp.9-16, Budapest.
Matthias K., 1989, Quantificational reductionism as a risk
in geography and cartography, GeoJournal, Vol.18, No.4,
pp.441-450.
Max N.L., 1988, Spherical harmonic molecular surfaces,
IEEE Computer Graphics and Applications, Vol.8, No.7,
pp.42-50.
Mayhew L., 1986, Urban hospital location, George Allen
and Unwin, London.
McAbee J.L., 1991, Spatial modelling and visualization:
methodologies for an integration - pathways to
understanding, EGIS '91 Proceedings, pp.707-716,
Brussels, Belgium.
Bibliography
McCarty H.H. & Salisbury N.E., 1961, Visual comparison
of isopleth maps as a means of determining correlations
between spatially distributed phenomena, Department of
Geography, State University of Iowa, Iowa City,
Monograph No.3.
212
McKee C., 1989d, The 1971/1981 census change files: a
user's view and projection to 1991, SERRL Working
Report Number 13, Department of Geography, Birkbeck
College, University of London.
McCleary G.F., 1975, In pursuit of the map user, Autocarto 2 (Proceedings of the International Symposium on
Computer-Assisted Cartography), pp.238-250.
McKeown D.M., 1987, The role of artificial intelligence in
the integration of remotely sensed data with geographic
information systems, IEEE Transactions on Geoscience and
Remote Sensing, Vol.25, No.3, pp.330-348.
McCleary G.F., 1988, The war atlas: armed conflict armed peace (review), Cartographica, Vol.25, No.3,
pp.147-149.
McLaren R.A., 1989, Visualization techniques and
applications within GIS, Auto Carto 9, pp.5-14, Baltimore,
Maryland.
McCormick B.H. et al, 1987, Visualization in scientific
computing, B.H. McCormick, T.A. DeFanti and M.D.
Brown (eds), Computer Graphics, Vol.21, No.6.
McLaren R.A. & Kennie T.J.M., 1989, Visualisation of
digital terrain models: techniques and applications , J.
Raper (ed), GIS: Three Dimensional Applications in
Geographic Information Systems, Taylor & Francis,
London.
McCormick B.H., DeFanti T.A. & Brown M.D. (eds),
1987, Visualization in scientific computing- a synopsis,
IEEE Computer Graphics and Applications, Vol.7, No.7,
pp.61-70.
McCullagh M.J., 1982, Mini/micro display of surface
mapping and analysis techniques, Cartographica Perspectives in the Alternative Cartography, Vol.19, No.2,
Monograph 28, pp.136-144.
McCullagh M.J., 1988, Terrain and surface modelling
systems: theory and practice, Photogrammetric Record,
Vol.12, No.72, pp.747-779.
McDonald J.A., 1988, Orion I: interactive graphics for data
analysis, W.S. Cleveland & M.E. McGill (eds) Dynamic
Graphics For Statistics, Wandsworth, California.
McGlashan N.D., 1977, Dean technique challenged, The
American Journal of Public Health, Vol.67, No.4, pp.380381.
McGranaghan M., 1989, Ordering choropleth map
symbols: the effect of background, The American
Cartographer, Vol.16, No.4, pp.279-285.
McKay F.W., 1975, A holographic solution to the problem
of three-dimensional display in thematic cartography, M.A.
Thesis, Department of Geography, Michigan State
University.
McKay F.W., 1976, Automated cartography for cancer
research, Proceedings of the Workshop on Automated
Cartography and Epidemiology, National Center for Health
Statistics, U.S. Department of Health, pp.9-17.
McKee C., 1989a, Local level socio-economic and
demographic change in the south-east of England, 1971 to
1981, Birkbeck College, University of London, DPhil
dissertation, Geography Department.
McKee C., 1989b, Analysis of census data available for
measuring local level demographic and socio-economic
change in the South East of England between 1971 and
1981, SERRL Working Report Number 11, Department of
Geography, Birkbeck College, University of London.
McKee C., 1989c, Manpower statistics and social survey
data, their availability and suitability for measuring local
scale change in South East England, SERRL Working
Report Number 12, Department of Geography, Birkbeck
College, University of London.
Medyckyj-Scott D., 1991, The presentation of spatial data,
Mapping Awareness, Vol.5, No.5, pp.19-22.
Meihoefer H.J., 1969, The utility of the circle as an effective
cartographic symbol, Canadian Cartographer, Vol.6, No.2,
pp.105-117.
Meihoefer H.J., 1973, The visual perception of the circle of
the circle in thematic maps/ experimental results, Canadian
Cartographer, Vol.10, No.1, pp.63-84.
Meine K.H., 1979, Thematic mapping: present and future
capabilities, World Cartography, Vol.15, pp.1-16.
Merrett S. & Sharp C., 1991, The denarius hypothesis:
house price inflation in owner occupied London, M.
Satsangi (ed) Changing Housing Finance Systems, Chapter
9, Centre for Housing Research, University of Glasgow.
Merrifield R.M., 1987, Visual parameters for color CRTs,
H.J. Durrett (ed) Color and the Computer, Ch.3, pp.63-82.
Mersey J.E., 1984, The effects of color scheme and number
of classes on choropleth map communication, PhD No.
DA85-00688, University of Wisconsin Madison.
Meyer M.A., 1981, Human understanding of two-variable
maps, The American Statistician, Vol.35, No.1, pp.56-57.
Meyer M.A., Broome F.R., & Schweitzer R.H., 1975,
Color statistical mapping by the US bureau of the census,
The American Cartographer, Vol.2, No.2, pp.100-117.
Meyers R.J. & Stephenson M.B., 1990, Ray traced scalar
fields with shaded polygonal output, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.263-272, October, San Francisco, California.
Migration Analysis Unit, OPCS, 1979, International
migration: recent trends, Population Trends, Vol.18, pp.2417.
Mihalisin T., Gawlinski E., Timlin J. & Schwegler J.,
1990, Visualizing a scalar field on a N-dimensional lattice,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.255-262, October, San Francisco,
California.
213
Miles R., 1989, Racism and class structure: migrant labour
in contemporary capitalism, L. McDowell, P.Sarre and C.
Hamnett (eds), Divided Nation: Social and Cultural Change
in Britain, Hodder and Stoughton & The Open University,
London.
Miller G.S.P., 1988, The motion dynamics of snakes and
worms, Computer Graphics, Vol.22, No.4, pp.169-178.
Miller J.V., Breen D.E. & Wozny M.J., 1990, Extracting
geometric models through constraint minimization,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.74-82, October, San Francisco,
California.
Miller W.L., 1977, Electoral dynamics in Britain since
1918, The Macmillan Press Ltd, London.
Miller W.L., 1990, Voting and the electorate, P. Dunleavy,
A. Gamble & G. Peele (eds) Developments in British
Politics 3, Macmillan, London.
Miller W.L., Raab G. & Britto K., 1974, Voting research
and the population census 1918-71: surrogate data for
constituency analyses, Journal of the Royal Statistical
Society A, Vol.137, part 3, pp.384-411 .
Mills M.I., 1981, A study of the human response to pictoral
representations in telidon, Telidon Behavioural Research 3,
The Behavioural Research and Evaluation Group,
Information Branch, Ottawa, Canada.
Bibliography
Moellering H., 1980a, The real-time animation of threedimensional maps, The American Cartographer, Vol.7,
No.1, pp.67-75.
Moellering H., 1980b, Strategies of real-time cartography,
The Cartographic Journal, Vol.17, No.1, pp.12-15.
Moellering H., 1989, A practical and efficient approach to
the stereoscopic display and manipulation of cartographic
objects, Auto Carto 9, pp.1-4, Baltimore, Maryland.
Moellering H., 1990, 3-d stereoscopic surface exploration
in interactive real time, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.1,
pp.381-389.
Moellering H., 1991, Whither analytical cartography?,
Cartography and Geographic Information Systems, Vol.18,
No.1, pp.7-9.
Moellering H. & Kimerling A.J., 1990, A new digital
slope-aspect display process, Cartography and Geographic
Information Systems, Vol.17, No.2, pp.151-159.
Mohamed B., 1986, Realistic 3-D displays from
cartographic data, PhD No.D74146'87, University of
Sussex.
Molenaar M., 1990, A formal data structure for three
dimensional vector maps, Proceedings of the 4th
International Symposium on Spatial Data Handling, Zurich,
Vol.2, pp.830-843.
Miner R.A. & Canning J., 1988, A low cost imaging
workstation using the commodore amiga and NEC's image
pipelined processors, Electronic Imaging '88, Anaheim,
California, pp.422-427.
Molho I., 1990, A behavioural map of commuting distances
in the London region, Environment and Planning A,
Vol.22, pp.779-792.
Mingolla E., 1986, Computer graphics and surface
perception, Behavior Research Methods, Instruments, &
Computers, Vol.18, No.6, pp.531-534.
Molinari J., 1988, Hue-saturation-intensity color image
processing, Electronic Imaging '88, Anaheim, California,
pp.110-111.
Mirante A. & Weingarten N., 1982, The radial sweep
algorithm for constructing triangulated irregular networks,
IEEE Computer Graphics and Applications, Vol.2, No.5,
pp.11-21.
Møller Jensen O., Carstensen B., Glattre E., Malker B.,
Pukkala E. & Tulinius H., 1988, Atlas of cancer incidence
in the Nordic countries, Nordic Cancer Union, The Cancer
Societies of Denmark, Finland, Iceland, Norway and
Sweden.
Mishler W., Hoskin M. & Fitzgerald R., 1989, British
parties in the balance: a time-series analysis of long-term
trends in Labour and Conservative support, British Journal
of Political Science, Vol.19, pp.211-236.
Mitchell A.P., 1985, An approach to microcomputer-based
cartographic modeling, AutoCarto 7 (Proceedings of the
7th International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.377-386.
Monmonier M.S., 1974, Measures of pattern complexity for
choroplethic maps, The American Cartographer, Vol.1,
No.2, pp.159-169.
Monmonier M.S., 1975, Class intervals to enhance the
visual correlation of choropleth maps, Canadian
Cartographer, Vol.12, No.2, pp.161-178.
Mitchell B.R. & Deane P., 1962, Abstract of British
historical statistics, Cambridge University Press,
Cambridge.
Monmonier M.S., 1976, Modifying objective functions and
constraints for maximizing visual correspondence of
choroplethic maps, Canadian Cartographer, Vol.13, No.1,
pp.21-34.
Mitchell B.R. & Jones H.G., 1971, Second abstract of
British historical statistics, Cambridge University Press,
Cambridge.
Monmonier M.S., 1977a, Maps, distortion and meaning,
The Association of American Geographers, Resource paper
No.75-4.
Moellering H., 1973, The automated mapping of traffic
crashes, Surveying and Mapping, Vol.33, pp.467-477.
Monmonier M.S., 1977b, Nonlinear reprojection to reduce
the congestion of symbols on thematic maps, The Canadian
Cartographer, Vol.14, No.1, pp.35-47.
Moellering H., 1975, Interactive cartography, Auto-carto 2
(Proceedings of the International Symposium on ComputerAssisted Cartography), pp.415-421.
Monmonier M.S., 1978a, Viewing azimuth and map clarity,
Annals of the Association of American Geographers,
Vol.68, No.2, pp.180-195.
Bibliography
214
Monmonier M.S., 1978b, Modifications of the choropleth
technique to communicate correlation, The International
Yearbook of Cartography, Vol.18, pp.143-158.
Morgan J.D., 1986, Negative numbers and symbolism,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.111-125.
Monmonier M.S., 1979, An alternative isomorphism for
mapping correlation, The International Yearbook of
Cartography, Vol.19, pp.77-89.
Morgan, J.M., 1987, Academic geographic information
systems education: a commentary, Photogrammetric
Engineering and Remote Sensing, Vol.53, No.10, pp.14431445.
Monmonier M.S., 1980, The hopeless pursuit of
purification in cartographic communication: a comparison
of graphic-art and perceptual distortions of graytone
symbols, Cartographica, Vol.17, No.1, pp.24-39.
Monmonier M.S., 1981, Trends in atlas development,
Cartographica, Monograph 27. L. Guelke (ed) Maps in
Modern Geography - Geographical Perspectives on the
New Cartography, Vol.18, No.2, pp.187-213.
Monmonier M.S., 1982, Computer assisted cartography:
principles and prospects, Prentice Hall, pp.122-124.
Monmonier M.S., 1985, Technological transition in
cartography, The University of Wisconsin Press, pp.110113.
Monmonier M.S., 1990, Strategies for the interactive
exploration of geographic correlation, Proceedings of the
4th International Symposium on Spatial Data Handling,
Zurich, Vol.1, pp.512-521.
Monmonier M.S. & Schnell G.A., 1984, Land use and land
cover data and the mapping of population density, The
International Yearbook of Cartography, Vol.24, pp.115121.
Monmonier M.S. & Schnell G.A., 1988, Map appreciation,
Prentice Hall, New Jersey.
Montine J.L., 1990, A procedural interface for volume
rendering, Visualization '90, Proceedings of the First IEEE
Conference on Visualization, pp.36-44, October, San
Francisco, California.
Moore C., 1990, Unpredictability and undecidability in
dynamical systems, Physical Review Letters, Vol.64,
No.20, pp.2354-2357.
Moore R.E., 1983, The influence of color and the figureground relationship, Msc No.1103662, University of South
Carolina.
Moore R.E.M. & Angell I.O., 1986, The mean hexagon,
S.B.M. Bell and B.M. Diaz (eds) Spatial Data Processing
using Tesseral Methods, pp.369-390.
Moore R.V., 1983, Digitizing the United Kingdom river
network, AutoCarto 6 (Proceedings of the 6th International
Symposium on Computer-Assisted Cartography), Vol.2,
pp.331-338.
Moran P.A.P., 1948, The interpretation of statistical maps,
Journal of the Royal Statistical Society B, Vol.10, No.2,
pp.243-251.
Moravec H., 1989, Human culture: a genetic takeover
underway, C.G. Langton (ed), Artificial Life, vol.VI,
Addison-Wesley, USA.
Morgan C. & Denham C., 1982, Census small area
statistics (SAS): measuring change and spatial variation,
Population Trends, Vol.28, pp.12-17.
Morrill R.L., 1970, The shape of diffusion in space and
time, Economic Geography, Vol.46, pp.259-268.
Morris D.G. & Flavin R.W., 1990, A digital terrain model
for hydrology, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.1,
pp.250-262.
Morrison A., 1970, Maps of road travel speed, Proceedings
of the International Conference on Transportation Maps,
pp.38-53, Budapest.
Morrison A., 1971, Experimental maps of road travel
speed, The Cartographic Journal, Vol.8, pp.115-132.
Morrison A., 1980, Existing and improved road
classifications for British road maps, related to the speed of
traffic, The Cartographic Journal, Vol.17, No.2, pp.111123.
Morrison J.L., 1975, Map generalization: theory, practice
and economics, Auto-carto 2 (Proceedings of the
International Symposium on Computer-Assisted
Cartography), pp.99-122.
Morrison J.L., 1985, Automated cartography: international
perspectives on achievements and challenges (review),
Cartographica, Vol.22, No.2, pp.122-123.
Morrison J.L., 1988, The population atlas of china
(review), The American Cartographer, Vol.15, No.4,
pp.416-419.
Moses L.E., 1987, Graphical methods in statistical analysis,
Annual Review of Public Health, Vol.8, pp.309-53.
Mounsey H. & Clarke J.I., 1981, Comparison of
population changes within Great Britain during 1971-75
and 1975-79, Census Research Unit Working Paper No.18,
Department of Geography, University of Durham.
Mounsey H.M., 1982a, Mapping population change
through time by computer and cine film, D.Rhind &
T.Adams (eds) Computers in Cartography, pp.127-132,
British Cartographic Society, Special Publication No.2,
London.
Mounsey H.M., 1982b, The cartography of time-changing
phenomena: the animated map, PhD, Department of
Geography, Durham University.
Muehrcke P.C., 1969, Visual pattern analysis: a look at
maps, PhD, University of Michigan.
Muehrcke P.C., 1972, Thematic cartography, Resource
Paper No.19, Association of American Geographers,
Washington.
Muehrcke P.C., 1978, Map use: reading, analysis, and
interpretation, JP Publications, Madison, Wisconsin.
215
Muehrcke P.C., 1981, Maps in geography, Cartographica,
Monograph 27. L. Guelke (ed) Maps in Modern Geography
- Geographical Perspectives on the New Cartography,
Vol.18, No.2, pp.1-41.
Bibliography
Murayama Y., 1990, Regional structure of commodity flows
in Japan: an application of dynamic geographical field
theory, Science Reports of the Institute of Geoscience,
University of Tsukuba, Section A Geographical Sciences,
Vol.11, pp.79-114.
Mueser P., 1989, The spatial structure of migration: an
analysis of flows between states in the USA over three
decades, Regional Studies, Vol.23.3, pp.185-200.
Murch G.M., 1987, Color displays and color science, H.J.
Durrett (ed) Color and the Computer, Ch.1, pp.1-26.
Muller J.C., 1975, Statistical accuracy and map
enhancement in choroplethic mapping, Auto-carto 2
(Proceedings of the International Symposium on ComputerAssisted Cartography), pp.136-148.
Murch G.M. & Taylor J.M., 1986, The effective use of
color in visual displays: text and graphics applications,
Color Research and Application (Supplement), Vol.11
pp.3-10.
Muller J.C., 1976, Number of classes and choropleth
pattern characteristics, The American Cartographer, Vol.3,
No.2, pp.169-175.
Murphy M. J., 1989, Housing the people: from shortage to
surplus?, H. Joshi (ed) The Changing Population of Britain,
Basil Blackwell, Oxford.
Muller J.C., 1978, The mapping of travel time in Edmonton,
Alberta, Canadian Geographer, Vol.22, No.3, pp.195-210.
Murray J.S., 1987, The map is the message, The
Geographical Magazine, Vol.59, No.5, pp.237-241.
Muller J.C., 1979, Perception of continuously shaded maps,
Annals of the Association of American Geographers,
Vol.69, No.2, pp.240-249.
Musgrave F.K. & Mandelbrot B.B., 1989, Natura ex
machina, IEEE Computer Graphics and Applications,
Vol.9, No.1, pp.4-7.
Muller J.C., 1980, Visual comparison of continuously
shaded maps, Cartographica, Vol.17, No.1, pp.40-52.
Myers W.L., Evans B.M., Baumer G.M. & Heivly D.,
1989, Synergism between human interpretation and digital
pattern recognition in preparation of thematic maps,
ISPRS Journal of Photogrammetry and Remote Sensing,
Vol.44, pp.85-96.
Muller J.C., 1981, Bertin's theory of graphics / a challenge
to north american thematic cartography, Cartographica,
Vol.18, No.3, pp.1-8.
Muller J.C., 1982, Non-euclidean geographic spaces:
mapping functional distances, Geographical Analysis,
Vol.14, No.3, pp.189-203.
Neal M., 1988, When did scientific visualization really
begin?, IEEE Computer Graphics and Applications, Vol.8,
No.11, pp.8-9.
Muller J.C., 1985, Mental maps at a global scale,
Cartographica, Vol.22, No.4, pp.51-59.
Nelson B. & McGregor B., 1983, A modification of the
non-contiguous area cartogram, New Zealand
Cartographic Journal, Vol.12, No.2, pp.21-29.
Muller J.C., 1989, Changes ahead for the mapping
profession, Auto Carto 9 (International Symposium on
Computer-Assisted Cartography), pp.675-683.
Newman W., 1973, Where are we?, Why is Computer
Graphics Always a Year Away?, Computer Graphics,
Quarterly report of Siggraph-ACM, Vol.8, No.1, pp.12-31.
Muller J.C., 1990, Rule based generalization: potentials
and impediments, Proceedings of the 4th International
Symposium on Spatial Data Handling, Zurich, Vol.1,
pp.317-334.
Nguyen P.T. & Ho D., 1988, Multiple source data
processing in remote sensing, J.P. Muller (ed) Digital
Image Processing in Remote Sensing, Taylor and Francis,
London, pp.153-176.
Muller J.C. & Honsaker J.L., 1978, Choropleth map
production by facsimile, The Cartographic Journal, Vol.15,
No.1, pp.14-19.
Nielson G.M., 1989, Visualization in scientific computing,
Computer, Vol.22, No.8, pp.10-11.
Muller J.P., 1988, Computing issues in digital image
processing and remote sensing, J.P. Muller (ed) Digital
Image Processing in Remote Sensing, Taylor and Francis,
London, pp.1-19.
Muller J.P., Day T., Kolbusz J.P., Dalton M., Richards S.
& Pearson J.C., 1988, Visualisation of topographic data
using video animation, J.P. Muller (ed) Digital Image
Processing in Remote Sensing, Taylor and Francis, London,
pp.21-38.
Mulligan J.B., 1986, Minimizing quantization errors in
digitally-controlled CRT displays, Color Research and
Application (Supplement), Vol.11 pp.47-51.
Munsell Color Company, 1967, Munsell book of color,
Munsell Color Company Inc., Baltimore, Maryland.
Nielson G.M. & Foley T.A., 1989, A survey of applications
of an affine invariant norm, Lyche T. & Schumaker L.L.
(eds) Mathematical Methods in Computer Aided
Geometrical Design, Academic Press, Boston.
Nielson G.M. & Franke R., 1984, A method for
construction of surfaces under tension, Rocky Mountain
Journal of Mathematics, Vol.14, No.1, pp.203-221.
Nielson G.M. & Hamann B., 1990, Techniques for the
interactive visualization of volumetric data, Visualization
'90, Proceedings of the First IEEE Conference on
Visualization, pp.45-50, October, San Francisco, California.
Nielson G.M., Shriver B. & Rosenblum L.J. (eds), 1990,
Visualization in scientific computing, IEEE Computer
Society Press , Los Alamitos, California.
Bibliography
Nimmer J., Nash T. & Phillips D., 1982, Proportional
prism maps: a statistical mapping technique, AutoCarto 5
(Proceedings of the 5th International Symposium on
Computer-Assisted Cartography), pp.555-564.
Nitta T., Ikeda K. & Katayama T., 1989, Phase
equilibrium calculations and three-dimensional computer
graphics representation, Fluid Phase Equilibria, Vol.53,
pp.105-112.
216
Olson J.M., 1975c, Experience and the improvement of
cartographic communication, The Cartographic Journal,
Vol.12, No.2, pp.94-108.
Olson J.M., 1976a, A coordinated approach to map
communication improvement, The American Cartographer,
Vol.3, No.2, pp.151-159.
Olson J.M., 1976b, Noncontiguous area cartograms, The
Professional Geographer, Vol.28, pp.371-380.
Noronha V.T., 1987, Choropleth mapping in a
microcomputer environment: a critical evaluation of some
commercial implementations, The American Cartographer,
Vol.14, No.2, pp.139-154.
Olson J.M., 1977, Rescaling dot maps for pattern
enhancement, The International Yearbook of Cartography,
Vol.17, pp.125-137.
Norris P., 1983, Microdata from the British Census, D.
Rhind (ed) A Census User's Handbook, Methuen, London.
Olson J.M., 1979, Cognitive cartographic experimentation,
Canadian Cartographer, Vol.16, No.1, pp.34-44.
Norris P. & Mounsey H.M., 1983, Analysing change
through time, D. Rhind (ed) A Census User's Handbook,
Methuen, London.
Olson J.M., 1981, Spectrally encoded two-variable maps,
Annals of the Association of American Geographers,
Vol.71, No.2, pp.259-276.
Nyerges T.L., 1991, Analytical map use, Cartography and
Geographic Information Systems, Vol.18, No.1, pp.11-22.
Olson J.M., 1984, Cognitive issues in map use, The
International Yearbook of Cartography, Vol.24, pp.151157.
Nystuen J., 1965, Effects of boundary shape and the concept
of local convexity, Regional Science Association Annual
Meeting, November, Philadelphia, Pennsylvania.
O'Callaghan J.F., 1984, Map display techniques for
interactive colour mapping, Proceedings of the
International Symposium on Spatial Data Handling, pp.316323, August, Zurich, Switzerland.
O'Connell M.A., Bell S.B.M., Mason D.C. & Young
P.A.V., 1991, Handling four-dimensional geo-coded data,
EGIS '91 Proceedings, pp.756-764, Brussels, Belgium.
Olson J.M., 1987a, Color and the computer in cartography,
H.J. Durrett (ed) Color and the Computer, Ch.11, pp.205220.
Olson J.M., 1987b, Review: the elements of graphing data,
The American Cartographer, Vol.14, No.1, pp.88-89.
Ommer R.E. & Wood C.H., 1985, Data, concept and the
translation to graphics, Cartographica, Vol.22, No.2,
pp.44-62.
O'Keefe J.A. & Greenberg A., 1977, A note on the van der
Grinten projection of the whole earth onto a circular disk,
The American Cartographer, Vol.4, No.2, pp.127-132.
van Oosterom P. & Claassen E., 1990, Orientation
insensitive indexing methods for geometric objects,
Proceedings of the 4th International Symposium on Spatial
Data Handling, Zurich, Vol.2, pp.1016-1029.
O.P.C.S., 1975, Reorganisation of local government areas,
H.M.S.O., London.
OPCS, 1991, 1991 census preliminary report for England
and Wales, H.M.S.O., London.
O.P.C.S. , 1991, Boundary Changes (Revised to include
changes up to the end of 1990), User Guide: 149 (Revised),
Customer Services Section, OPCS, Titchfield.
Openshaw S., 1982, The modifiable areal unit problem,
Concepts and Techniques in Modern Geography No.38,
Geo books.
Öberg S., 1991, The population: the national atlas of
Sweden, Royal Swedish Academy of Sciences, Stockholm.
Openshaw S., 1983, Multivariate analysis of census data:
the classification of areas, D. Rhind (ed) A Census User's
Handbook, Methuen, London.
Ogilvy A.A., 1982, Population migration between the
regions of Great Britain, 1971-9, Population Studies,
Vol.16.1, pp.65-73.
Olson J.M., 1972a, Class interval systems on maps of
observed correlated distributions, Canadian Cartographer,
Vol.9, No.2, pp.122-131.
Olson J.M., 1972b, The effects of class interval systems on
choropleth map correlation, Canadian Cartographer, Vol.9,
No.1, pp.44-49.
Olson J.M., 1975a, Autocorrelation and visual map
complexity, Annals of the Association of American
Geographers, Vol.65, No.2, pp.189-204.
Olson J.M., 1975b, The organization of color on twovariable maps, Auto-carto 2 (Proceedings of the
International Symposium on Computer-Assisted
Cartography), pp.289-294.
Openshaw S., 1984, Ecological fallicies and the analysis of
areal census data, Environment and Planning A, Vol.16,
pp.17-31.
Ormeling F., 1988, Requirements for computer-assisted
map design courses, International Yearbook of
Cartography, Vol.XXVIII, pp.265-273.
Owen D.W. & Green A.E. , 1984, Modelling population
and sectoral employment change in British local labour
market areas, 1971-81, C.U.R.D.S., Newcastle University,
Discussion Paper, No. 61.
Owen D.W. & Green A.E., 1985, A comparison of the
changing spatial distribution of socio-economic groups,
employed in manufacturing and services, C.U.R.D.S.,
Newcastle University, Discussion Paper, No. 70.
217
Owen D.W. & Green A.E., 1989, Spatial aspects of labour
mobility in the 1980s, Geoforum, Vol.20, No. 1, pp.107126.
Owens J.R. & Wade L.L., 1988, Economic conditions and
constituency voting in Great Britain, Political Studies,
Vol.XXXVI, pp.30-51.
Pajon J.L. & Tran V.B., 1990, Visualization of scalar data
defined on a structured grid - applications to petroleum
research, Visualization '90, Proceedings of the First IEEE
Conference on Visualization, pp.281-288, October, San
Francisco, California.
Palacious-Velez O.L. & Cuevas-Renaud B., 1986,
Automated river-course, ridge and basin delineation from
digital elevation data, Journal of Hydrology, Vol.86,
pp.299-314.
Papathomas T.V. & Julesz B., 1988, The application of
depth separation to the display of large data sets, W.S.
Cleveland & M.E. McGill (eds) Dynamic Graphics For
Statistics, Wandsworth, California.
Papathomas T.V., Schiavone J.A. & Julesz B., 1987,
Stereo animation for very large data bases: case studymeteorology, IEEE Computer Graphics and Applications,
Vol.7, No.9, pp.18-27.
Papathomas T.V., Schiavone J.A. & Julesz B., 1988,
Applications of computer graphics to the visualization of
meteorological data, Computer Graphics, Vol.22, No.4,
pp.327-334.
Parkes D.N. & Thrift N., 1975, Timing space and spacing
time, Environment and Planning A, Vol.7 pp.651-670.
Parslow R., 1987, A new direction for computer graphics,
Computer Bulletin, September, pp.22-25.
Parvey C.A., 1983, A digital geographic base file for
Australian federal electoral divisions, Cartography, Vol.13,
No.1, pp.49-53.
Bibliography
Pearce D.T., 1987, Cartography - is there a future?,
Cartography, Vol.16, No.1, pp.75-78.
Peddle J.B., 1910, The construction of graphical charts,
McGraw-Hill Book Company, New York.
Pellom A.C., 1984, Clustering and the structural analysis of
mosaic distributions, PhD, no. 8508610, University of
North Carolina.
Penney W., 1988, Micro computer based color image
processing, Electronic Imaging '88, Anaheim, California,
pp.99-104.
Pereyra V. & Rial J.A., 1990, Visualizing wave
phenomenae with seismic rays, G.M. Nielson, B. Shriver &
L.J. Rosenblum (eds) Visualization in Scientific
Computing, pp.174-189, IEEE Computer Society Press,
California.
Petchenik B.B., 1975, Cognition in cartography, Auto-carto
2 (Proceedings of the International Symposium on
Computer-Assisted Cartography), pp.183-193.
Petchenik B.B., 1979, From place to space: the
psychological achievement of thematic mapping, The
American Cartographer, Vol.6, No.1, pp.5-12.
Petchenik B.B., 1985, Value and values in cartography,
Cartographica, Vol.22, No.3, pp.1-59.
Petermann A., 1852, England and Wales: distribution of
the population, Census of Great Britain, 1851, Population
Tables I, Her Majesty's Stationery Office, London.
Peters A.B., 1984a, A world map with Australia as focal
point, Cartography, Vol.13, No.3, pp.185-188.
Peters A.B., 1984b, Distance-related maps, The American
Cartographer, Vol.11, No.2, pp.119-131.
Peterson M.A., 1979, An evaluation of unclassed crossedline choropleth mapping, The American Cartographer,
Vol.6, No.1, pp.21-37.
Paslawski J., 1984, In search of a general idea of class
selection for choropleth maps, The International Yearbook
of Cartography, Vol.24, pp.159-171.
Peterson M.P., 1985, Evaluating a map's image, The
American Cartographer, Vol.12, No.1, pp.41-55.
Paslawski J., 1989, Is the choropleth presentation really a
map?, International Yearbook of Cartography, Vol.29,
pp.209-215.
Peterson M.P., 1987, The mental image in cartographic
communication, The Cartographic Journal, Vol.24, No.1,
pp.35-41.
Pattie C., 1990, On and on and on... must the conservatives
win? The electoral geography of Britain in the 1990s, Dept.
of Geography, Working Paper no. 4, University of
Nottingham.
Petrich M. & Tobler W.R., 1984, The globular projection
generalized, The American Cartographer, Vol.11, No.2,
pp.101-105.
Patton J.C. & Slocum T.A., 1985, Spatial pattern recall /
an analysis of the aesthetic use of color, Cartographica,
Vol.22, No.3, pp.70-87.
Peach C., 1982, The growth and distribution of the black
population in Britain 1945-1980, Demography of
immigrants and minority groups in the UK, ed. D.A.
Coleman, Academic Press, London.
Peach C., 1984, The force of West Indian island identity in
Britain, C. Clarke, D. Ley & C. Peach (eds), Geography
and Ethnic Pluralism, George Allen and Unwin, London.
Peak K.D., 1989, A new dimension, The Geographical
Magazine, Vol.61, No.9, pp.40-42.
Peucker T.K., 1972, Computer cartography, Association of
American Geographers, Washington D.C..
Peucker T.K., 1975, A theory of the cartographic line,
Auto-carto 2 (Proceedings of the International Symposium
on Computer-Assisted Cartography), pp.508-518.
Peucker T.K., 1980a, The impact of different mathematical
approaches to contouring, Cartographica, Monograph 25.
A.J. Kerr (ed) The Dynamics of Oceanic Cartography,
Vol.17, No.2, pp.73-95.
Peucker T.K., 1980b, The use of computer graphics for
displaying data in three dimensions, Cartographica,
Monograph 25. A.J. Kerr (ed) The Dynamics of Oceanic
Cartography, Vol.17, No.2, pp.59-72.
Bibliography
218
Peucker T.K., Fowler R.J. & Little J.J., 1980, The
triangulated irregular network, AutoCarto IV (Proceedings
of the International Symposium on Cartography and
Computing: Applications in Health and the Environment,
Virginia), Vol.2, pp.96-103.
Pike R.J., Thelin G.P. & Acevedo W., 1987, A topographic
base for GIS from automated TINs and image-processed
DEMs, GIS'87 Technical papers, ASPRS-ACSM Second
Annual International Conference, San Francisco, California,
pp.340-351.
Peuker T.K., 1972, Computer cartography: a working
bibliography, Discussion Paper No. 12, Department of
Geography, University of Toronto.
Pinch S. & Williams A., 1983, Social class change in
British cities, J.B. Goddard & A.G. Champion (eds) The
Urban and Regional Transformation of Britain, Methuen,
London.
Peuquet D.J., 1981a, An examination of techniques for
reformatting digital cartographica data / part 1: the rasterto-vector process, Cartographica, Vol.18, No.1, pp.34-48.
Peuquet D.J., 1981b, An examination of techniques for
reformatting digital cartographica data / part 2: the vectorto-raster process, Cartographica, Vol.18, No.3, pp.21-33.
Peuquet D.J., 1983, The application of artificial intelligence
techniques to very large geographic databases, AutoCarto
6 (Proceedings of the 6th International Symposium on
Computer-Assisted Cartography), Vol.1, pp.419-420.
Pinker S., 1983, Pattern perception and the comprehension
of graphs, Report of the Psychology Department (EDRS
No.ED 237-339), Massachusetts Institute of Technology,
Cambridge MA..
Pipkin J., 1975, The map as an information channel:
ignorance before and after looking at a choropleth map,
The Canadian Cartographer, Vol.12, No.1, pp.80-82.
Plantinga W.H., 1988, The asp: a continuous, viewer
centred object representation for computer vision, PhD
No.DA8820061, University of Wisconsin-Madison.
Peuquet D.J., 1984, Data structures for a knowledge-based
geographic information system, Proceedings of the
International Symposium on Spatial Data Handling, pp.372391, August, Zurich, Switzerland.
Plumb G.A. & Slocum T.A., 1986, Alternative designs for
dot-matrix printer maps, The American Cartographer,
Vol.13, No.2, pp.121-133.
Peuquet D.J., 1988, Representations of geographic space:
towards a conceptual synthesis, Annals of the Association
of American Geographers, Vol.78, No.3, pp.375-394.
Pocock D.C.D., 1960, The migration of scottish labour to
corby new town, Scottish Geographical Magazine, Vol.76,
pp.169-171.
Pham B., 1990, Spline-based color sequences for
univariate, bivariate and trivariate mapping, Visualization
'90, Proceedings of the First IEEE Conference on
Visualization, pp.202-208, October, San Francisco,
California.
Poiker T.K., 1985, Mapping information: the graphic
display of quantitative information (review), Cartographica,
Vol.22, No.2, pp.116-118.
Phillips D.J., 1977, The visual effectiveness of flow maps
and cartograms, M.A., University of Georgia.
Phillips R.J., 1989, Are maps different from other kinds of
graphic information?, The Cartographic Journal, Vol.26,
pp.24-25.
Phillips R.L., 1990a, Distributed visualization at Los
Alamos national laboratory, G.M. Nielson, B. Shriver &
L.J. Rosenblum (eds) Visualization in Scientific
Computing, pp.212-220, IEEE Computer Society Press,
California.
Phillips R.L., 1990b, An interpersonal multimedia
visualization system, Visualization '90, Proceedings of the
First IEEE Conference on Visualization, pp.338-341,
October, San Francisco, California.
Pickett R.M. & White B.W., 1966, Constructing data
pictures, Seventh National Symposium on Information
Display, October, Boston Massachusetts, pp.76-81.
Pickles A.R., Davies R.B. & Crouchley R., 1984,
Individual and geographical variation in longitudinal
voting data for England, 1964-1970, Environment and
Planning, Vol.16, pp.1237-1247.
Pickover C.A., 1989, Mathematics and beauty VIII:
tessellation automata derived from a single defect,
Computers and Mathematics with Applications, Vol.17,
No.1-3, pp.321-336.
Poiker T.K. & Griswold L.A., 1985, A step towards
interactive displays of digital elevation models, AutoCarto
7 (Proceedings of the 7th International Symposium on
Computer-Assisted Cartography), Washington D.C.,
pp.408-415.
Pond C., 1989a, The changing distribution of income,
wealth and poverty, C. Hamnett, L. McDowe, and P. Sarre
(eds), Restructuring Britain: The Changing Social Structure,
Sage publications, The Open University, London.
Pond C., 1989b, Wealth and the two nations, L. McDowell,
P.Sarre and C. Hamnett (eds), Divided Nation: Social and
Cultural Change in Britain, Hodder and Stoughton &The
Open University, London.
Population Statistics Division, OPCS, 1979, Geographical
area units, Population Trends, Vol.15, pp.16-19.
Population Statistics Division, OPCS, 1983, Population
definitions, Population Trends, Vol.33, pp.21-25.
Population Statistics Division, OPCS, 1984, Centres of
population of local authority districts, counties and
regions, 1981 and 1971, Population Trends, Vol.38, pp.3134.
Population Statistics Division, OPCS, 1987, Migration in
1986, Population Trends, Vol.50, pp.32-38.
Population Statistics Division, OPCS, 1988, Migration in
1987, Population Trends, Vol.54, pp.32-39.
219
Porch W.M., Barr S. Clements W.E., Archuleta J.A.,
Fernandez A.B., King C.W., Neff W.D. & Hosker R.P.,
1989, Smoke flow visualization in a tributary of a deep
valley, Bulletin of the American Meteorological Society,
Vol.70, No.1, pp.30-35.
Bibliography
Rallings C. & Thrasher M., 1985, The 1985 County
Council Election Results in England: a statistical digest,
Centre for the Study of Local Elections, Plymouth
Polytechnic.
Porter P. & Voxland P., 1986, Distortion in maps: the
Peters projection and other devilments, Focus, Vol.36,
pp.22-30.
Rallings C. & Thrasher M., 1986, The 1986 Metropolitan
Borough Council Election Results: a statistical digest,
Centre for the Study of Local Elections, Plymouth
Polytechnic.
Post A.R., 1969, Mobility analysis: an appropriate
technique in small area demography, Proceedings of the
Social Statistics Section of the American Statistical
Association, pp.261-266.
Rallings C. & Thrasher M., 1987, The 1987 Metropolitan
Borough Council Election Results: a statistical digest,
Centre for the Study of Local Elections, Plymouth
Polytechnic.
Post J.B., 1973, An atlas of fantasy, Mirage Press Ltd,
Baltimore.
Rallings C. & Thrasher M., 1988, Local elections
handbook 1988, Centre for the Study of Local Elections,
Plymouth Polytechnic.
Prashker S., 1989, Sliding Tolerance 3-D point reduction
for globograms, Auto Carto 9, pp.655-664, Baltimore,
Maryland.
Rallings C. & Thrasher M., 1989, Local elections
handbook 1989, Centre for the Study of Local Elections,
Polytechnic South West, Plymouth.
Pred A., 1984, Place as historically contingent process:
structuration and the time-geography of becoming places,
Annals of the Association of American Geographers,
Vol.74, No.2, pp.279-297.
Rallings C. & Thrasher M., 1990a, Local elections
handbook 1990, Centre for the Study of Local Elections,
Polytechnic South West, Plymouth.
Pred A., 1986, Place, practice and structure: social and
spatial transformation in Southern Sweden: 1750-1850,
Barnes and Noble Books, Totowa, New Jersey.
Rallings C. & Thrasher M., 1990b, Turnout in English
local elections - an aggregate analysis with electoral and
contextual data, Electoral Studies, Vol.9, No.2, pp.79-90.
Prescott J.R.V., 1959, The function and method of electoral
geography, Annals of the Association of American
Geographers, Vol.49, pp.296-304.
Ramphal S.S., 1985, A world turned upside down,
Geography, Vol.70, pp.193-205.
Prescott J.R.V., 1969, Electoral studies in political
geography, R.E. Kasperson and J.V.Minghi (eds) The
Structure of Political Geography, University of London
Press.
Raper J.F., 1989, The 3-dimensional geoscientific mapping
and modelling system: a conceptual design, J. Raper (ed),
GIS: Three Dimensional Applications in Geographic
Information Systems, Taylor & Francis, London.
Prigogine I. & Stengers I., 1984, Order out of chaos: man's
new dialogue with nature, Heineman, London.
Rase W.D., 1980, A family of subroutines for plotting
graduated symbol maps, Geo-Processing, Vol.1, pp.231242.
Provin R.W., 1977, The perception of numerousness on dot
maps, The American Cartographer, Vol.4, No.2, pp.111125.
Ratajski L. & Siwek J., 1977, Regular density network, The
International Yearbook of Cartography, Vol.17, pp.138154.
Prueitt M.L., 1987, A window on science, IEEE Computer
Graphics and Applications, Vol.7, No.9, pp.4-8.
Raubertas R.F., 1988, Spatial and temporal analysis of
disease occurrence for detection of clustering, Biometrics,
Vol.44, pp.1121-1129.
Pugh M., 1988, The evolution of the British electoral system
1832-1987, The Historical Association, London.
Puu T., 1981, Structural stability and change in
geographical space, Environment and Planning, Vol.13,
pp.979-989.
Rahu M., 1989, Graphical representation of cancer
incidence data: chernoff faces, International Journal of
Epidemiology, Vol.18, No.4, pp.763-767.
Raisz E., 1934, The rectangular statistical cartogram, The
Geographical Review, Vol.24, pp.292-296.
Raisz E., 1936, Rectangular statistical cartogram of the
world, The Journal of Geography, Vol.35, pp.8-10.
Raisz E., 1944, Atlas of global geography, Global Press
Corporation, New York.
Raisz E., 1962, Principles of cartography, McGraw-Hill,
New york.
Ravindran S. & Manohar M., 1987, Algorithm for
converting a forest of quadtrees to a binary array, Image
and Vision Computing, Vol.5, No.4, pp.297-300.
Redford A., 1976, Labour migration in England, 18001850, Manchester University Press, Manchester.
Rees P., 1986, A geographical forecast of the demand for
student places, Trans. Inst. of British Geogr. N.S., Vol.11,
pp.5-26.
Rees P. & Stillwell J., 1984, A framework for modelling
population change and migration in the U.K., A.J. Boyce
(ed), Migration and Mobility: Biosocial aspects of human
movement, Taylor & Francis, London.
Rees P. & Stillwell J., 1987, Internal migration in the
United Kingdom, Working Paper 497, School of
Geography, University of Leeds.
Bibliography
Rees P.H., 1977, The measurement of migration, from
census data and other sources, Environment and Planning
A., Vol.9, pp.247-272.
Registrar General, 14th Annual Report, 1977, 126 years
ago, Population Trends, Vol.9, pp.27-28.
Reichman S., 1970, Transport mapping in Israel,
Proceedings of the International Conference on
Transportation Maps, pp.190-200, Budapest.
Remus W., 1987, A study of graphical and tabular displays
and their interaction with environmental complexity,
Management Science, Vol.33, No.9, pp.1200-1204.
Reynolds D.R., 1990, Whither electoral geography? a
critique, Developments in Electoral Geography, (eds) R.J.
Johnston, F.M. Shelley & P.J. Taylor , Routledge, London.
Rhind D., 1975a, The reform of areal units, Area, Vol.7,
No.1, pp.1-3.
Rhind D., 1975b, Geographical analysis and mapping of the
1971 UK census data, Census Research Unit Working
paper No.3, University of Durham.
Rhind D., 1975c, Mapping the 1971 census by computer,
Population Trends, Vol.2, pp.9-12.
Rhind D., 1979, Cartography: topological best-seller,
Geographical Magazine, Vol.51, No.5, p.328.
Rhind D., 1983a, Creating new variables and new areas
from the census data, D. Rhind (ed) A Census User's
Handbook, Methuen, London.
Rhind D., 1983b, Mapping census data, D. Rhind (ed) A
Census User's Handbook, Methuen, London.
Rhind D., 1988, Personality as a factor in the development
of a discipline: the example of computer-assisted
cartography, The American Cartographer, Vol.15, No.3,
pp.277-289.
Rhind D., 1991, Data access, charging and copyright and
their implications for GIS, EGIS '91 Proceedings, pp.929945, Brussels, Belgium.
Rhind D. & Tannenbaum E., 1983, Linking census and
other data, D. Rhind (ed) A Census User's Handbook,
Methuen, London.
Rhind D., Evans I.S. & Dewdney J.C., 1977, The
derivation of new variables from population census data,
Census Research Unit, Dept. of Geography, Working Paper
no. 9, Durham University.
Rhind D., Mounsey H. & Shepherd J., 1984, Automated
mapping, Proceedings of the Census of Population and
Associated Social Statistics Conference, November 22,
Sheffield.
Riggleman J.R., 1936, Graphical methods for presenting
business statistics, McGraw-Hill, New York.
Ripley B.D., 1987, An introduction to statistical pattern
recognition, B. Phelps (ed) Interactions in Artificial
Intelligence and Statistical Methods, Gower Technical
Press, Aldershot, pp.176-187.
Ristic J.R., 1988, Recent Literature, The American
Cartographer, Vol.15, No.2, pp.229-234.
220
Robbins W.E. & Fisher S.S., 1989, Three-dimensional
visualization and display technologies, SPIE Proceedings:
The International Society for Optical Engineering , 18-20
January, Los Angeles, California.
Robert S. & Randolph W.G., 1983, Beyond
decentralization: the evolution of population distribution in
England and Wales, 1961-81, Geoforum, Vol.14, No.1,
pp.75-102.
Robert S.D., 1989, The relationship of mental rotation to
the effectiveness of three-dimensional computer models and
planar projections for visualizing topography, PhD
No.8921422, University of Pittsburgh.
Roberts M.C. & Rumage K.W., 1965, The spatial
variations in urban left-wing voting in England and Wales
in 1951, Annals of the Association of American
Geographers, Vol.55, pp.161-178.
Robertson P.K., 1987, Fast perspective views of images
using one-dimensional operations, IEEE Computer
Graphics and Applications, Vol.7, No.2, pp.47-56.
Robertson P.K., 1988, Visualizing color gamuts: a user
interface for the effective use of perceptual color spaces in
data displays, IEEE Computer Graphics and Applications,
Vol.8, No.9, pp.50-64.
Robertson P.K., 1989a, A methodology for scientific data
visualisation: choosing representations based on a natural
scene paradigm, Information Technology Technical
Report, Centre for Spatial Information Systems, CSIRO,
Australia.
Robertson P.K., 1989b, Spatial transformations for rapid
scan-line shadowing, IEEE Computer Graphics and
Applications, Vol.9, No.2, pp.30-38.
Robertson P.K., 1990, A methodology for scientific data
visualization: choosing representations based on a natural
scene paradigm, Visualization '90, Proceedings of the First
IEEE Conference on Visualization, pp.114-123, October,
San Francisco, California.
Robertson P.K. & O'Callaghan J.F., 1985, The
application of scene synthesis techniques to the display of
multidimensional image data, ACM Transactions on
graphics, Vol.4, No.4, pp.247-275.
Robertson P.K. & O'Callaghan J.F., 1986, The generation
of color sequences for univariate and bivariate mapping,
IEEE Computer Graphics and Applications, Vol.6, No.2,
pp.24-32.
Robinson A.H., 1959, The curve of the grey spectrum: a
review, Annals of the Association of American
Geographers, Vol.49.
Robinson A.H., 1974, A new map projection: its
development and characteristics, The International
Yearbook of Cartography, Vol.14, pp.145-155.
Robinson A.H., 1975, Map design, Auto-carto 2
(Proceedings of the International Symposium on ComputerAssisted Cartography), pp.9-14.
Robinson A.H., 1977, Research in cartographic design, The
American Cartographer, Vol.4, No.2, pp.163-169.
221
Robinson A.H., 1982, Early thematic mapping in the history
of cartography, University of Chicago Press, Chicago.
Robinson A.H., 1985, Arno Peters and his new
cartography, The American Cartographer, Vol.12, No.2,
pp.103-111.
Robinson A.H., 1989, Cartography as an art, D.W. Rhind
& D.R.F. Taylor (eds) Cartography Past, Present and
Future, pp.91-102.
Robinson A.H. & Petchenik B.B., 1975, The map as a
communication system, The Cartographic Journal, Vol.12,
No.1, pp.7-15.
Robinson A.H. & Petchenik B.B., 1976, The nature of
maps, THe University of Chicago, .
Robinson A.H., Sale, Morrison & Muehrcke, 1984, The
elements of cartography, John Wiley and Sons, .
Bibliography
Rosenblum L.J., 1990b, Visualization of experimental data
at the naval research laboratory, G.M. Nielson, B. Shriver
& L.J. Rosenblum (eds) Visualization in Scientific
Computing, pp.245-253, IEEE Computer Society Press,
California.
Rosenfeld A., 1988, Survey: image analysis and computer
vision: 1987, Computer Vision, Graphics, and Image
Processing, Vol.42, pp.234-293.
Rosenfeld A., 1989, Survey: image analysis and computer
vision: 1988, Computer Vision, Graphics, and Image
Processing, Vol.46, pp.196-264.
Ross M.D., Cutler L., Meyer G., Lam T. & Vaziri P.,
1990, 3-D components of a biological neural network
visualized in computer generated imagery: 1. macular
receptive field organization, Acta Oto-Laryngologica,
Vol.109, Nos.1-2, pp.83-92.
Rockwood A., 1990, Accurate display of tensor product
isosurfaces, Visualization '90, Proceedings of the First
IEEE Conference on Visualization, pp.353-360, October,
San Francisco, California.
Ross M.D., Meyer G., Lam T., Cutler L. & Vaziri P.,
1990, 3-D components of a biological neural network
visualized in computer generated imagery: 2. macular
neural network organization, Acta Oto-Laryngologica,
Vol.109, Nos.3-4, pp.235-244.
Rodrigue M. & Thompson L., 1982, Computer graphics at
the united states military academy, AutoCarto 5
(Proceedings of the 5th International Symposium on
Computer-Assisted Cartography), pp.629-638.
Roubal J. & Poiker T.K., 1985, Automated contour
labelling and the contour tree, AutoCarto 7 (Proceedings of
the 7th International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.472-481.
Rodrigue M. & Thompson L., 1983, The availability and
use of digital topographic data, AutoCarto 6 (Proceedings
of the 6th International Symposium on Computer-Assisted
Cartography), Vol.2, pp.580-587.
Rouhani S., 1986, Comparative study of ground-water
mapping techniques, Ground Water, Vol.24, No.2, pp.207216.
van Roessel J.W., 1988, Conversion of cartesian
coordinates from and to generalized balanced ternary
addresses, Photogrammetric Engineering and Remote
Sensing, Vol.54, No.11, pp.1565-1570.
van Roessel J.W., Langley P.G. & Smith T.D., 1978,
Timber-Pak - a second generation forest management
information system, Integrated Inventories of Renewable
Natural Resources (Proceedings of Workshop), Station
General Tech. Report RM-55.
Rogers A., 1990, Requiem for the net migration,
Geographical Analysis, Vol.22, pp.283-300.
Rogers J.E. & Groop R.E., 1981, Regional portrayal with
multi-pattern color dot maps, Cartographica, Vol.18, No.4,
pp.51-64.
Rogerson R.J., Findlay A.M., Morris A.S. & Coombes
M.G., 1989, Indicators of quality of life: some
methodological issues, Environment and Planning, Vol.21,
pp.1655-1666.
Root-Bernstein R.S., 1987, Visual thinking: the art of
imaging reality, Transactions of the American
Philosophical Society, Vol.75, pp.50-67.
Rosenblum L.J., 1989, Visualizing oceanographic data,
IEEE Computer Graphics and Applications, Vol.9, No.3,
pp.14-19.
Rosenblum L.J., 1990a, Scientific visualization at research
laboratories, G.M. Nielson, B. Shriver & L.J. Rosenblum
(eds) Visualization in Scientific Computing, pp.209-211,
IEEE Computer Society Press, California.
Rowles R.A., 1978, Perception of perspective block
diagrams, The American Cartographer, Vol.5, No.1, pp.3144.
Rowley G., 1965, The Greater London council elections of
1964: some geographical considerations, Tijdschrift Voor
Economische en Sociale Geografie, Vol.56, pp.113-114.
Rowley G., 1970, Elections and population changes, Area,
Vol.3, pp.13-18.
Rowley G., 1971, The Greater London Council elections of
1964 and 1967: a study in electoral geography,
Transactions of the Institute of British Geographers, Vol.53,
pp.117-131.
Rowley G., 1973, Landslide by cartogram, The
Geographical Magazine, Vol.45, p.344.
Rowley T.W., 1986, Terrain modelling for flight simulation,
British Computer Society Conference on the State of the
Art in Sterio and Terrain Modelling, London, pp.1-7.
van Royan W., 1954, Atlas of the world's resources (volume
1 - the agricultural resources of the world), Prentice-Hall,
New York.
Royston E., 1956, A note on the history of the graphical
presentation of data, Biometrika, Vol.43, Nos.3-4, pp.241247.
Royston E., 1970, A note on the history of the graphical
presentation of data, E.S. Pearson & M.G. Kendall (eds)
Studies in the History of Statistics and Probability, Charles
Griffin & Co. Ltd., London, pp.173-181.
Bibliography
Rucker R., 1984, The fourth dimension: towards a geometry
of higher reality, Houghton Mifflin Co., Boston.
Ruijtenberg P.A., Buchanan R. & Marke P., 1990, Threedimensional data improve reservoir mapping, Journal of
Petroleum Technology, Vol.42, No.1, pp.22-25,59-61.
Rylander G.W., 1986, Areal data reaggregation: a
comparison of two methods, MA., Michigan State
University.
Saalfeld A.J., 1985a, Lattice structures in geography,
AutoCarto 7 (Proceedings of the 7th International
Symposium on Computer-Assisted Cartography),
Washington D.C., pp.482-489.
Saalfeld A.J., 1985b, Topics in advanced topology for
cartography, AutoCarto 7 (Proceedings of the 7th
International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.490.
Sabella P., 1988, A rendering algorithm for visualizing 3D
scalar fields, Computer Graphics, Vol.22, No.4, pp.51-58.
Sabella P. & Carlbom I., 1989, An object-oriented
approach to the solid modeling of empirical data, IEEE
Computer Graphics and Applications, Vol.9, No.5, pp.2435.
Salichtchev K.A., 1983, Cartographic communication: a
theoretical survey, D.R.F. Taylor (ed) Progress in
Contemporary Cartography Volume II: Graphic
Communication and Design in Contemporary Cartography,
pp.11-35.
Salichtchev K.A., 1987, On restructuring thematic
cartography: trends and problems, Mapping Sciences and
Remote Sensing, Vol.24, No.4, pp.285-291.
Salichtchev K.A., 1988, Problems and achievements of
Soviet thematic cartography , Mapping Sciences and
Remote Sensing, Vol.25, No.3, pp.179-187.
Salt J., 1990, Organisational labour migration: theory and
practise in the United Kingdom, J.H. Johnson and J. Salt
(eds), Labour Migration: the Internal Geographical
Mobility of Labour in the Developed World , David Fulton
Publishers, London.
Salzman D. & Neumann J. von, 1987, Visualization in
scientific computing: summary of an NSF-sponsored panel
report on graphics, image processing, and workstations,
International Journal of Supercomputer Applications, Vol.1,
No.4, pp.106-108.
Samet H., 1984, The quadtree and related hierarchical data
structures, A.C.M. Computing Surveys, Vol.16, No.2,
pp.187-260.
Samet H., 1989, Neighbor finding in images represented by
octrees, Computer Vision, Graphics, and Image Processing,
Vol.46, pp.367-386.
Samet H., 1990, The design and analysis of spatial data
structures, Addison-Wesley , USA.
Samet H. & Webber R.E., 1988a, Hierarchical data
structures and algorithms for computer graphics - part I:
fundamentals, IEEE Computer Graphics and Applications,
Vol.8, No.5, pp.48-68.
222
Samet H. & Webber R.E., 1988b, Hierarchical data
structures and algorithms for computer graphics - part II:
applications, IEEE Computer Graphics and Applications,
Vol.8, No.7, pp.59-75.
Samet H., Rosenfeld A. & Shaffer C.A., 1984, Use of
hierarchical data structures in geographical information
systems, Proceedings of the International Symposium on
Spatial Data Handling, pp.392-411, August, Zurich,
Switzerland.
Sanders D., 1991, Voting behaviour in Britain,
Contemporary Record, Vol.4, No.3, pp.2-6.
Sanderson J., 1987, Defining functional occupational
groupings, Environment and Planning A., Vol.19, pp.11991220. Pion, G.B. .
Sandford H.A., 1986, Son of Peters', The Cartographic
Journal, Vol.23, p.69.
Sanguineti M.B., 1988, Interactive graphics and surface
modelling by computer, Technical papers of the ACSMASPRS Annual Convention, St. Louis, Missouri, Vol.2,
pp.300-308.
Sapita R.F., 1987, Process control using color displays,
H.J. Durrett (ed) Color and the Computer, Ch.6, pp.115138.
Saunders P., 1989, Beyond housing classes: the
sociological significance of private property rights in
means of comsumption, L. McDowell, P.Sarre and C.
Hamnett (eds), Divided Nation: Social and Cultural Change
in Britain, Hodder and Stoughton & The Open University,
London.
Savage M., 1989, Spatial differences in modern Britain, C.
Hamnett, L. McDowe, and P. Sarre (eds), Restructuring
Britain: The Changing Social Structure, Sage publications,
The Open University, London.
Scarlatos L. & Pavlidis T., 1990, Hierarchical
triangulation using terrain features, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.168-175, October, San Francisco, California.
Schachter B., 1978, A nonlinear mapping algorithm for
large data sets, Computer Graphics and Image Processing,
Vol.8, No.2, pp.271-276.
Schmandt C., 1987, Color text display in video media, H.J.
Durrett (ed) Color and the Computer, Ch.14, pp.255-266.
Schmid C.F., 1928, Notes on two multiple-variable spot
maps, Social Forces, Vol.6, No.3, pp.378-382.
Schmid C.F., 1983, Statistical graphics: design principles
and practices, John Wiley and Sons, New York.
Schnell G.A. & Monmonier M.S., 1983, The study of
population, Charles E. Merrill Publishing Co., p.64,42 &
138.
Schoyer G., 1979, The coverage of political patterns and
elections in some selected state atlases of the United States,
Bulletin, Geography and Map Division, Special Libraries
Association, Vol.117, pp.2-7.
Schulman J., 1987, The statistical analysis of density
equalized map projections, PhD, University of California,
Berkeley.
223
Schulman J., Selvin S. & Merrill D.W., 1988, Density
equalized map projections: a method for analysing
clustering around a fixed point, Statistics in Medicine,
Vol.7, pp.491-505.
Schulmeister Th., Tschapek A. & Zinke M., 1986,
Visualization of the entire surface of a protein by
cartographic projection, Computer Applications in the
Biosciences, Vol.2, No.4, pp.265-268.
Schultz G.M., 1961, Using dots for traffic flow maps, The
Professional Geographer, Vol.13, No. 1.
Schwartz E.L., Merker B., Wolfson E. & Shaw A., 1988,
Applications of computer graphics and image processing to
2D and 3D modeling of the functional architecture of visual
cortex, IEEE Computer Graphics and Applications, Vol.8,
No.7, pp.13-23.
Scott J., 1989, The corporation and the class structure, L.
McDowell, P.Sarre and C. Hamnett (eds), Divided Nation:
Social and Cultural Change in Britain, Hodder and
Stoughton & The Open University, London.
Seager M.K., Werner N.E., Zosel M.E. & Strout R.E.,
1990, Graphical analysis of multi/micro-tasking on Cray
multiprocessor, G.M. Nielson, B. Shriver & L.J.
Rosenblum (eds) Visualization in Scientific Computing,
pp.160-173, IEEE Computer Society Press, California.
SEEDS, 1987, The south-south divide, The SEEDS
Association, Harlow, Britain.
Segi M., 1977, Graphic presentation of cancer incidence by
site and by area and population, Segi Institute of Cancer
Epidemiology, Yomeicho 2-5, Mizuho-ku, Nagoya, 467,
Japan.
Selvin S., Merrill D., Sacks S., Wong L., Bedell L. &
Schulman J., 1984, Transformations of maps to investigate
clusters of disease, Lawrence Berkeley Lab Report, LBL18550.
Selvin S., Merrill D.W., Schulman J., Sacks S., Bedell L.
& Wong L., 1988, Transformations of maps to investigate
clusters of disease, Social Science in Medicine, Vol.26,
No.2, pp.215-221.
Selvin S., Shaw G., Schulman J. & Merrill D.W., 1987,
Spatial distribution of disease: three case studies, J.N.C.I.,
Vol.79, No.3, pp.417-423.
Sen A.K., 1975, A theorem related to cartograms, American
Mathematics Monthly, Vol.82, pp.382-385.
Sen A.K., 1976, On a class of map transformations,
Geographical Analysis, Vol.8, pp.23-37.
Sen P.K., 1960, A new method of mapping three variables,
Geographical Review of India, Vol.22, No.2, pp.15-28.
Shea K.S. & McMaster R.B., 1989, Cartographic
generalization in a digital environment: when and how to
generalize, Auto Carto 9, pp.56-67, Baltimore, Maryland.
Shelberg M.C., Moellering H. & Lam N., 1982,
Measuring the fractal dimensions of empirical cartographic
curves, AutoCarto 5 (Proceedings of the 5th International
Symposium on Computer-Assisted Cartography), pp.481490.
Bibliography
Shepherd J., Westaway J. & Lee T., 1974, A social atlas
of London, Clarendon Press, Oxford.
Sherman J.C., 1975, The challenge of maps for the visually
handicapped, Auto-carto 2 (Proceedings of the
International Symposium on Computer-Assisted
Cartography), pp.91-98.
Shrivastava V.K., 1970, Some aspects of the means of
representation of lower order transport characteristics in
Baghelkhand - Madhya Pradesh - India: a case study,
Proceedings of the International Conference on
Transportation Maps, pp.202-209, Budapest.
Sibert J.L., 1980, Continuous-colour choropleth maps,
Geo-Processing, Vol.1, pp.207-217.
Siedlecki W., Siedlecka K. & Sklansky J., 1988a, An
overview of mapping techniques for exploratory pattern
analysis, Pattern Recognition, Vol.21, No.5, pp.411-429.
Siedlecki W., Siedlecka K. & Sklansky J., 1988b,
Experiments on mapping techniques for exploratory pattern
analysis, Pattern Recognition, Vol.21, No.5, pp.431-438.
Sijmons K., 1978, Hexagonal grid cells for the analysis of
mapped phenomena, ITC Journal, Vol.2, pp.244-262.
Silverstein L.D., 1987, Human factors for color display
systems: concepts, methods, and research, H.J. Durrett (ed)
Color and the Computer, Ch.2, pp.27-62.
Simkin D.K., 1987, Towards an information processing
view of graph comprehension, PhD No.DA8729042,
Northwestern University.
Simkin D.K. & Hastie R., 1987, An information-processing
analysis of graph perception, Journal of the American
Statistical Association, Vol.82, No.398, pp.454-465.
Simpson I.A. & Camm A.J., 1990, Colour doppler flow
mapping, British Medical Journal, Vol.300, No.6716, p.1.
Sinclair D.J. (ed), 1970, Faber Atlas (5th edition), Geo,
Oxford.
Skoda L. & Robertson J.C., 1972, Isodemographic map of
Canada, Geographical Paper No.50, Department of the
Environment, Ottawa, Canada.
Slocum T.A., 1981, Analyzing the communicative efficiency
of two-sectored pie graphs, Cartographica, Vol.18, No.3,
pp.53-65.
Slocum T.A., 1982, Circle size judgment and map design: a
comment, The American Cartographer, Vol.9, No.2,
pp.179-181.
Slocum T.A., 1983, Predicting visual clusters on graduated
circle maps, The American Cartographer, Vol.10, No.1,
pp.59-72.
Slocum T.A., 1984, A cluster analysis model for predicting
visual clusters, The Cartographic Journal, Vol.21, pp.103111.
Slocum T.A., 1985, Principles of thematic map design
(review), The American Cartographer, Vol.12, No.2,
pp.177-178.
Bibliography
Slocum T.A. & Gilmartin P.P., 1979, Cartographic
analysis of proportional circle maps using delaunay
triangles, Canadian Cartographer, Vol.16, No.2, pp.133144.
Slocum T.A. & McMaster R.B., 1986, Gray tone versus
line plotter area symbols: a matching experiment, The
American Cartographer, Vol.13, No.2, pp.151-164.
Slomka M., 1987, Graphical aids in the analysis of
demographic data and drug-demographic variable
interaction, Controlled Clinical Trials, Vol.8, No.3, p.307.
Smith A., 1991, Conservatives braced for another bad news
by-election, Financial Times, 16th May.
Smith D.H., 1988, Some consequences of a three-party split
in a UK general election, Journal of the Operational
Research Society, Vol.39, No.1, p.111.
Smith D.R. & Paradis A.R., 1989a, Three-dimensional GIS
for the earth sciences, Auto Carto 9, pp.324-335,
Baltimore, Maryland.
Smith D.R. & Paradis A.R., 1989b, Three-dimensional GIS
for the earth sciences, J. Raper (ed), GIS: Three
Dimensional Applications in Geographic Information
Systems, Taylor & Francis, London.
Smith R.M., 1977, The development of black and white twovariable maps: ongoing cartographic research, H.A.
Winters & M.K. Winters (eds) Applications of Geographic
Research, Department of Geography, Michigan State
University, pp.145-154.
224
Sorm F. (ed), 1966, Atlas Ceskoslovenske Socialisticke
Republiky, Czechoslovakian National Atlas,
Czechoslovakia.
Southard R.B., 1987, Automation in cartography revolution or evolution?, The Cartographic Journal, Vol.24,
No.1, pp.59-63.
Spence N.A. & Frost M.E., 1983, Urban employment
change, J.B. Goddard & A.G. Champion (eds) The Urban
and Regional Transformation of Britain, Methuen, London.
Spence N.A., Gillespie A., Goddard J., Kennett S., Pinch
S. & Williams A., 1982, British cities an analysis of urban
change , Pergamon Press, Oxford.
Spicer A., 1989, Controversial cartography, The
Geographical Magazine, Vol.61, No.9, pp.42-44.
Spielman H.A., 1987, Color and business graphics, H.J.
Durrett (ed) Color and the Computer, Ch.15, pp.267-284.
Sprunt B.F., 1975, Relief representation in automated
cartography: an algorithmic approach, J.C. Davies & M.J.
McCullagh (eds) Display and Analysis of Spatial Data,
pp.173-186, John Wiley & Sons, London.
Starkweather G.K., 1986a, A color-correction scheme for
color electronic printers, Color Research and Application
(Supplement), Vol.11 pp.67-72.
Starkweather G.K., 1986b, Electronic color-printer
technologies, Color Research and Application
(Supplement), Vol.11 pp.73-74.
Smith R.M., 1987, Influence of texture on perception of
gray tone map symbols, The American Cartographer,
Vol.14, No.1, pp.43-47.
Statistics Canada, 1977, Perspective Canada II: A
compendium of social statistics 1977, Ministry of Industry,
Trade and Commerce, Canada.
Smith W., 1987, Ergonomic vision, H.J. Durrett (ed) Color
and the Computer, Ch.5, pp.101-114.
Staudhammer J., 1975, Multi-dimensional function display
using color scale, Siggraph, Vol. 9, part 1, pp.181-183.
Smith W. (ed), 1874, An atlas of ancient geography, John
Murray, London.
Staudhammer J., 1987, Supercomputers and graphics,
IEEE Computer Graphics and Applications, Vol.7, No.7,
pp.24-25.
Smyrnew J.M., 1983, Accuracy of viewing three
dimensional bars in perspective: the case of computer
generated pillars as a thematic map symbolism type,
AutoCarto 6 (Proceedings of the 6th International
Symposium on Computer-Assisted Cartography), Vol.2,
pp.443.
Snyder J.P., 1977, A comparison of pseudocylindrical map
projections, The American Cartographer, Vol.4, No.1,
pp.59-81.
Staudhammer J., 1991a, Computer graphics - toward the
next millennium, IEEE Computer Graphics and
Applications, Vol.11, No.1, pp.21-22.
Staudhammer J., 1991b, Computer graphics hardware,
IEEE Computer Graphics and Applications, Vol.11, No.1,
pp.42-44.
Snyder J.P., 1981, The perspective map projection of the
earth, The American Cartographer, Vol.8, No.2, pp.149160.
Steinke T.R., 1975, The optimal thematic map reading
procedure: some clues provided by eye movement
recordings, Auto-carto 2 (Proceedings of the International
Symposium on Computer-Assisted Cartography), pp.214223.
Snyder J.P., 1984, Map-projection graphics from a
personal computer, The American Cartographer, Vol.11,
No.2, pp.132-138.
Steinke T.R., 1981, Cognitive integration of objective
choropleth map attribute information, Cartographica,
Vol.18, No.1, pp.13-23.
Snyder J.P., 1987, "Magnifying-Glass" azimuthal map
projections, The American Cartographer, Vol.14, No.1,
pp.61-68.
Steinke T.R., 1990, Three dimensional applications in
geographic information systems (review), Cartography and
Geographic Information Systems, Vol.17, No.2, pp.177178.
Snyder J.P., 1988, New equal-area projections for
noncircular regions, The American Cartographer, Vol.15,
No.4, pp.341-355.
Stephen E.H., 1982, 1980 census geography, American
Demographics, Vol.4, No.1, pp.30-35.
225
Stepney S., 1988, Life, Acorn User, November pp.80-83.
Stewart J.Q., 1950, The development of social physics,
American Journal of Physics, Vol.18, No.5, pp.239-253.
Stewart J.Q. & Warntz W., 1958, Physics of population
distribution, Journal of Regional Science, Vol. 1, pp.99123.
Stillwell J., Boden P. & Rees P., 1988, Internal migration
change in the UK: trends based on NHSCR movement data,
1975-76 to 1985-86, Working paper 510, School of
Geography, University of Leeds.
Stillwell J., Boden P. & Rees P., 1990, Trends in internal
net migration in the UK: 1975 to 1986, Area, Vol.22.1,
pp.57-65.
Stillwell J.C., 1986, The analysis and projection of
interregional migration in the UK, R. Woods & P.H. Rees
(eds), Population Structures and Models, pp.160-202, Allen
& Unwin, London.
Stone M.C., 1986, Color, graphic design, and computer
systems, Color Research and Application (Supplement),
Vol.11 pp.75-82.
Stooke P.J., 1985, Cartography of non-spherical worlds,
PhD ISBN 0-315-46485-2, University of Victoria.
Storper M. & Walker R., 1983, The theory of labour and
the theory of location, International Journal of Urban and
Regional Research, Vol.7, pp.1-43.
Storrie M.C., 1968, Household tenure in the British Isles,
Institute of British Geographers, Transactions no.43, April
1968, George Philip & Son, London.
Storrie M.C. & Jackson C.I., 1967, A comparison of some
methods of mapping census data of the British Isles, The
Cartographic Journal, Vol.4, No.1, pp.38-43.
Stray S. & Upton G., 1989, Triangles and triads, Journal of
Operational Research Society , Vol.40, No.1, pp.83-92.
Stuetzle W., 1987, Plot windows, Journal of the American
Statistical Association, Vol.82, No.398, pp.466-475.
Stuetzle W., 1988, Plot windows, W.S. Cleveland & M.E.
McGill (eds) Dynamic Graphics For Statistics,
Wandsworth, California.
Stutz F.P., 1975, Interactive computer cartography, The
Cartographic Journal, Vol.12, No.1, pp.17-21.
Subramian K.R. & Fussell D.S., 1990, Applying space
subdivision techniques to volume rendering, Visualization
'90, Proceedings of the First IEEE Conference on
Visualization, pp.150-159, October, San Francisco,
California.
Sukenik I.Y., 1988, The great october atlas (review),
Mapping Sciences and Remote Sensing, Vol.25, No.4,
pp.341-346.
Sullivan W.T., 1989, A 10km resolution image of the entire
night-time Earth based on cloud-free satellite photographs
in the 400-1100nm band, International Journal of Remote
Sensing, Vol.10, No.1, pp.1-5.
Bibliography
Sutherland I.N., 1962, Representations of national,
regional, and local statistics, The British Journal
Preventative and Social Medicine, Vol.16, pp.30-39.
Sutner K., 1989, Linear cellular automata and the gardenof-eden, The Mathematical Intelligencer, Vol.11, No.2,
pp.49-53.
Suvorov A.K., 1987, New forms of cartographic models and
their importance for scientific and practical activity,
Mapping Sciences and Remote Sensing, Vol.24, No.3,
pp.257-269.
Swaddle K. & Heath A., 1989, Official and reported
turnout in the British general election of 1987, British
Journal of Political Science, Vol.19, pp.537-551.
Sweeney C.J. & Simson J.A., 1967, The Ordnance Survey
and land registration, The Geography Journal, Vol.133,
pp.10-23.
Symm G.T., 1966, An integral equation method in
conformal mapping, Numerische Mathematik, Vol.9,
pp.250-258.
Szegö J., 1984, A census atlas of Sweden, Statistics Sweden,
Central Board of Real Estate Data, Swedish Council for
Building Research and The University of Lund, Stockholm.
Szegö J., 1987, Human cartography: mapping the world of
man, Swedish Council for Building Research, Stockholm.
Szollosi-Nagy A., 1987, Input detection by the discrete
linear cascade model, Journal of Hydrology, Vol.89,
pp.353-370.
Tamsma R., 1988, The Netherlands in fifty maps: an
annotated atlas, Tijdschrift voor Economische en Sociale
Geografie, Special Issue.
Tannenbaum P.M., 1986, The colorimetry of color
displays: 1950 to the Present, Color Research and
Application (Supplement), Vol.11 pp.27-28.
Tatsumi H., Takaoki E., Omura K. & Fujita H., 1990, A
new method for three-dimensional reconstruction from
serial sections by computer graphics using "meta-balls":
reconstruction of "hepatoskeletal system" formed by ito
cells in the cod liver, Computers and Biomedical Research,
Vol.23, No.1, pp.37-45.
Taylor D.R.F., 1977, Graphic perceptions of language in
Ottawa-Hull, Canadian Cartographer, Vol.14, No.1, pp.2434.
Taylor D.R.F., 1980, Computer-assisted cartography and
rural development / a case study from Kenya,
Cartographica, Vol.17, No.1, pp.72-87.
Taylor D.R.F., 1983, Some conclusions and new challenges,
D.R.F. Taylor (ed) Progress in Contemporary Cartography
Volume II: Graphic Communication and Design in
Contemporary Cartography, pp.285-307.
Taylor D.R.F., 1985, The educational challenges of a new
cartography, Cartographica, Vol.22, No.4, pp.19-37.
Taylor D.R.F., 1987a, The art and science of cartography:
the development of cartography and cartography for
development, The Canadian Surveyor, Vol.14, No.3,
pp.359-372.
Bibliography
226
Taylor D.R.F., 1987b, New methods and technologies in
cartography: the digital world atlas, The International
Yearbook of Cartography, Vol.27, pp.219-224.
Tikunov V.S., 1986, Thematic contents of maps: some
theoretical approaches in the development of modelling
methods, The Cartographic Journal, Vol.23, pp.56-65.
Taylor D.R.F., 1988, Cartography past, present and future,
New Zealand Cartographic Journal, Vol.18, No.2, pp.9-20.
Tikunov V.S., 1987, Some debatable issues in cartography,
Mapping Sciences and Remote Sensing, Vol.24, No.4,
pp.313-319.
Taylor I., 1955, An epidemiological map, The Monthly
Bulletin of the Ministry of Health, Vol.14, pp.200-201.
Taylor P.J., 1977, Quantitative methods in geography,
Houghton Mifflin Co., Boston, pp.150-156.
Tikunov V.S., 1988, Anamorphated cartographic images:
historical outline and construction techniques,
Cartography, Vol.17, No.1, pp.1-8.
Taylor P.J., 1978, Political geography, Progress in Human
Geography, Vol.2, pp.153-162.
Tikunov V.S. & Yudin S.A., 1987, Use of transformed
images in urban construction studies, Mapping Sciences
and Remote Sensing, Vol.24, No.3, pp.202-208.
Taylor P.J., 1979, The changing geography of
representation in Britain, Area, Vol.11, No.4, pp.294-297.
Tobler W.R., 1959, Automation and cartography, The
Geographical Review, Vol.49, pp.526-536.
Taylor P.J., 1982, The changing political map, R.J.
Johnston and J.C. Doornkamp (eds), The Changing
Geography of the United Kingdom , Methuen and co.,
London.
Tobler W.R., 1961, Map transformations of geographic
space, PhD, Department of Geography, University of
Washington.
Taylor P.J., 1990, Editorial comment GKS, Political
Geography Quarterly, Vol.9, No.3, pp.211-212.
Tobler W.R., 1963a, D'Arcy Thompson and the analysis of
growth and form, Papers of the Michigan Academy of
Science, Arts, and Letters, Vol.48, pp.385-390.
Taylor P.J., 1991, A future for geography, Terra, Vol.103,
No.1, pp.21-31.
Tobler W.R., 1963b, Geographic area and map projections,
The Geographical Review, Vol.53, pp.59-78.
Teeffelen P.B.M., 1991, Analyzing interaction data with
GIS techniques in developing countries: the use of
"flowmap: in Mali and Mexico, EGIS '91 Proceedings,
pp.1058-1066, Brussels, Belgium.
Tobler W.R., 1966, Medieval distortions: the projections of
ancient maps, Annals of the Association of American
Geographers, Vol.56, pp.351-360.
Thakur B. & Thaker R., 1982, Statistical techniques of
population and settlement mapping, Geographical Review
of India, Vol.44, pp.62-77.
Thalmann D. & Magnenat-Thalmann N., 1986, Artificial
intelligence in three-dimensional computer animation,
Computer Graphics Forum, Vol.5, pp.341-348.
Thane P., 1989, Old age: burden or benefit, H. Joshi (ed)
The Changing Population of Britain, Basil Blackwell,
Oxford.
The Economist, 1991, Hidden hope for the Tories, The
Economist, May 11th.
The Institution of Highways and Transportation, 1987,
Roads and traffic in urban areas, The Institution of
Highways and Transportation with the Department of
Transport, London.
Thiemann R., 1989, Visualization of digital terrain data,
Eurographics '89, Hamburg, pp.173-195.
Thomas E.N., 1955, "Balanced" colors for use on the multicolor dot map, The Professional Geographer, Vol.7, No.6,
pp.8-10.
Thompson J.M., 1988, Advanced scientific visualization
productivity with integrated analysis, graphics, and image
processing techniques, Electronic Imaging '88,
International Electronic Imaging Exposition and
Conference, Boston, Massachusetts, pp.1084-1085.
Thornthwaite C.W. & Slentz H.I., 1934, Internal
migration in the United States, University of Pennsylvania
Press, Philadelphia.
Tobler W.R., 1967, Of maps and matrices, Journal of
Regional Science, Vol.7, No.2, pp.275-280.
Tobler W.R., 1968, Transformations, J.D. Nystuen (ed) The
Philosophy of Maps, Discussion Paper No.12, Michigan
Inter-University Community of Mathematical Geographers,
Wayne State University, pp.2-4.
Tobler W.R., 1969a, Geographical filters and their
inverses, Geographical Analysis, Vol.1, pp.234-251.
Tobler W.R., 1969b, Semiologie Graphique: Review,
Journal of the American Statistical Association, Vol.64,
pp.391-392.
Tobler W.R., 1969c, The spectrum of US 40, Papers of the
Regional Science Association, Vol.23, pp.45-52.
Tobler W.R., 1970, A computer movie simulating urban
growth in the Detroit region, Economic Geography,
Vol.46, pp.234-240.
Tobler W.R., 1971, A Cappadocian speculation, Nature,
Vol.231, pp.39-41.
Tobler W.R., 1973a, A continuous transformation useful for
districting, Annals of the New York Academy of Sciences,
Vol. 219, pp.215-220.
Tobler W.R., 1973b, Choropleth maps without class
intervals?, Geographical Analysis, Vol.3, pp.262-265.
Tobler W.R., 1973c, The geometry of mental maps,
Golledge R.G. & Rushton G. (eds) Spatial Choice and
Spatial Behavior, Ohio State University Press, Columbus,
pp.69-81.
227
Tobler W.R., 1975a, Mathematical map models, Auto-carto
2 (Proceedings of the International Symposium on
Computer-Assisted Cartography), pp.66-73.
Tobler W.R., 1975b, Linear operators applied to areal
data, J.C. Davies & M.J. McCullagh (eds) Display and
Analysis of Spatial Data, pp.14-37, John Wiley & Sons,
London.
Tobler W.R., 1976a, Cartograms and cartosplines,
Proceedings of the Workshop on Automated Cartography
and Epidemiology, National Center for Health Statistics,
U.S. Department of Health, pp.53-58.
Tobler W.R., 1976b, Analytical cartography, The American
Cartographer, Vol.3, No.1, pp.21-31.
Tobler W.R., 1977, A transformational view of
cartography, U.A.Uotila (ed) The Changing World of
Geodetic Science, Report No.250, Department of Geodetic
Science, University of Ohio State.
Tobler W.R., 1978, A proposal for an equal area map of the
entire world on Mercator's projection, The American
Cartographer, Vol.5, No.2, pp.149-154.
Tobler W.R., 1979a, Cellular geography, S.Gale &
G.Olsson (eds) Philosophy in Geography, D. Reidel
Publishing Co. Holland, pp.379-386.
Tobler W.R., 1979b, A transformational view of
cartography, The American Cartographer, Vol.6, No.2,
pp.101-106.
Tobler W.R., 1984, Application of image processing
techniques to map processing, Proceedings of the
International Symposium on Spatial Data Handling, pp.140145, August, Zurich, Switzerland.
Tobler W.R., 1985, Interactive construction of continuous
cartograms, AutoCarto 7 (Proceedings of the 7th
International Symposium on Computer-Assisted
Cartography), Washington D.C., pp.525.
Tobler W.R., 1986a, Measuring the similarity of map
projections, The American Cartographer, Vol.13, No.2,
pp.135-139.
Tobler W.R., 1986b, Polycylindric map projections, The
American Cartographer, Vol.13, No.2, pp.117-120.
Tobler W.R., 1986c, Pseudo-cartograms, The American
Cartographer, Vol.13, No.1, pp.43-50.
Tobler W.R., 1987a, Alternatives in movement mapping,
Personal Correspondence, January 1991.
Tobler W.R., 1987b, Experiments in migration mapping By
computer, The American Cartographer, Vol.14, No.2,
pp.155-163.
Tobler W.R., 1989a, Frame independent spatial analysis,
M. Goodchild & S. Gopal (eds), The Accuracy of Spatial
Databases, Taylor and Francis, Philadelphia.
Tobler W.R., 1989b, An update to 'numerical map
generalization', R.B. McMaster (ed), Numerical
Generalization in Cartography, Cartographica, University
of Toronto Press.
Bibliography
Tobler W.R., 1990, GIS transformations, Proceedings GIS/
LIS '90, pp.163-166, Anaheim California.
Tobler W.R. & Chen Z.T., 1986, A quadtree for global
information storage, Geographical Analysis, Vol.18, No.4,
pp.360-371.
Toffoli T. & Margolus N., 1987, Cellular automata
machines, The MIT Press, MA..
Torguson J.S., 1990, Cartogram: a microcomputer
program for the interactive construction of contiguous
value-by-area cartograms, MA, University of Georgia.
Tovey C.A., 1988, Simulated simulated annealing,
American Journal of Mathematical and Management
Sciences, Vol.8, Nos.3 & 4, pp.389-407.
Town and Country Planning Association, 1987, NorthSouth divide: a new deal for Britain's regions, Town and
Country Planning Association, 17 Carlton House Terrace,
London.
Townsend P. with Corrigan P. & Kowarzik U., 1987,
Poverty and labour in London: interim report of a
centenary survey, Survey of Londoners' Living Standards
no.1, Low pay Unit , Low Pay Unit and Poverty Research
Trust, London.
Traylor C.T. & Watkins J.F., 1985, Map symbols for use
in the three dimensional graphic displays of large scale
digital terrain models using microcomputer technology,
AutoCarto 7 (Proceedings of the 7th International
Symposium on Computer-Assisted Cartography),
Washington D.C., pp.526-531.
Truran H.C., 1975, A practical guide to statistical maps
and diagrams, Heinemann, London.
Tschudi M.K., 1988, A technique for viewing a large digital
map background, GIS/LIS'88 proceedings - Accessing the
World, San Antonio - Texas, pp.91-99.
Tsuchiya K., Miyahara T. & Fan L.S., 1989, Visualization
of bubble-wake interactions for a stream of bubbles in a
two-dimensional liquid-solid fluidized bed, International
Journal of Multiphase Flow, Vol.15, No.1, pp.35-49.
Tsukune H. & Aggarwal J.K., 1988, Analyzing
orthographic projection of multiple 3D velocity vector
fields in optical flow, Computer Vision, Graphics, and
Image Processing, Vol.42, pp.157-191.
Tufte E.R., 1983, The visual display of quantitative
information, Graphics Press, Chesire, Connecticut.
Tufte E.R., 1988, Comment on: dynamic graphics for data
analysis (1987); Becker, Cleveland & Wilks , W.S.
Cleveland & M.E. McGill (eds) Dynamic Graphics For
Statistics, Wandsworth, California.
Tufte E.R., 1990, Envisioning Information, Graphics Press,
Chesire, Connecticut.
Tuhkanen T., 1987, Computer-assisted production of hill
shading and hypsometric tints, The International Yearbook
of Cartography, Vol.27, pp.225-232.
Tukey J.W., 1965, The future of processes of data analysis,
Proceedings of the tenth conference on the design of
experiments in army research development and testing,
report 65-3, Durham N.C., U.S. army research office,
pp.691-725.
Bibliography
Tukey J.W., 1976, Statistical mapping- what should not be
plotted, Proceedings of the Workshop on Automated
Cartography and Epidemiology, National Center for Health
Statistics, U.S. Department of Health, pp.18-22.
Tukey J.W., 1979, Methodology, and the statistician's
responsibility for both accuracy and relevance, Journal of
the American Statistical Association, Vol.74, No.368,
pp.786-793.
Tukey J.W., 1988a, Comment on: dynamic graphics for
data analysis (1987); Becker, Cleveland & Wilks , W.S.
Cleveland & M.E. McGill (eds) Dynamic Graphics For
Statistics, Wandsworth, California.
Tukey J.W., 1988b, Control and stash philosophy for twohanded, flexible, and immediate control of a graphic
display, W.S. Cleveland & M.E. McGill (eds) Dynamic
Graphics For Statistics, Wandsworth, California.
Tukey P.A. & Tukey J.W., 1981, Graphical display of data
sets in 3 or more dimensions, Barnett V. (ed) Interpreting
Multivariate Data, Part III, pp.189-275.
Turner A.K., 1989, The role of three-dimensional
geographic information systems in subsurface
characterization for hydrogeological applications, J. Raper
(ed), GIS: Three Dimensional Applications in Geographic
Information Systems, Taylor & Francis, London.
U.S. Bureau of the Census, 1872, Statistics of wealth and
public indebtedness, US Government Printing Office,
Washington, USA.
Uhorczak F., Grzechnik L. & Zelasko R., 1982, Two
isopleth maps of world population density constructed on
equal-size unit areas, Geographica Polonica, Vol.48, pp.93104.
Ulack R., 1987, Demo-graphics: world population and
projections (review), The Professional Geographer, Vol.39,
No.1, pp.85-86.
Ulichney R.A., 1988, Dithering with blue noise,
Proceedings of the IEEE, Vol.76, No.1, pp.56-79.
Underwood J.D.M., 1981, Influencing the perception of
contour lines, The Cartographic Journal, Vol.18, No.2,
pp.116-119.
Unger J.D., Liberty L.M., Phillips J.D. & Wright B.E.,
1989, Creating a 3-dimensional transect of the earth's crust
from craton to ocean basin across the N. Appalachian
Orogen, J. Raper (ed), GIS: Three Dimensional
Applications in Geographic Information Systems, Taylor &
Francis, London.
University of Surrey, 1989, Ninth easter school on colour
in visual displays, The University of Surrey, 5-7 April.
Upson C. & Keeler M., 1988, V-buffer: visible volume
rendering, Computer Graphics, Vol.22, No.4, pp.59-64.
Upson C., Faulhaber T., Kamins D., Laidlaw D., Schlegel
D., Vroom J., Gurwitz R. &. van Dam A, 1989, The
application visualization system: a computational
environment for scientific visualization, IEEE Computer
Graphics and Applications, Vol.9, No.4, pp.30-42.
Upton G.J.G., 1976, Research notes: diagrammatic
representation of three-party contests, Political Studies,
Vol.XXIV, pp.448-454, Clarendon Press.
228
Upton G.J.G., 1991a, The impact of by-elections on general
elections: England, 1950-87, British Journal of Political
Science, Vol.21, pp.108-119.
Upton G.J.G., 1991b, Displaying election results, Political
Geography Quarterly, Vol.10, No.3, pp.200-220.
Urwin D.W., 1982, Territorial structure and political
developments in the United Kingdom, S. Rokkan & D.W.
Urwin (eds) The Politics of Territorial Identity: Studies in
European Regionalism, Chapter 2, Sage Publications,
London.
Utano J.J., 1980, Computer program 2: a program to draw
four types of statistical maps on vector plotter (STATMAP),
Environment and Planning, Vol.12, pp.963-966.
Van Dyke M., 1982, An album of fluid motion, The
Parabolic Press, Stanford, California.
Varanka D.E., 1987, An analysis of contemporary map-like
art, M.A., University of Illinois, Chicago.
Verts W.T., 1989, IBM PC animation - crude but effective,
Auto Carto 9 (International Symposium on ComputerAssisted Cartography), pp.858-866.
Vicsek T. & Szalay A.S., 1987, Fractal distribution of
galaxies modelled by a cellular-automata type stochastic
process, Physical Review Letters, Vol.58, No.26, pp.28182821.
Vidale M.L., 1955, A graphical solution to the
transportation problem, Journal of the Operations Research
Society of America, Vol.4,, pp.193-203.
Visvalingham M., 1975, Storage of the 1971 UK census
data: some technical considerations, Census Research Unit
Working paper No.4, University of Durham.
Visvalingham M., 1981, The signed chi-score measure for
the classification and mapping of polychotomous data, The
Cartographic Journal, Vol.18, No.1, pp.32-43.
Visvalingham M., 1985, Concept and refinement in social
planning through the graphical representation of large
data sets, B.Shackel (ed) Human Computer Interaction Interact (September) '84, London, North Holland,
Amsterdam.
Visvalingham M., 1988, Issues relating to basic spatial
units, Mapping Awareness, Vol.2, No.3, pp.40-42.
Visvalingham M., 1989, Cartography, GIS and maps in
perspective, The Cartographic Journal, Vol.26, pp.26-87.
Vize R., 1990, Computers that bend the rules of animation,
New Scientist, Vol.125, No.1705, p.33.
van Vliet L.J. & Verwer B.J.H., 1988, A contour
processing method for fast binary neighbourhood
operations, Pattern Recognition Letters, Vol.7, No.1,
pp.27-36.
Voegele K., 1990a, Annotated bibliography, G.M. Nielson,
B. Shriver & L.J. Rosenblum (eds) Visualization in
Scientific Computing, pp.5-30, IEEE Computer Society
Press, California.
229
Voegele K., 1990b, Scattered data interpolation tools in a
microcomputer visualization environment, Visualization
'90, Proceedings of the First IEEE Conference on
Visualization, pp.315-322, October, San Francisco,
California.
Vujakovic P., 1988, Arno Peters' cult of the "new
cartography": from concept to world atlas, The Society of
University Cartographers Bulletin, Vol.22, No.2, pp.1-6.
Wacholder E., Han J. & Mann R.C., 1989, A neural
network algorithm for the multiple travelling salesmen
problem, Biological Cybernetics, Vol.61, No.1, pp.11-19.
Wainer H. & Francolini C.M., 1980, An empirical inquiry
concerning human understanding of two-variable color
maps, The American Statistician, Vol.34, No.2, pp.81-93.
Wainer H. & Reiser M., 1976, Assessing the efficacy of
visual displays, American Statistical Association,
Proceedings of the Social Statistics Section, No.1, pp.89-92.
Wainwright R.T., 1971-1973, Lifeline: a quarterly
newsletter for enthusiasts of John Conway's game of life,
R.T. Wainwright, Nos.1-11.
Walker C., 1977, Demographic characteristics of migrants,
1964-75, Population Trends, Vol.8, pp.11-13.
Walker C. & Gee M., 1977, Migration: the impact on the
population, Population Trends, Vol.9, pp.24-26.
Bibliography
Wandell B.A., 1986, Color rendering of color camera data,
Color Research and Application (Supplement), Vol.11
pp.30-33.
Wang C.M. & Gugel H.W., 1988, A high performance
color graphics facility for exploring multivariate data,
W.S. Cleveland & M.E. McGill (eds) Dynamic Graphics
For Statistics, Wandsworth, California.
Wang P.C.C. (ed), 1978, Graphical representation of
multivariate data, Academic Press, New York.
Ward K., 1979, Cartography in the round - The
orthographic projection, The Cartographic Journal, Vol.16,
No.2, pp.104-116.
Ward R., 1989, Minority settlement and the local economy,
L. McDowell, P.Sarre and C. Hamnett (eds), Divided
Nation: Social and Cultural Change in Britain, Hodder and
Stoughton & The Open University, London.
Wards I. (ed), 1976, New Zealand atlas, A.R. Shearer,
Government Printer, Wellington.
Ware C., 1988, Color sequences for univariate maps:
theory, experiments, and principles, IEEE Computer
Graphics and Applications, Vol.8, No.9, pp.41-49.
Warne F.J., 1917, Warne's book of charts, Frank J. Warne,
420-421 Southern Building, Washington D.C..
Walker F.A., 1870a, Statistical atlas of the United States,
based on the results of the Ninth Census, 1870, US Bureau
of the Census, Washington D.C..
Warnes A.M. & Law C.M., 1984, The elderly population
of Great Britain: locational trends and policy implications,
Transactions of the Institute of British Geographers, Vol.9,
pp.37-59.
Walker F.A., 1870b, The statistics of the wealth and
industry of the United States, Ninth Census, Vol.III, US
Government printing office, Washington.
Warntz W.W., 1967, Global science and the tyranny of
space, Papers of the Regional Science Association, Vol.19,
pp.7-19.
Walker P.A. & Cocks K.D., 1984, Computerised
choropleth mapping of Australian resources data,
Cartography, Vol.13, No.4, pp.243-253.
Warntz W.W., 1973, First variations on the theme,
cartographics is to geographical science as graphics is to
science generally, Applied geography and the human
environment, Vol.2, pp.54-85.
Wallace J.W., 1926, Population map for health officers,
American Journal of Public Health, Vol.16, No.10, p.1023..
Waller R., 1985, The atlas of British politics, Croom Helm,
London.
Wallin A. & Gerig G., 1990, Automatic construction of isosurfaces from volume data, Proceedings of the 4th
International Symposium on Spatial Data Handling, Zurich,
Vol.1, pp.354-362.
Wallin E., 1984, Isarithm maps and geographical
disaggregation, Proceedings of the International
Symposium on Spatial Data Handling, pp.209-217, August,
Zurich, Switzerland.
Wallis J.R., 1987, Hydrology - the computer revolution
continues, Reviews of Geophysics, Vol.25, No.2, pp.101105.
Walsh S.J., 1988, On error in GIS (reply), Photogrammetric
Engineering and Remote Sensing, Vol.54, No.3, pp.343344.
Walton D.J., 1987, Terrain modelling with B-spline type
surfaces defined on curved knot lines, Image and Vision
Computing, Vol.5, No.1, pp.37-43.
Warantz W.W., 1975, The pattern of patterns: current
problems of sources of future solutions, R. Abler, D.
Janelle, A. Philbrick & J. Sommer (eds), Human Geography
in a Shrinking World, Duxbury Press, Massachusetts
Warntz W.W., 1976, A note on surfaces and paths and
applications to geographical problems, S. Angel & G.M.
Hyman, Urban Fields, Pion Limited, London, pp.121-134.
Wasowski R.J., 1985, The Macintosh microcomputer as a
digital image processor, Technical papers of the ACSMASPRS Fall Convention, September, Indianapolis, Indiana,
pp.625-635.
Waters N.M. & De Leeuw G.J.A., 1987, Computer atlases
to complement printed atlases, Cartographica, Vol.24,
No.1, pp.118-133.
Watson S.G., Millham C.B., Lewis K. & Stulik D., 1986,
Computer graphics display of 3-D analysis data, Surface
and Interface Analysis, Vol.8, No.4, pp.139-146.
Watts H.D., 1968, The industrial journey-to-work in a rural
area, Journel of the Town Planning Institute, Vol.54, No.2,
pp.79-80.
Bibliography
Webber R. & Craig J., 1978, Socio-economic classification
of local authority areas, Studies on Medical and Population
Subjects No.35, H.M.S.O., London.
Webster C.J. & Omare C.N., 1991, A formal approach to
objectoriented spatial database design, EGIS '91
Proceedings, pp.1209-1218, Brussels, Belgium.
Weeks J.R., 1985, The shape of space: how to visualize
surfaces and three-dimensional manifolds, Marcel Dekker
Inc., New York.
Wehrend S. & Lewis C., 1990, A problem-orientated
classification of visualization techniques, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.139-143, October, San Francisco, California.
Weibel R., 1987, An adaptive methodology for automated
relief generalization, AutoCarto 8 (Proceedings of the 8th
International Symposium on Computer-Assisted
Cartography), pp.42-49 .
230
Wilkie R.W., 1976, Maps and cartograms, Statistical
abstracts of Latin America, Vol.17, pp.1-23.
Wilkie R.W., 1977, Population cartograms and political
subdivisions: Latin American populations in the 1970s,
Statistical abstracts of Latin America, Vol.18, pp.1-26.
Wilkie R.W., 1980, Latin American cartogram, Statistical
abstracts of Latin America, Vol.21, p.xxvii.
Williams A.V., 1978, Interactive cartogram production on a
microprocessor graphics system, Proceedings of the
American Congress on Surveying and Mapping, Fall
Technical Meeting, Albuquerque, New Mexico, pp.426431.
Williams L.G., 1971, Obtaining information from displays
with discrete elements, H.W. Castner & G. McGraths (eds)
Map Design and the Map User, Cartographica, monograph
No.2, pp.29-34.
Weigert H.W., 1957, Principles of political geography, ,
New York, pp.293-297.
Williams P.M., Goldey D. & Harrison M., 1970, French
politicians and elections, 1951-1969, Cambridge University
Press, London.
Weissman M.A., 1988, A 3-D stereoscopic imaging system,
Electronic Imaging '88, Anaheim, California, pp.339A339D.
Williams R.L., 1976, The misuse of area in mapping
census-type numbers, Historical Methods Newsletter,
Vol.9, No.4, pp.213-216.
Wellar B.S., 1985, The significance of automated
cartography to an information society, Cartographica,
Vol.22, No.4, pp.38-50.
Williams T., 1969, Social gradients in the city: a trend
surface analysis of enumeration district data for inner
London, Discussion Paper, Graduate Geography
Department, London School of Economics and Political
Science.
Wenzel E.M., Stone P.K., Fisher S.S. & Foster S.H.,
1990, A system for three-dimensional acoustic
"visualization" in a virtual environment workstation,
Visualization '90, Proceedings of the First IEEE Conference
on Visualization, pp.329-337, October, San Francisco,
California.
Williams-Pickle L., Mason T.J., Howard N., Hoover R. &
Faumeni J.F., 1987, Atlas of U.S. cancer mortality among
whites: 1950-1980, U.S. Department of Health and Human
Services, USA.
Wheate R., 1985a, Atlas of the holocaust (review),
Cartographica, Vol.22, No.1, pp.123-124.
Williamson, 1982, The equal contrast gray scale, The
American Cartographer, Vol.9, No.2, pp.131-139.
Wheate R., 1985b, The war atlas (review), Cartographica,
Vol.22, No.3, p.119.
Wills G., Bradley R., Haslett J. & Unwin A., 1990,
Statistical exploration of spatial data, Proceedings of the
4th International Symposium on Spatial Data Handling,
Zurich, Vol.1, pp.491-500.
White D., 1985, Tilting Britain onto its side, New Society,
Vol.72, pp.383-385.
White M.S., Griffin P.E., 1980, Coordinate free
cartography, AutoCarto IV (Proceedings of the
International Symposium on Cartography and Computing:
Applications in Health and the Environment, Virginia),
Vol.2, pp.236-245.
White R.D., 1984, The structural complexity of multivariate
maps in national atlases, M.A., Boston University.
Willumsen U., 1986, Interior colour-combination schemes
by computer, Color Research and Application
(Supplement), Vol.11 pp.89-90.
Wilsher P. & Cassidy J., 1987, Two nations: the false
frontier, Sunday Times, News in Focus, 11 January, pp.2426 .
Wilson G., 1988, The life and times of cellular automata,
New Scientist, Vol.120, No.1633, pp.44-47.
White R.R., 1972, Probability maps of leukaemia
mortalities in England and Wales, N.D.McGlashan (ed)
Medical Geography: Techniques and Field Studies,
Methuen and Co. Ltd., London, Chapter 13.
Wingate W.J.G., 1986, Tesseral arithmetic in more than
two dimensions, S.B.M. Bell and B.M. Diaz (eds) Spatial
Data Processing using Tesseral Methods, pp.257-264.
Whitted T., 1980, An improved illumination model for
shaded display, Communications of the ACM, Vol.23,
No.6, pp.343-349.
Winkler K.H.A., Chalmers J.W., Hodson S.W.,
Woodward P.R. & Zabusky N.J., 1987, A numerical
laboratory, Physics Today, October, pp.28-37.
Wigert-Johnston M.E., 1987, Color graphic displays for
network planning and design, H.J. Durrett (ed) Color and
the Computer, Ch.7, pp.139-150.
Witkin A. & Kass M., 1988, Spacetime constraints,
Computer Graphics, Vol.22, No.4, pp.159-168.
231
Wittkowski K.M., 1987, An expert system approach for
generating and testing statistical hypotheses, B. Phelps (ed)
Interactions in Artificial Intelligence and Statistical
Methods, Gower Technical Press, Aldershot, pp.45-59.
Wolfe R.H. & Liu C.N., 1988, Interactive visualization of
3D seismic data: a volumetric method, IEEE Computer
Graphics and Applications, Vol.8, No.7, pp.24-30.
Wolff R.S., 1988, Visualization in the eye of the scientist,
Computers in Physics, Vol.2, No.3, pp.28-35.
Wolfram S., 1986, Cellular automata fluids 1: basic theory,
Journal of Statistical Physics, Vol.45, Nos.3/4, pp.471-527.
Wong C.C.K., 1982, Refinement of dense digital elevation
models, AutoCarto 5 (Proceedings of the 5th International
Symposium on Computer-Assisted Cartography), pp.703714.
Wong W., 1972, Principles of two-dimensional design, Van
Nostrand Reinhold, New York.
Wood A.H. (ed), 1987, The Times guide to the house of
commons: june 1987, Times Books, London.
Wood D., 1985, Graphics and graphic informationprocessing (review), Cartographica, Vol.22, No.2, pp.118121.
Wood D., 1986, Review: The new state of the world atlas,
The American Cartographer, Vol.13, No.2, pp.172-173.
Wood M., 1979, Maps, distortion and meaning (review),
The American Cartographer, Vol.6, No.1, pp.83-84.
Woodward D., 1982, Map design and the national
consciousness: typography and the look of topographic
maps, ACSM-ASP 42nd Annual Convention, March,
Denver Colorado, pp.339-347.
Woodwark J. (ed), 1989, Geometric reasoning, Clarendon
Press, Oxford.
Woodwark J.R., 1986, Techniques of spatial segmentation
in solid modelling, S.B.M. Bell and B.M. Diaz (eds) Spatial
Data Processing using Tesseral Methods, pp.325-330.
World Bank, 1979, World atlas of the child, The Works
Bank, Washington D.C..
World Health Organisation, 1985, Atlas of cancer in
Scotland 1975-1980, I. Kemp, P.Boyle, M.Smans and C.
Muir (eds), WHO, International Agency for Research on
Cancer and The Cancer Registries of Scotland, IARC
Scientific Publications No.72, Lyon.
Worth C., 1978a, The construction of computer produced
views of three-dimensional data, Graduate Geography
School Discussion Paper No.67, London School of
economics and Political Science.
Worth C., 1978b, Determining a vertical scale for
graphical representations of three-dimensional surfaces,
The Cartographic Journal, Vol.15, No.2, pp.86-92.
Woytinsky W.S. & Woytinsky E.S., 1953, World
population and production trends and outlook, Twentieth
Century Fund.
Bibliography
Wright J.K. (ed), 1938, Notes on statistical mapping,
American Geographical Society and Population Association
of America, New York.
Wright J.K., 1942, Map makers are human: comments on
the subjective in maps, The Geographical Review, Vol.32,
No.4, pp.527-544.
Wright J.K., 1955, "Crossbreeding" geographical
quantities, The Geographical Review, Vol.45, No.1, pp.5265.
Wright J.K., 1988, The plain fellow's guide to geographic
information systems, The Geographical Journal, Vol.154,
No.2, pp.161-168.
Wright P., 1989, The ghosting of the inner city, L.
McDowell, P.Sarre and C. Hamnett (eds), Divided Nation:
Social and Cultural Change in Britain, Hodder and
Stoughton & The Open University, London.
Yacoob Y., 1990, Displaying voxel-based object according
to their qualitative shape synthesis, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.51-58, October, San Francisco, California.
Yamakawa S., 1990, A brief chronology of the world
disasters, Science Reports of the Institute of Geoscience,
University of Tsukuba, Section A Geographical Sciences,
Vol.11, pp.19-37.
Yan J.K., 1985, Advances in computer-generated imagery
for flight simulation, IEEE Computer Graphics and
Applications, Vol.5, No.8, pp.38-51.
Yen C.C.J., Bedford K.W., Kempf J.L. & Marshall R.E.,
1990, A three-dimensional/stereoscopic display and model
control system for Great Lakes forecasts, Visualization '90,
Proceedings of the First IEEE Conference on Visualization,
pp.194-201, October, San Francisco, California.
Yeorgaroudakis Y., 1991, Visual representation of spatially
referenced multimedia objects, EGIS '91 Proceedings,
pp.1250-1260, Brussels, Belgium.
Yoeli P., 1983a, About cartographic contouring with
computers, AutoCarto 6 (Proceedings of the 6th
International Symposium on Computer-Assisted
Cartography), Vol.2, pp.262-266.
Yoeli P., 1983b, Shadowed contours with computer and
plotter, The American Cartographer, Vol.10, No.2, pp.101110.
Yoeli P., 1984, Computer-assisted determination of the
valley and ridge lines of digital terrain models, The
International Yearbook of Cartography, Vol.24, pp.197206.
Yoeli P., 1986, Computer executed production of a regular
grid of height points from digital contours, The American
Cartographer, Vol.13, No.3, pp.219-229.
Yokoya N. & Yamamoto K., 1989, Fractal-based analysis
and interpolation of 3D natural surface shapes and their
application to terrain modeling, Computer Vision,
Graphics, and Image Processing, Vol.46, pp.284-302.
Young F.W., Kent D.P. & Kuhfeld W.F., 1988, Dynamic
graphics for exploring multivariate data, W.S. Cleveland &
M.E. McGill (eds) Dynamic Graphics For Statistics,
Wandsworth, California.
Bibliography
232
Youngmann C., 1989, Spatial data structures for modeling
subsurface features, J. Raper (ed), GIS: Three Dimensional
Applications in Geographic Information Systems, Taylor &
Francis, London.
Zarzycki J.M., 1988, Cartographic data depiction: digital
and conventional, World Cartography, Vol.19, pp.15-23.
Zlotin R.I., Zonn S.V., Liliyenberg D.A., 1989, National
atlases: concepts, content, and experience in development,
Mapping Sciences and Remote Sensing, Vol.26, No.1,
pp.31-40.
Yuill R.S., 1964, A simulation study of the barrier effects in
spatial diffusion problems, Technical Report No.1, Task
No. 389-140, Contract 1228(33), Office of Naval Research,
Geography Branch, Department of Geography,
Northwestern University, Evanston, Illinois.
Zyda M.J., Mcghee R.B., Ross R.S., Smith D.B. & Streyle
D.G., 1988, Flight simulators for under $100,000, IEEE
Computer Graphics and Applications, Vol.8, No.1, pp.1927.
Zahradnitzky G., 1985, The time atlas of the oceans
(review), Cartographica, Vol.22, No.4, pp.94-95.
200
Total = 1762
175
150
125
Number
100
75
50
25
0
1852
1900
1940
1950
1960
1970
1980
Year of Publication
Figure 29: References Over Time
1990
Appendices
233
Appendices
Appendix A lists the algorithm which was developed to create most of the
illustrations in this dissertation.
The other appendices are included because the tables shown could not
easily be derived from any other single source and were all specifically
constructed for the purposes of this thesis. Without such lists, and the lookup tables of the relationships between so many different areas, many of the
pictures created here could not have been produced.
Appendix B gives a table linking the changing geography of parliamentary
constituencies between three incompatible time periods. It represents the
consolidation and extension of two previous studies. Appendix C provides
the complete list of general election results for these constituencies. It was
created from many separate sources and was meticulously checked by
hand. Appendix D gives a list of the changing average house prices in each
of the constituencies during most of the 1980s. It was compiled from
building society records and a sophisticated translation of the postcode to
administrative geographies was made. Appendix E shows how the
particularly difficult problems of local geography in Scotland were
handled, through several months of monotonous manual work with high
scale maps and geographical indexes. Appendix F presents the ward
cartogram created for this thesis, in detail. A complete list of the wards is
given, along with a dozen insets showing, close up, the actual cartogram
covering most of Britain.
Literally hundreds of millions of statistics were used to create the pictures
shown here. Most were available from national sources as computer files:
the government census office, research council data archive and national
information system (NOMIS). Other information was amassed from
contacts within the author's department; the cancer registry figures, local
election results and building society statistics were collected in this way.
Still more numbers were laboriously typed in from paper sources, the 1988
Scottish local election results, for instance. Listed here are those tables
which had to be specifically created for this dissertation and for which
others may find a use.
Circular Cartogram Algorithm
234
Appendix A: Circular Cartogram Algorithm
The algorithm is presented in its Pascal implementation and annotated. The
entire code required to produce the cartograms is contained in the next two
pages. The final page gives an example of the sort of information the
program listed would require, as input, to create a circular cartogram. All
that is needed is a list of the places to be included giving their initial
centroids in Euclidean space, population, and their topological
relationships with their neighbours (indicated by length of a common
border). Changing these topological relationships will alter the final image
so, for instance, a bridge can be represented as a 10km border in the file to
have an influence. The program outputs a list of the circles' radii and
centroids after an arbitrary number of iterations.
The algorithm could be improved by being able to decide when to finish.
However, it is difficult to avoid settling on local solutions too early. It is
most effective to be able to see the operation in progress on a computer
screen, and to influence the final image through interaction with a pointer.
Such procedures are not included in the following listing as they would
complicate it and are machine dependant. If a way could be found to avoid
having to rebuild the two-dimensional tree structure at every iteration (as it
changes very little) that could substantially increase the efficiency of the
algorithm. It should be noted that, as given, the code could easily be
implemented on systems based on massively parallel architecture. The
algorithm is also fast enough to run on a microcomputer with over ten
thousand units, as it stands.
APPENDIX A
program cartogram (output);
{Pascal translation of Basic V MakeCarto program.}
{This version is geared to real numbers as the mainframe it was tested on
appears not to realizethat life is easier without them. The Basicand C versions
which were actually used ran on Archimedes and Sun machines with RISC chips in
them. Both were of course much faster (a Fortran translation was made - this is
possible, but, like most things in that language, not a good idea). Pascal is
used here as it is most widely understood.}
{The two recursive procedures and tree structure are not strictly neccessary,
but speed things up by a couple of orders of magnitude or more, and so are
included.}
{Constants are currently set for the 64 counties and 10,000 iterations - a
suitably large number
(Counties do actually converge very quickly – there are no problems with the
algorithm's speed - in fact it appears to move from O(n*n) to O(n log n)until
other factors come into play when n reaches between 10,000 and 100,000 zones...}
const
itters = 10000;
zones = 64;
ratio = 0.4;
friction = 0.25;
pi = 3.141592654;
{has to be some-what less than 0.5}
{this is another magic number ...}
{both of these are explained in the text}
type
vector = array [1..zones] of real;
index = array [1..zones] of integer;
vectors = array [1..zones, 1..21] of real;
{no zone in any of the}
indexes = array [1..zones, 1..21] of integer; {collections I use has}
leaves = record
{more than 21 neighbours}
id
: integer;
xpos
: real;
ypos
: real;
left
: integer;
right
: integer;
end;
trees = array [1..zones] of leaves;
var
infile, outfile
list
tree
widest, distance
closest, overlap
xrepel, yrepel, xd, yd
xattract, yattract
displacement
atrdst, repdst
total_distance
total_radius, scale
xtotal, ytotal
zone, nb
other, itter
end_pointer, number
x, y
xvector, yvector
perimeter, people, radius
border
:text;
:index;
:trees;
:real;
:real;
:real;
:real;
:real;
:real;
:real;
:real;
:real;
:integer;
:integer;
:integer;
:index;
:vector;
:vector;
:vectors;
{input and output files}
{list for nearest neighbours}
{tree structure - see below}
{Suitably small distance units}
{should be used - for Britain}
{metres is standard. It makes}
{little difference if reals are}
{used, on most machines integers}
{are much faster and more sensible}
{ - even for gravity type models!}
{-arrays for zone centroids}
{-arrays for zone velocities}
{-other information about the zones}
{-border lengths between zones}
nbours
nbour
:index;
:indexes;
{-number of neighbours per zone}
{-zone neighbours - 0 for the sea}
{Recursive procedure to add the zone designated by global variable "zone" to
the "tree" structure - this was written in a hurry, is messy but works - I'm
afraid it uses a lot of global variables, but the structure is probably well
known to any reader who already works with computers and geographic data.}
procedure add_point(pointer,axis :integer);
begin
if tree[pointer].id = 0 then
{there is a free leaf so put the
begin
zone on it}
tree[pointer].id := zone;
tree[pointer].left := 0;
tree[pointer].right:= 0;
tree[pointer].xpos := x[zone];
tree[pointer].ypos := y[zone];
end
else
{Decide which way to go down the
if axis = 1 then
tree depending on whether we
if x[zone] >= tree[pointer].xpos then are at a horizontal or vertical
begin
“branch" and where the zone to
if tree[pointer].left = 0 then
be placed is}
begin
end_pointer := end_pointer +1;
tree[pointer].left := end_pointer;
end;
add_point(tree[pointer].left,3-axis);
end
else
begin
if tree[pointer].right = 0 then
begin
end_pointer := end_pointer +1;
tree[pointer].right := end_pointer;
end;
add_point(tree[pointer].right,3-axis);
end
else
if y[zone] >= tree[pointer].ypos then
begin
if tree[pointer].left = 0 then
begin
end_pointer := end_pointer +1;
tree[pointer].left := end_pointer;
end;
add_point(tree[pointer].left,3-axis);
end
else
begin
if tree[pointer].right = 0 then
begin
end_pointer := end_pointer +1;
tree[pointer].right := end_pointer;
end;
add_point(tree[pointer].right,3-axis);
end
end;
{This procedure recursively recovers the "list" of zones within “distance"
horizontally or vertically of the "zone" from the "tree". The list length is
given by the integer "number".}
procedure get_point(pointer, axis :integer);
begin
if pointer>0 then
if tree[pointer].id > 0 then
begin
if axis = 1 then
begin
if x[zone]-distance < tree[pointer].xpos then
get_point(tree[pointer].right,3-axis);
if x[zone]+distance >= tree[pointer].xpos then
get_point(tree[pointer].left,3-axis);
end;
if axis = 2 then
begin
if y[zone]-distance < tree[pointer].ypos then
get_point(tree[pointer].right,3-axis);
if y[zone]+distance >= tree[pointer].ypos then
get_point(tree[pointer].left,3-axis);
end;
if (x[zone]-distance < tree[pointer].xpos)
and (x[zone]+distance >= tree[pointer].xpos) then
if (y[zone]-distance < tree[pointer].ypos)
and (y[zone]+distance >= tree[pointer].ypos) then
begin
number := number +1;
list[number] := tree[pointer].id;
end;
end;
end;
{Here's the program, first of all set input and output and intitialize a few
things.}
begin
reset(infile,'FILE=county.in');
rewrite(outfile,'FILE=county.out');
total_distance :=0;
total_radius := 0;
{read in the data (an example input file follows this program) and find a
standard scale for calculating the zone's circle radii.}
for zone := 1 to zones do
begin
read(infile, people[zone],x[zone], y[zone], nbours[zone]);
perimeter[zone] := 0;
for nb := 1 to nbours[zone] do
begin
read(infile,nbour[zone,nb], border[zone,nb]);
perimeter[zone] := perimeter[zone] + border[zone,nb];
if nbour[zone,nb] > 0 then
if nbour[zone,nb] < zone then
begin
xd := x[zone]- x[nbour[zone,nb]];
yd := y[zone]- y[nbour[zone,nb]];
total_distance := total_distance + sqrt(xd*xd+yd*yd);
total_radius := total_radius + sqrt(people[zone]/pi)
+ sqrt(people[nbour[zone,nb]]/pi);
end;
end;
readln(infile);
end;
writeln ('Finished reading in topology');
scale := total_distance / total_radius;
widest := 0;
{widest is to be the radius of the widest circle
for zone := 1 to zones do
to help find overlaps later}
begin
radius[zone] := scale * sqrt(people[zone]/pi);
if radius[zone] > widest then
widest := radius[zone];
xvector[zone] := 0;
yvector[zone] := 0;
end;
writeln ('Finished scaling by ',scale,' widest is ',widest);
{main iteration loop of cartogram algorithm}
for itter := 1 to itters do
begin
{bit of program to create a tree}
for zone := 1 to zones do
tree[zone].id := 0;
end_pointer := 1;
for zone := 1 to zones do
add_point(1,1);
{end of tree building}
displacement := 0.0;
{to keep a note of how much things are moving.}
{loop of independent displacements}
for zone := 1 to zones do
begin
xrepel := 0.0;
yrepel := 0.0;
xattract := 0.0;
yattract := 0.0;
closest := widest; {to find out the closest neighbour}
{get all points within widest+radius(zone) into list of length "number"}
number := 0;
distance := widest + radius[zone];
get_point(1,1);
{work out repelling force of overlapping neighbours}
if number > 0 then
for nb := 1 to number do
begin
other := list[nb];
if other <> zone then
begin
xd := x[zone]-x[other];
yd := y[zone]-y[other];
distance := sqrt(xd * xd + yd * yd);
if distance < closest then
closest := distance;
overlap := radius[zone] + radius[other] - distance;
if overlap > 0.0 then
if distance > 1.0 then
begin
xrepel := xrepel - overlap*(x[other]-x[zone])/distance;
yrepel := yrepel - overlap*(y[other]-y[zone])/distance;
end;
end;
end;
{work out forces of attraction between neighbours}
for nb := 1 to nbours[zone] do
begin
other := nbour[zone,nb];
if other <> 0 then
begin
xd := x[zone]-x[other];
yd := y[zone]-y[other];
distance := sqrt(xd * xd + yd * yd);
overlap := distance - radius[zone] - radius[other];
if overlap > 0.0 then
begin
overlap := overlap * border[zone,nb] / perimeter[zone];
xattract:= xattract +overlap*(x[other]-x[zone])/distance;
yattract:= yattract +overlap*(y[other]-y[zone])/distance;
end;
end;
end;
{now work out the combined effect of attraction and repulsion}
atrdst := sqrt(xattract*xattract+yattract*yattract);
repdst := sqrt(xrepel*xrepel+yrepel*yrepel);
if repdst > closest then {Things are too close, scale them}
begin {down to avoid "whiplash" effects}
xrepel := closest * xrepel / (repdst + 1);
yrepel := closest * yrepel / (repdst + 1);
repdst := closest;
end;
if repdst > 0 then
begin
xtotal := (1-ratio)*xrepel+ratio*(repdst*xattract/(atrdst+1));
ytotal := (1-ratio)*yrepel+ratio*(repdst*yattract/(atrdst+1));
end
else
{nothing's overlapping}
begin
if atrdst > closest then
{don't move too fast!}
begin
xattract := closest*xattract/(atrdst+1);
yattract := closest*yattract/(atrdst+1);
end;
xtotal := xattract;
ytotal := yattract;
end;
{record the vector for posterity}
xvector[zone] := friction *(xvector[zone]+xtotal);
yvector[zone] := friction *(yvector[zone]+ytotal);
displacement := displacement + sqrt(xtotal*xtotal+ytotal*ytotal);
end;
{update the positions}
for zone := 1 to zones do
begin
x[zone] := x[zone] + round(xvector[zone]);
y[zone] := y[zone] + round(yvector[zone]);
end;
displacement := displacement / zones;
writeln('Iteration ', itter, ' displacement ', displacement);
end;
{we've finished all the iterations so write out the new file}
for zone := 1 to zones do
writeln(outfile,radius[zone]:9:0,',',x[zone]:9,',',y[zone]:9);
end.
Parliamentary Constituencies 1955-1987 Continuity
238
Appendix B: Parliamentary Constituencies 1955-1987
Continuity
A list of seven hundred and five parliamentary constituencies was
constructed by hand, covering a period containing two substantial
boundary changes. In many cases the link over time has had to be between
best fitting constituencies. Twenty eight constituencies disappeared
between 1970 and 1974, and twenty seven were lost between 1979 and
1983. However, at each change the overall number of constituencies
increased (from 630, to 635, and then 650). Sedgefield is a unique
example; it existed, as a constituency, between 1955 and 1970 (number
681 in the table), disappeared from 1974 to 1979, but reappeared from
1983 to 1987 (as number 12). Often the name has changed, as with
Newcastle North-, from West-, but the actual area and people have
remained substantially the same.
Official names and press association numbers are given for each
constituency in each period, along with the identification number used in
this thesis. Cartograms indexed by this consistent code, are included for
the years 1955, 1964, 1970, 1979 and 1987. The changing boundaries of
Greater London, the West Midands, Greater Manchester and the central
Glasgow conurbation are shown on all the diagrams, as are the Welsh and
Scottish borders. These were all carefully added by hand to ensure
continuity had been maintained. The constituencies of Northern Ireland are
also shown, as they increased in number, and their changing electorates
altered the sizes of the circles in population space.
The major two sources for estimating the extent of continuity and
implication of parliamentary constituency name changes were Butler D.E.
& Kavanagh D. (1974, pp.282-283) for 1970-1974, and BBC/ITN (1983)
for 1979-1983. The constituencies are grouped by region.
PANo. stands for the "Press Association Number" of the constituency, and
can be seen to change between redistributions of seats.
Parliamentary Constituencies
1955 - 1987 Continuity
The North
No.
Name 1983 to 1987
PANo.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
BARROW & FURNESS
26
BERWICK-UPON-TWEED
43
BISHOP AUCKLAND
61
DURHAM CITY OF
209
COPELAND
286
EASINGTON
610
HEXHAM
374
LANGBAURGH
478
DURHAM NORTH
135
DURHAM NORTH WEST
211
PENRITH & THE BORDER
458
SEDGEFIELD
207
WANSBECK
413
WESTMORLAND & LONSDALE 621
WORKINGTON
642
BLAYDON
68
BLYTH VALLEY
157
CARLISLE
256
DARLINGTON
175
GATESHEAD EAST
584
HARTLEPOOL
298
HOUGHTON & WASHINGTON
323
JARROW
339
MIDDLESBROUGH
314
NEWCASTLE UPON TYNE CENTL422
NEWCASTLE UPON TYNE EAST 417
NEWCASTLE UPON TYNE NORTH418
REDCAR
475
SOUTH SHIELDS
212
STOCKTON NORTH
543
STOCKTON SOUTH
573
SUNDERLAND NORTH
556
SUNDERLAND SOUTH
557
TYNE BRIDGE
257
TYNEMOUTH
587
WALLSEND
595
Name 1974 to 1979
BARROW IN FURNESS
BERWICK UPON TWEED
BISHOP AUCKLAND
DURHAM
WHITEHAVEN
EASINGTON
HEXHAM
CLEVELAND
CHESTER-LE-STREET
NORTH WEST DURHAM
PENRITH AND THE BOR
MORPETH
WESTMORLAND
WORKINGTON
BLAYDON
BLYTH
CARLISLE
DARLINGTON
GATESHEAD EAST
THE HARTLEPOOLS
HOUGHTON-LE-SPRING
JARROW
MIDDLESBOROUG
NEWCASTLE/TYNE N.
NEWCASTLE/TYNE EAST
NEWCASTLE/TYNE WEST
REDCAR
SOUTH SHIELDS
STOCKTON
THORNABY
SUNDERLAND NORTH
SUNDERLAND SOUTH
GATESHEAD WEST
TYNEMOUTH
WALLSEND
PANo.
35
59
75
206
611
209
310
152
142
207
462
0
415
609
628
80
81
130
171
251
294
321
342
407
425
424
426
482
536
548
572
557
558
252
583
590
Name 1955 to 1970
BARROW IN FURNESS
BERWICK UPON TWEED
BISHOP AUCKLAND
DURHAM
WHITEHAVEN
EASINGTON
HEXHAM
CLEVELAND
CHESTER-LE-STREET
NORTH WEST DURHAM
PENRITH AND THE BOR
MORPETH
WESTMORLAND
WORKINGTON
BLAYDON
BLYTH
CARLISLE
DARLINGTON
GATESHEAD EAST
THE HARTLEPOOLS
HOUGHTON-LE-SPRING
JARROW
MIDDLESBROUGH,EAST
NEWCASTLE/TYNE N.
NEWCASTLE/TYNE EAST
NEWCASTLE/TYNE WEST
SOUTH SHIELDS
MIDDLESBROUGH,WEST
SUNDERLAND NORTH
SUNDERLAND SOUTH
GATESHEAD WEST
TYNEMOUTH
WALLSEND
PANo.
36
57
76
196
605
200
296
149
142
197
456
0
411
603
623
80
81
131
166
241
282
307
326
402
421
420
422
0
531
0
403
552
553
242
573
578
Yorkshire & Humberside
No.
Name 1983 to 1987
PANo.
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
BARNSLEY WEST & PENISTONE 25
BEVERLEY
287
BOOTHFERRY
324
BRIDLINGTON
258
BRIGG & CLEETHORPES
609
CALDER VALLEY
530
COLNE VALLEY
153
DEWSBURY
191
DONCASTER NORTH
189
DON VALLEY
190
ELMET
229
HARROGATE
297
HEMSWORTH
307
KEIGHLEY
340
NORMANTON
434
PONTEFRACT & CASTLEFORD 578
RICHMOND (YORKS)
481
ROTHER VALLEY
614
RYEDALE
542
SCARBOROUGH
505
SELBY
504
SHEFFIELD HALLAM
510
SHEFFIELD HILLSBOROUGH
457
SHIPLEY
514
SKIPTON & RIPON
519
WENTWORTH
487
BARNSLEY CENTRAL
23
BARNSLEY EAST
304
BATLEY & SPEN
93
BRADFORD NORTH
80
BRADFORD SOUTH
81
BRADFORD WEST
82
DONCASTER CENTRAL
405
GLANFORD & SCUNTHORPE
31
GREAT GRIMSBY
281
HALIFAX
45
HUDDERSFIELD
326
HULL EAST
327
HULL NORTH
325
HULL WEST
328
LEEDS CENTRAL
359
LEEDS EAST
356
LEEDS NORTH EAST
357
LEEDS NORTH WEST
354
LEEDS WEST
533
LEEDS SOUTH & MORLEY
358
PUDSEY
470
ROTHERHAM
489
SHEFFIELD ATTERCLIFFE
507
SHEFFIELD BRIGHTSIDE
509
SHEFFIELD CENTRAL
513
SHEFFIELD HEELEY
508
WAKEFIELD
593
YORK
650
Name 1974 to 1979
PENISTONE
HALTEMPRICE
HOWDEN
BRIDLINGTON
LOUTH
SOWERBY
COLNE VALLEY
DEWSBURY
DONCASTER
DEARNE VALLEY
BARKSTON ASH
HARROGATE
HEMSWORTH
KEIGHLEY
NORMANTON
PONTEFRACT
RICHMOND#YORKSHIRE#
ROTHER VALLEY
THIRSK AND MALTON
SCARBOROUGH AND WHI
GOOLE
SHEFFIELD,HALLAM
SHEFFIELD,HILLSBORO
SHIPLEY
SKIPTON
BARNSLEY
BRIGHOUSE AND SPENB
BRADFORD,NORTH
BRADFORD,SOUTH
BRADFORD,WEST
DON VALLEY
BRIGG
GRIMSBY
HALIFAX
HUDDERSFIELD,EAST
KINGSTON/HULL EAST,
HULL CENTRAL
KINGSTON/HULL WEST,
LEEDS,SOUTH
LEEDS,EAST
LEEDS,NORTH EAST
LEEDS,NORTH WEST
LEEDS,WEST
BATLEY AND MORLEY
PUDSEY
ROTHERHAM
SHEFFIELD,ATTERCLIF
SHEFFIELD,BRIGHTSID
SHEFFIELD,PARK
SHEFFIELD,HEELEY
WAKEFIELD
YORK
PANo.
461
284
323
102
386
537
156
186
187
175
33
290
301
343
437
469
488
496
571
514
270
518
520
522
526
0
34
0
104
91
92
93
188
103
277
283
324
327
326
328
360
357
358
359
362
41
477
495
516
517
521
519
588
635
Name 1955 to 1970
PENISTONE
HALTEMPRICE
HOWDEN
BRIDLINGTON
LOUTH
SOWERBY
COLNE VALLEY
DEWSBURY
DONCASTER
DEARNE VALLEY
BARKSTON ASH
HARROGATE
HEMSWORTH
KEIGHLEY
NORMANTON
PONTEFRACT
RICHMOND#YORKSHIRE#
ROTHER VALLEY
THIRSK AND MALTON
SCARBOROUGH AND WHI
GOOLE
SHEFFIELD,HALLAM
SHEFFIELD,HILLSBORO
SHIPLEY
SKIPTON
BARNSLEY
BRIGHOUSE AND SPENB
BRADFORD,NORTH
BRADFORD,SOUTH
BRADFORD,WEST
DON VALLEY
BRIGG
GRIMSBY
HALIFAX
HUDDERSFIELD,EAST
KINGSTON/HULL EAST,
KINGSTON/HULL NORTH
KINGSTON/HULL WEST,
LEEDS,SOUTH
LEEDS,EAST
LEEDS,NORTH EAST
LEEDS,NORTH WEST
LEEDS,WEST
BATLEY AND MORLEY
PUDSEY
ROTHERHAM
SHEFFIELD,ATTERCLIF
SHEFFIELD,BRIGHTSID
SHEFFIELD,PARK
SHEFFIELD,HEELEY
WAKEFIELD
YORK
PANo.
455
273
309
98
380
533
153
179
180
169
32
278
287
327
430
462
480
488
563
507
262
512
514
516
519
0
34
0
101
92
93
94
181
100
269
272
310
334
335
336
350
347
348
349
352
41
472
487
510
511
515
513
576
630
East Midlands
No.
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
Name 1983 to 1987
PANo.
AMBER VALLEY
390
ASHFIELD
15
BASSETLAW
29
BLABY
62
BOLSOVER
70
BOSWORTH
412
BROXTOWE
101
CORBY
342
DAVENTRY
146
LINDSEY EAST
319
EREWASH
184
GAINSBOROUGH & HORNCASTLE254
GEDLING
123
GRANTHAM
276
HARBOROUGH
293
HIGH PEAK
309
HOLLAND WITH BOSTON
318
KETTERING
647
LOUGHBOROUGH
92
MANSFIELD
493
NEWARK
416
DERBYSHIRE NORTH EAST
235
LEICESTERSHIRE NORTH WEST 384
RUSHCLIFFE
267
RUTLAND & MELTON
399
SHERWOOD
415
DERBYSHIRE SOUTH
183
STAMFORD & SPALDING
46
WELLINGBOROUGH
520
DERBYSHIRE WEST
185
CHESTERFIELD
136
DERBY NORTH
181
DERBY SOUTH
182
LEICESTER EAST
361
LEICESTER SOUTH
362
LEICESTER WEST
364
LINCOLN
377
NORTHAMPTON NORTH
435
NORTHAMPTON SOUTH
567
NOTTINGHAM EAST
441
NOTTINGHAM NORTH
442
NOTTINGHAM SOUTH
443
Name 1974 to 1979
ILKESTON
ASHFIELD
BASSETLAW
BLABY
BOLSOVER
BOSWORTH
BEESTON
DAVENTRY
HORNCASTLE
SOUTH EAST DERBYSHI
GAINSBOROUGH
CARLTON
GRANTHAM
HARBOROUGH
HIGH PEAK
HOLLAND WITH BOSTON
KETTERING
MANSFIELD
NEWARK
NORTH EAST DERBYSHI
LOUGHBOROUGH
RUSHCLIFFE
MELTON
BELPER
RUTLAND AND STAMFOR
WELLINGBOROUGH
WEST DERBYSHIRE
CHESTERFIELD
DERBY NORTH
DERBY SOUTH
LEICESTER EAST
LEICESTER SOUTH
LEICESTER WEST
LINCOLN
NORTHAMPTON NORTH
NORTHAMPTON SOUTH
NOTTINGHAM EAST
NOTTINGHAM,NORTH
NOTTINGHAM,WEST
PANo.
333
22
39
76
83
87
51
0
174
317
181
249
131
273
288
312
315
345
0
402
420
180
385
501
403
0
56
503
600
182
141
178
179
364
365
366
374
438
439
444
445
446
Name 1955 to 1970
ILKESTON
ASHFIELD
BASSETLAW
HARBOROUGH
BOLSOVER
BOSWORTH
RUSHCLIFFE
SOUTH NORTHANTS
HORNCASTLE
SOUTH EAST DERBYSHI
GAINSBOROUGH
CARLTON
GRANTHAM
HIGH PEAK
HOLLAND WITH BOSTON
KETTERING
MANSFIELD
NEWARK
NORTH EAST DERBYSHI
LOUGHBOROUGH
NOTTINGHAM,SOUTH
MELTON
BELPER
RUTLAND AND STAMFOR
WELLINGBOROUGH
WEST DERBYSHIRE
CHESTERFIELD
DERBY NORTH
DERBY SOUTH
LEICESTER,NORTH EAS
LEICESTER,SOUTH EAS
LEICESTER,NORTH WES
LINCOLN
NORTHAMPTON
NOTTINGHAM,CENTRAL
NOTTINGHAM,NORTH
NOTTINGHAM,WEST
PANo.
316
21
39
277
83
87
494
0
432
303
175
239
132
265
0
298
301
330
0
396
416
174
379
439
397
0
54
496
592
176
141
172
173
354
356
355
366
431
0
437
438
440
East Anglia
No.
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
Name 1983 to 1987
PANo.
BURY ST. EDMUNDS
108
SUFFOLK CENTRAL
241
GREAT YARMOUTH
648
HUNTINGDON
349
NORFOLK MID
430
CAMBRIDGESHIRE NORTH EAST334
NORFOLK NORTH
428
NORFOLK NORTH WEST
431
CAMBRIDGESHIRE SOUTH EAST116
NORFOLK SOUTH
432
SUFFOLK SOUTH
553
CAMBRIDGESHIRE SOUTH WEST114
NORFOLK SOUTH WEST
433
SUFFOLK COASTAL
555
WAVENEY
385
CAMBRIDGE
115
IPSWICH
113
NORWICH NORTH
438
NORWICH SOUTH
439
PETERBOROUGH
289
Name 1974 to 1979
BURY ST.EDMUNDS
EYE
YARMOUTH
HUNTINGDONSHIRE
THE ISLE OF ELY
NORTH NORFOLK
NORFOLK NW
CAMBRIDGESHIRE
SOUTH NORFOLK
SUDBURY AND WOODBRI
SOUTH WEST NORFOLK
LOWESTOFT
CAMBRIDGE
IPSWICH
NORWICH,NORTH
NORWICH,SOUTH
PETERBOROUGH
PANo.
117
232
633
329
0
337
433
434
122
435
556
0
436
0
387
121
336
441
442
464
Name 1955 to 1970
BURY ST.EDMUNDS
EYE
YARMOUTH
HUNTINGDONSHIRE
THE ISLE OF ELY
NORTH NORFOLK
KINGS LYNN
CAMBRIDGESHIRE
SOUTH NORFOLK
SUDBURY AND WOODBRI
SOUTH WEST NORFOLK
LOWESTOFT
CAMBRIDGE
IPSWICH
NORWICH,NORTH
NORWICH,SOUTH
PETERBOROUGH
PANo.
117
225
628
312
0
320
427
333
124
428
551
0
429
0
381
123
319
435
436
458
Greater London
No.
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
Name 1983 to 1987
BARKING
BATTERSEA
BECKENHAM
BETHNAL GREEN & STEPNEY
BEXLEYHEATH
BOW & POPLAR
BRENT EAST
BRENT NORTH
BRENT SOUTH
BRENTFORD & ISLEWORTH
CARSHALTON & WALLINGTON
CHELSEA
CHINGFORD
CHIPPING BARNET
CHISLEHURST
CROYDON CENTRAL
CROYDON NORTH EAST
CROYDON NORTH WEST
CROYDON SOUTH
DAGENHAM
DULWICH
EALING ACTON
EALING NORTH
EALING SOUTHALL
EDMONTON
ELTHAM
ENFIELD NORTH
ENFIELD SOUTHGATE
ERITH & CRAYFORD
PANo.
22
32
34
44
47
539
85
86
87
88
128
131
623
139
140
166
167
168
173
174
147
210
213
521
231
637
233
526
237
Name 1974 to 1979
BARKING
BATTERSEA,NORTH
BECKENHAM
BETHNAL GREEN
BEXLEY HEATH
STEPNEY
BRENT EAST
BRENT NORTH
BRENT SOUTH
BRENTFORD ISLEWO
CARSHOLTEN
CHELSEA
CHINGFORD
CHIPPING BARNET
CHISLEHURST
CROYDON CENTRAL
CROYDON,NORTH EAST
CROYDON,NORTH WEST
CROYDON,SOUTH
DAGENHAM
CAMBERWELL,DULWICH
ACTON
EALING,NORTH
SOUTHALL
EDMONTON
WOOLWICH,WEST
ENFIELD NORTH
SOUTHGATE
ERITH AND CRAYFORD
PANo.
32
42
46
60
61
542
96
97
98
99
133
136
144
146
147
166
167
168
169
170
198
10
208
529
223
625
224
534
227
Name 1955 to 1970
BARKING
BATTERSEA,NORTH
BECKENHAM
BETHNAL GREEN
BEXLEY
STEPNEY
WILLESDEN,EAST
WEMBLEY,NORTH
WILLESDEN,WEST
BRENTFORD AND CHISW
CARSHALTON
CHELSEA
EPPING
BARNET
CROYDON,SOUTH
CROYDON,NORTH EAST
CROYDON,NORTH WEST
EAST SURREY
DAGENHAM
CAMBERWELL,DULWICH
ACTON
EALING,NORTH
SOUTHALL
EDMONTON
WOOLWICH,WEST
ENFIELD,WEST
SOUTHGATE
ERITH AND CRAYFORD
PANo.
31
42
45
58
59
537
608
594
609
96
134
137
218
33
0
164
162
163
555
165
121
10
198
523
215
620
217
529
220
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
FELTHAM & HESTON
205
FINCHLEY
7
FULHAM
646
GREENWICH
277
HACKNEY NORTH & STOKE NEW283
HACKNEY SOUTH & SHORED’H 284
HAMMERSMITH
291
HAMPSTEAD & HIGHGATE
292
HARROW EAST
296
HARROW WEST
294
HAYES & HARLINGTON
302
HENDON NORTH
305
HENDON SOUTH
306
HOLBORN & ST PANCRAS
317
HORNCHURCH
320
HORNSEY & WOOD GREEN
162
ILFORD NORTH
331
ILFORD SOUTH
8
ISLINGTON NORTH
336
ISLINGTON SOUTH & FINSBUR 338
KENSINGTON
155
KINGSTON UPON THAMES
346
LEWISHAM DEPTFORD
180
LEWISHAM EAST
369
LEWISHAM WEST
370
LEYTON
532
MITCHAM & MORDEN
408
NEWHAM NORTH EAST
423
NEWHAM NORTH WEST
424
NEWHAM SOUTH
425
NORWOOD
440
OLD BEXLEY & SIDCUP
517
ORPINGTON
516
PECKHAM
455
PUTNEY
471
RAVENSBOURNE
473
RICHMOND & BARNES
480
ROMFORD
485
RUISLIP - NORTHWOOD
605
SOUTHWARK & BERMONDSEY 529
STREATHAM
550
SURBITON
558
SUTTON & CHEAM
563
CITY OF LONDON & WESTMIN 353
TOOTING
577
TOTTENHAM
580
TWICKENHAM
585
UPMINSTER
589
UXBRIDGE
591
VAUXHALL
592
WALTHAMSTOW
600
WANSTEAD & WOODFORD
602
WESTMINSTER NORTH
452
WIMBLEDON
290
WOOLWICH
230
FELTHAM HESTON
FINCHLEY
FULHAM
GREENWICH
STOKE NEWINGTON AND
HACKNEY SOUTH
HAMMERSMITH NORTH
HAMPSTEAD
HARROW,EAST
HARROW,WEST
HAYES AND HARLINGTO
HENDON,NORTH
HENDON,SOUTH
HOLBORN AND ST.PANC
HORNCHURCH
HORNSEY
ILFORD,NORTH
ILFORD,SOUTH
ISLINGTON,NORTH
ISLINGTON SOUTH
KENSINGTON
KINGSTON UPON THAME
DEPTFORD
LEWISHAM EAST
LEWISHAM,WEST
LEYTON
MITCHAM
NEWHAM NORTH EAST
WEST HAM NORTH
NEWHAM SOUTH
LAMBETH,NORWOOD
SIDCUP
ORPINGTON
CAMBERWELL,PECKHAM
WANDSWORTH,PUTNEY
RAVENSBOURNE
RICHMOND%SURREY;
ROMFORD
RUISLIP-NORTHWOOD
BERMONDSEY
WANDSWORTH,STREATHA
SURBITON
SUTTON AND CHEAM
CITIES OF LONDON AN
TOOTING
TOTTENHAM
TWICKENHAM
UPMINSTER
UXBRIDGE
LAMBETH,VAUXHALL
WALTHAMSTOW
WANSTEAD AND WOODFO
PADDINGTON
WIMBLEDON
WOOLWICH,EAST
238
242
246
276
280
281
286
287
292
293
298
302
303
314
318
319
331
332
340
341
344
348
177
370
371
372
410
428
429
430
443
525
453
459
478
479
487
492
499
57
553
559
563
151
576
579
582
585
586
587
593
594
457
614
624
FELTHAM
FINCHLEY
FULHAM
GREENWICH
STOKE NEWINGTON AND
SHOREDITCH AND FINS
HAMMERSMITH NORTH
HAMPSTEAD
HARROW,EAST
HARROW,WEST
HAYES AND HARLINGTO
HENDON,NORTH
HENDON,SOUTH
HOLBORN AND ST.PANC
HORNCHURCH
HORNSEY
ILFORD,NORTH
ILFORD,SOUTH
ISLINGTON,NORTH
ISLINGTON,SOUTH WES
KENSINGTON,NORTH
KINGSTON UPON THAME
DEPTFORD
LEWISHAM,NORTH
LEWISHAM,WEST
LEYTON
MITCHAM
EAST HAM,NORTH
WEST HAM NORTH
WEST HAM SOUTH
LAMBETH,NORWOOD
CHISLEHURST
ORPINGTON
CAMBERWELL,PECKHAM
WANDSWORTH,PUTNEY
BROMLEY
RICHMOND%SURREY;
ROMFORD
RUISLIP-NORTHWOOD
BERMONDSEY
WANDSWORTH,STREATHA
SURBITON
SUTTON AND CHEAM
CITIES OF LONDON AN
WANDSWORTH,CENTRAL
TOTTENHAM
TWICKENHAM
UXBRIDGE
LAMBETH,VAUXHALL
WALTHAMSTOW,WEST
WANSTEAD AND WOODFO
PADDINGTON,NORTH
WIMBLEDON
WOOLWICH,EAST
230
234
238
268
544
517
275
276
280
281
285
288
289
300
304
305
314
315
324
325
328
337
171
361
363
364
406
203
599
600
342
146
448
122
585
110
479
484
492
55
586
554
556
377
583
570
572
0
575
343
582
587
451
610
619
Rest of South East
No.
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
Name 1983 to 1987
PANo.
AYLESBURY
18
BANBURY
20
BEACONSFIELD
33
BILLERICAY
48
BRAINTREE
83
BRENTWOOD & ONGAR
90
BUCKINGHAM
403
CHELMSFORD
130
CHESHAM & AMERSHAM
134
EPPING FOREST
234
HARLOW
295
HARWICH
299
HENLEY
303
HERTFORD & STORTFORD
100
HERTSMERE
310
BEDFORDSHIRE MID
35
MILTON KEYNES
103
BEDFORDSHIRE NORTH
36
COLCHESTER NORTH
151
HERTFORDSHIRE NORTH
316
LUTON NORTH
388
OXFORD WEST & ABINGDON
451
ROCHFORD
393
SAFFRON WALDEN
500
ST ALBANS
498
COLCHESTER SOUTH & MALDON149
BEDFORDSHIRE SOUTH WEST 37
HERTFORDSHIRE SOUTH WEST 313
STEVENAGE
308
WANTAGE
601
WELWYN HATFIELD
615
HERTFORDSHIRE WEST
24
WITNEY
619
WYCOMBE
278
BASILDON
27
BROXBOURNE
312
CASTLE POINT
518
LUTON SOUTH
386
OXFORD EAST
629
SOUTHEND EAST
524
SOUTHEND WEST
232
THURROCK
574
WATFORD
611
ALDERSHOT
4
ARUNDEL
14
ASHFORD
16
BASINGSTOKE
28
BEXHILL & BATTLE
495
CANTERBURY
118
CHICHESTER
137
DARTFORD
176
DOVER
198
BERKSHIRE EAST
632
HAMPSHIRE EAST
461
SURREY EAST
559
FAREHAM
560
FAVERSHAM
244
FOLKESTONE & HYTHE
251
Name 1974 to 1979
AYLESBURY
BANBURY
BEACONSFIELD
BRAINTREE
BRENTWOOD
CHELMSFORD
CHESHAM
EPPING
HARLOW
HARWICH
HENLEY
HERTFORDSHIRE S
MID-BEDFORDSHIRE
BUCKINGHAM
BEDFORD
COLCHESTER
HITCHEN
LUTON WEST
MALDON
SAFFRON WALDON
ST.ALBANS
SOUTH BEDFORDSHIRE
SOUTH WEST HERTFORD
HERTFORD STEVEN
ABINGDON
WELWYN
HEMEL HEMPSTEAD
OXON MD
WYCOMBE
BASILDON
EAST HERTFORDSHIRE
SOUTH EAST ESSEX
LUTON EAST
OXFORD
SOUTHEND EAST
SOUTHEND WEST
THURROCK
WATFORD
ALDERSHOT
ARUNDEL
ASHFORD
BASINGSTOKE
RYE
CANTERBURY
CHICHESTER
DARTFORD
DOVER
WOKINGHAM
PETERSFIELD
SURREY EAST
FAREHAM
FAVERSHAM
FOLKESTONE AND HYTH
PANo.
25
30
44
0
94
100
0
135
139
225
289
295
304
0
308
48
113
47
155
313
390
0
393
505
506
0
49
309
306
8
602
300
456
632
37
307
229
389
455
532
533
573
599
11
21
23
38
504
124
143
172
193
619
465
560
234
237
245
Name 1955 to 1970
AYLESBURY
BANBURY
SOUTH BUCKINGHAMSHI
MALDON
CHELMSFORD
CHIGWELL
HARWICH
HENLEY
MID-BEDFORDSHIRE
BUCKINGHAM
BEDFORD
COLCHESTER
HITCHEN
SAFFRON WALDON
ST.ALBANS
SOUTH BEDFORDSHIRE
SOUTH WEST HERTFORD
ABINGDON
HERTFORD
HEMEL HEMPSTEAD
WYCOMBE
BILLERICAY
EAST HERTFORDSHIRE
SOUTH EAST ESSEX
LUTON
OXFORD
SOUTHEND EAST
SOUTHEND WEST
THURROCK
WATFORD
ALDERSHOT
ASHFORD
BASINGSTOKE
RYE
CANTERBURY
CHICHESTER
DARTFORD
DOVER
WOKINGHAM
PETERSFIELD
GOSPORT AND FAREHAM
FAVERSHAM
FOLKESTONE AND HYTH
PANo.
24
29
113
0
386
0
0
136
0
144
0
283
290
0
0
47
112
46
152
299
0
0
0
498
499
0
48
294
0
8
292
286
0
627
60
293
222
383
450
526
527
564
590
11
0
22
38
497
126
143
167
186
615
459
0
263
229
237
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
GRAVESHAM
GUILDFORD
HASTINGS & RYE
HORSHAM
ISLE OF WIGHT
LEWES
MAIDSTONE
MEDWAY
KENT MID
SUSSEX MID
MOLE VALLEY
NEWBURY
NEW FOREST
THANET NORTH
HAMPSHIRE NORTH WEST
SURREY NORTH WEST
READING EAST
READING WEST
ROMSEY & WATERSIDE
SEVENOAKS
SHOREHAM
THANET SOUTH
SURREY SOUTH WEST
TONBRIDGE & MALLING
TUNBRIDGE WELLS
WEALDEN
WINCHESTER
WINDSOR & MAIDENHEAD
WOKING
WOKINGHAM
BRIGHTON KEMPTOWN
BRIGHTON PAVILION
CHERTSY & WALTON
CRAWLEY
EASTBOURNE
EASTLEIGH
EPSOM & EWELL
ESHER
GILLINGHAM
GOSPORT
HAVANT
HOVE
PORTSMOUTH NORTH
PORTSMOUTH SOUTH
REIGATE
SLOUGH
SOUTHAMPTON ITCHEN
SOUTHAMPTON TEST
SPELTHORNE
WORTHING
279
282
300
322
335
368
482
341
483
562
192
419
484
494
625
561
474
631
512
506
515
571
641
576
583
215
626
628
42
472
94
96
133
321
216
66
236
126
259
274
301
73
467
468
476
238
522
523
496
643
GRAVESEND
GUILDFORD
HASTINGS
ISLE OF WIGHT
LEWES
MAIDSTONE
ROCHESTER AND CHATH
SUSSEX MID
DORKING
NEWBURY
THANET WEST
SURREY NW
READING SOUTH
READING NORTH
NEW FOREST
SEVENOAKS
SHOREHAM
THANET EAST
FARNHAM
TONBRIDGE
TUNBRIDGE WELLS
EAST GRINSTEAD
WINCHESTER
WINDSOR
WOKING
BRIGHTON,KEMPTOWN
BRIGHTON,PAVILION
CHERTSEY
HORSHAM
EASTBOURNE
EASTLEIGH
EPSOM
ESHER
GILLINGHAM
GOSPORT
HAVANT
HOVE
PORTSMOUTH NORTH
PORTSMOUTH,SOUTH
REIGATE
ETON AND SLOUGH
SOUTHAMPTON,ITCHEN
SOUTHAMPTON,TEST
SPELTHORNE
WORTHING
274
278
296
0
338
369
392
491
0
562
189
421
0
570
0
561
481
480
427
515
523
569
235
575
581
211
615
616
618
0
105
106
138
320
210
213
226
228
253
271
297
322
473
474
483
230
530
531
538
629
GRAVESEND
GUILDFORD
HASTINGS
ISLE OF WIGHT
LEWES
MAIDSTONE
ROCHESTER AND CHATH
DORKING
NEWBURY
READING
NEW FOREST
SEVENOAKS
ARUNDEL AND SHOREHA
ISLE OF THANET
FARNHAM
TONBRIDGE
EAST GRINSTEAD
WINCHESTER
WINDSOR
WOKING
BRIGHTON,KEMPTOWN
BRIGHTON,PAVILION
CHERTSEY
HORSHAM
EASTBOURNE
EASTLEIGH
EPSOM
ESHER
GILLINGHAM
PORTSMOUTH,LANGSTON
HOVE
PORTSMOUTH,WEST
PORTSMOUTH,SOUTH
REIGATE
ETON AND SLOUGH
SOUTHAMPTON,ITCHEN
SOUTHAMPTON,TEST
SPELTHORNE
WORTHING
266
270
284
0
322
360
385
483
0
0
182
417
0
0
0
0
0
473
423
509
20
321
227
0
566
202
611
612
614
0
102
103
139
306
201
205
219
221
243
0
467
308
469
468
474
223
524
525
534
624
South West
No.
345
346
347
348
349
350
Name 1983 to 1987
PANo.
BRIDGWATER
CHRISTCHURCH
CIRENCESTER & TEWKESBURY
DEVIZES
FALMOUTH & CAMBORNE
HONITON
91
142
143
186
242
372
Name 1974 to 1979
BRIDGWATER
CHRISTCHURCH
CIRENCESTER AND TEW
DEVIZES
FALMOUTH AND CAMBOR
HONITON
PANo.
101
149
150
183
233
316
Name 1955 to 1970
BRIDGWATER
CIRENCESTER AND TEW
DEVIZES
FALMOUTH AND CAMBOR
HONITON
PANo.
97
0
148
177
226
302
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
NORTHAVON
CORNWALL NORTH
DEVON NORTH
DORSET NORTH
WILTSHIRE NORTH
ST IVES
SALISBURY
SOMERTON & FROME
DORSET SOUTH
CORNWALL SOUTH EAST
SOUTH HAMS
STROUD
TAUNTON
TEIGNBRIDGE
TIVERTON
DEVON WEST & TORRIDGE
TRURO
WANSDYKE
WELLS
WESTBURY
DORSET WEST
GLOUCESTERSHIRE WEST
WESTON-SUPER-MARE
WOODSPRING
YEOVIL
BATH
BOURNEMOUTH EAST
BOURNEMOUTH WEST
BRISTOL EAST
BRISTOL NORTH WEST
BRISTOL SOUTH
BRISTOL WEST
CHELTENHAM
EXETER
GLOUCESTER
KINGSWOOD
PLYMOUTH DEVONPORT
PLYMOUTH DRAKE
PLYMOUTH SUTTON
POOLE
SWINDON
TORBAY
271
158
187
193
138
531
502
612
194
69
570
554
572
579
575
188
581
528
613
618
195
273
154
599
650
30
77
79
98
97
95
638
132
552
436
347
462
460
463
466
568
525
SOUTH GLOUCESTERSHI
NORTH CORNWALL
NORTH DEVON
NORTH DORSET
CHIPPENHAM
ST.IVES
SALISBURY
WELLS
SOUTH DORSET
BODMIN
STROUD
TAUNTON
TOTNES
TIVERTON
DEVON WEST
TRURO
WESTBURY
WEST DORSET
WEST GLOUCESTERSHIR
WESTON SUPER MARE
NORTH SOMERSET
YEOVIL
BATH
BOURNEMOUTH EAST AN
BOURNEMOUTH WEST
BRISTOL,SOUTH EAST
BRISTOL,NORTH WEST
BRISTOL,SOUTH
BRISTOL,WEST
CHELTENHAM
EXETER
GLOUCESTER
KINGSWOOD
PLYMOUTH,DEVONPORT
PLYMOUTH DRAKE
PLYMOUTH,SUTTON
POOLE
SWINDON
TORQUAY
268
159
184
190
145
508
513
601
191
82
0
555
568
578
574
185
580
0
0
605
192
269
610
528
634
40
89
90
110
108
109
111
137
231
267
349
466
467
468
472
567
577
SOUTH GLOUCESTERSHI
NORTH CORNWALL
NORTH DEVON
NORTH DORSET
CHIPPENHAM
ST.IVES
SALISBURY
WELLS
SOUTH DORSET
BODMIN
STROUD
TAUNTON
TOTNES
TIVERTON
TAVISTOCK
TRURO
WESTBURY
WEST DORSET
WEST GLOUCESTERSHIR
WESTON SUPER MARE
NORTH SOMERSET
YEOVIL
BATH
BOURNEMOUTH EAST AN
BOURNEMOUTH WEST
BRISTOL,SOUTH EAST
BRISTOL,NORTH WEST
BRISTOL,SOUTH
BRISTOL,WEST
CHELTENHAM
EXETER
GLOUCESTER
PLYMOUTH,DEVONPORT
PLYMOUTH,SUTTON
POOLE
SWINDON
TORQUAY
260
156
178
183
145
501
506
593
184
82
0
550
561
569
565
562
571
0
0
597
185
261
604
522
629
40
89
90
108
106
107
109
138
224
259
0
460
461
0
465
560
567
West Midlands
No.
393
394
395
396
397
398
399
400
401
402
403
404
405
406
Name 1983 to 1987
BROMSGROVE
BURTON
CANNOCK & BURNTWOOD
HEREFORD
LEOMINSTER
LUDLOW
MERIDEN
STAFFORDSHIRE MID
WORCESTERSHIRE MID
SHROPSHIRE NORTH
WARWICKSHIRE NORTH
NUNEATON
RUGBY & KENILWORTH
SHREWSBURY & ATCHAM
PANo.
102
105
117
538
366
387
607
535
99
450
398
89
491
445
Name 1974 to 1979
BROMSGROVE
BURTON
CANNOCK
HEREFORD
LEOMINSTER
LUDLOW
MERIDEN
OSWESTRY
NUNEATON
RUGBY
SHREWSBURY
PANo.
112
115
123
305
368
388
404
0
0
454
0
447
498
524
Name 1955 to 1970
BROMSGROVE
BURTON
CANNOCK
HEREFORD
LEOMINSTER
LUDLOW
MERIDEN
OSWESTRY
NUNEATON
RUGBY
SHREWSBURY
PANo.
111
115
125
291
359
382
398
0
0
449
0
441
491
518
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
STAFFORDSHIRE SOUTH EAST 371
STAFFORDSHIRE SOUTH
536
WORCESTERSHIRE SOUTH
640
STAFFORD
534
STAFFORDSHIRE MOORLANDS 360
STRATFORD-ON-AVON
549
WARWICK & LEAMINGTON
608
WYRE FOREST
343
ALDRIDGE-BROWNHILLS
5
BIRMINGHAM EDGBASTON
50
BIRMINGHAM ERDINGTON
51
BIRMINGHAM HALL GREEN
52
BIRMINGHAM HODGE HILL
53
BIRMINGHAM LADYWOOD
54
BIRMINGHAM NORTHFIELD
55
BIRMINGHAM PERRY BARR
56
BIRMINGHAM SELLY OAK
57
BIRMINGHAM SMALL HEATH
58
BIRMINGHAM SPARKBROOK
59
BIRMINGHAM YARDLEY
60
COVENTRY NORTH EAST
159
COVENTRY NORTH WEST
160
COVENTRY SOUTH EAST
161
COVENTRY SOUTH WEST
152
DUDLEY EAST
199
DUDLEY WEST
200
HALESOWEN & STOURBRIDGE 285
NEWCASTLE UNDER LYME
420
SOLIHULL
635
STOKE-ON-TRENT CENTRAL
544
STOKE-ON-TRENT NORTH
545
STOKE-ON-TRENT SOUTH
547
SUTTON COLDFIELD
564
WREKIN THE
644
WALSALL NORTH
596
WALSALL SOUTH
597
WARLEY EAST
603
WARLEY WEST
604
WEST BROMWICH EAST
616
WEST BROMWICH WEST
373
WOLVERHAMPTON NORTH EAST633
WOLVERHAMPTON SOUTH EAST634
WOLVERHAMPTON SOUTH WEST636
WORCESTER
639
LICHFIELD AND TAMWO
STAFFORDSHIRE SW
SOUTH WORCESTERSHIR
STAFFORD AND STONE
LEEK
STRATFORD
WARWICK AND LEAMING
KIDDERMINSTER
ALDRIDGE BROWNHILL
BIRMINGHAM,EDGBASTO
BIRMINGHAM ERDIN
BIRMINGHAM,HALL GRE
BIRMINGHAM,STETCHFO
BIRMINGHAM,LADYWOOD
BIRMINGHAM,NORTHFIE
BIRMINGHAM,PERRY BA
BIRMINGHAM,SELLY OA
BIRMINGHAM,SMALL HE
BIRMINGHAM,SPARKBRO
BIRMINGHAM,YARDLEY
COVENTRY NE
COVENTRY NW
COVENTRY SE
COVENTRY SW
DUDLEY EAST
DUDLEY WEST
HALESOWEN STOURB
NEWCASTLE UNDER LYM
SOLIHULL
STOKE ON TRENT,CENT
STOKE ON TRENT,NORT
STOKE ON TRENT,SOUT
SUTTON COLDFIELD
THE WREKIN
WALSALL,NORTH
WALSALL,SOUTH
WARLEY EAST
WARLEY WEST
WEST BROMWICH E
WEST BROMWICH W
WOLVERHAMPTON N.E.
WOLVERHAMPTON SE
WOLVERHAMPTON S.W.
WORCESTER
373
540
627
539
363
552
598
346
12
63
64
65
73
67
68
69
70
71
72
74
160
161
162
163
196
197
282
422
527
549
550
551
564
630
591
592
595
596
603
604
620
621
622
626
LICHFIELD AND TAMWO
SOUTH WORCESTERSHIR
STAFFORD AND STONE
LEEK
STRATFORD
WARWICK AND LEAMING
KIDDERMINSTER
BIRMINGHAM,EDGBASTO
BIRMINGHAM,ASTON
BIRMINGHAM,HALL GRE
BIRMINGHAM,STETCHFO
BIRMINGHAM,LADYWOOD
BIRMINGHAM,NORTHFIE
BIRMINGHAM,PERRY BA
BIRMINGHAM,SELLY OA
BIRMINGHAM,SMALL HE
BIRMINGHAM,SPARKBRO
BIRMINGHAM,YARDLEY
COVENTRY,EAST
COVENTRY,NORTH
COVENTRY,SOUTH
DUDLEY
BRIERLEY HILL
OLDBURY AND HALESOW
NEWCASTLE UNDER LYM
SOLIHULL
STOKE ON TRENT,CENT
STOKE ON TRENT,NORT
STOKE ON TRENT,SOUT
SUTTON COLDFIELD
THE WREKIN
WALSALL,NORTH
WALSALL,SOUTH
SMETHWICK
ROWLEY REGIS AND TI
WEST BROMWICH
WOLVERHAMPTON N.E.
BILSTON
WOLVERHAMPTON S.W.
WORCESTER
365
0
622
535
353
548
589
331
0
65
64
66
74
68
69
70
71
72
73
75
157
158
0
159
189
99
443
418
521
545
546
547
557
625
579
580
520
489
596
0
616
61
617
621
North West
No.
451
452
453
454
455
456
457
458
459
460
461
462
463
Name 1983 to 1987
BOLTON WEST
CHORLEY
CHESTER CITY OF
CONGLETON
CREWE & NANTWICH
EDDISBURY
ELLESMERE PORT & NESTON
FYLDE
HALTON
HAZEL GROVE
HEYWOOD & MIDDLETON
KNOWSLEY NORTH
KNOWSLEY SOUTH
PANo.
Name 1974 to 1979
PANo.
Name 1955 to 1970
PANo.
74
141
208
163
164
437
228
253
622
311
106
351
330
WESTHOUGHTON
CHORLEY
CITY OF CHESTER
CREWE
NANTWICH
NORTHWICH
SOUTH FYLDE
WIDNES
HAZEL GROVE
HUYTON
607
148
140
164
417
440
0
248
612
299
0
0
330
WESTHOUGHTON
CHORLEY
CITY OF CHESTER
CREWE
NANTWICH
NORTHWICH
SOUTH FYLDE
WIDNES
CHEADLE
HUYTON
601
147
140
160
413
434
0
528
606
135
0
0
313
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
LANCASTER
355
LITTLEBOROUGH & SADDLEWO’ 315
MACCLESFIELD
389
MAKERFIELD
332
MORECOMBE & LUNESDALE
598
RIBBLE VALLEY
407
SOUTH RIBBLE
252
STALYBRIDGE & HYDE
78
TATTON
220
LANCASHIRE WEST
449
WIRRAL SOUTH
627
WIRRAL WEST
630
WORSLEY
71
WYRE
527
ALTRINCHAM & SALE
6
ASHTON-UNDER-LYNE
17
BIRKENHEAD
49
BLACKBURN
63
BLACKPOOL NORTH
67
BLACKPOOL SOUTH
64
BOLTON NORTH EAST
72
BOLTON SOUTH EAST
243
BOOTLE
75
BURNLEY
104
BURY NORTH
107
BURY SOUTH
402
CHEADLE
129
CROSBY
165
DAVYHULME
551
DENTON & REDDISH
394
ECCLES
221
HYNDBURN
329
LEIGH
365
LIVERPOOL BROADGREEN
380
LIVERPOOL GARSTON
378
LIVERPOOL MOSSLEY HILL
376
LIVERPOOL RIVERSIDE
379
LIVERPOOL WALTON
375
LIVERPOOL WEST DERBY
382
MANCHESTER BLACKLEY
392
MANCHESTER CENTRAL
179
MANCHESTER GORTON
391
MANCHESTER WITHINGTON
395
MANCHESTER WYTHENSHAWE 396
OLDHAM CENTRAL & ROYTON 447
OLDHAM WEST
448
PENDLE
421
PRESTON
469
ROCHDALE
397
ROSSENDALE & DARWEN
488
ST HELENS NORTH
429
ST HELENS SOUTH
499
SALFORD EAST
501
SOUTHPORT
111
STOCKPORT
541
STRETFORD
177
WALLASEY
594
WARRINGTON NORTH
606
WARRINGTON SOUTH
492
WIGAN
624
LANCASTER
HEYWOOD AND ROYTON
MACCLESFIELD
INCE
MORECAMBE AND LONSD
CLITHEROE
PRESTON,SOUTH
STALYBRIDGE AND HYD
KNUTSFORD
ORMSKIRK
BEBINGTON
WIRRAL
FARNWORTH
NORTH FYLDE
ALTRINCHAM AND SALE
ASHTON UNDER LYNE
BIRKENHEAD
BLACKBURN
BLACKPOOL,SOUTH
BLACKPOOL,NORTH
BOLTON,EAST
BOLTON,WEST
BOOTLE
BURNLEY
BURY AND RADCLIFFE
MIDDLETON AND PREST
CHEADLE
CROSBY
STRETFORD
MANCHESTER,GORTON
ECCLES
ACCRINGTON
LEIGH
LIVERPOOL,WAVERTREE
LIVERPOOL,GARSTON
LIVERPOOL,EDGE HILL
LIVERPOOL,TOXTETH
LIVERPOOL,WALTON
LIVERPOOL,WEST DERB
MANCHESTER,BLACKLEY
MANCHESTER CEN
MANCHESTER,ARDWICK
MANCHESTER,WITHINGT
MANCHESTER,WYTHENSH
OLDHAM,EAST
OLDHAM,WEST
NELSON AND COLNE
PRESTON,NORTH
ROCHDALE
ROSSENDALE
NEWTON
ST.HELENS
SALFORD,EAST
SOUTHPORT
STOCKPORT NORTH
MANCHESTER,MOSS SID
WALLASEY
WARRINGTON
RUNCORN
WIGAN
356
311
391
334
414
153
476
541
352
452
45
617
236
247
13
24
62
77
79
78
84
85
86
114
116
408
134
165
554
397
215
9
367
381
376
375
379
380
382
395
396
394
400
401
449
450
419
475
490
494
432
507
511
535
546
398
589
597
500
613
LANCASTER
HEYWOOD AND ROYTON
MACCLESFIELD
INCE
MORECAMBE AND LONSD
CLITHEROE
PRESTON,SOUTH
STALYBRIDGE AND HYD
KNUTSFORD
ORMSKIRK
BEBINGTON
WIRRAL
FARNWORTH
NORTH FYLDE
ALTRINCHAM AND SALE
ASHTON UNDER LYNE
BIRKENHEAD
BLACKBURN
BLACKPOOL,SOUTH
BLACKPOOL,NORTH
BOLTON,EAST
BOLTON,WEST
BOOTLE
BURNLEY
BURY AND RADCLIFFE
MIDDLETON AND PREST
CROSBY
STRETFORD
MANCHESTER,GORTON
ECCLES
ACCRINGTON
LEIGH
LIVERPOOL,WAVERTREE
LIVERPOOL,GARSTON
LIVERPOOL,EDGE HILL
LIVERPOOL,TOXTETH
LIVERPOOL,WALTON
LIVERPOOL,WEST DERB
MANCHESTER,BLACKLEY
MANCHESTER,CHEETHAM
MANCHESTER,ARDWICK
MANCHESTER,WITHINGT
MANCHESTER,WYTHENSH
OLDHAM,EAST
OLDHAM,WEST
NELSON AND COLNE
PRESTON,NORTH
ROCHDALE
ROSSENDALE
NEWTON
ST.HELENS
SALFORD,EAST
SOUTHPORT
STOCKPORT NORTH
MANCHESTER,MOSS SID
WALLASEY
WARRINGTON
RUNCORN
WIGAN
346
297
384
317
410
150
471
536
340
447
44
613
228
433
12
23
62
77
79
78
84
85
86
114
116
404
0
161
549
391
207
9
358
374
369
367
372
373
375
388
389
387
394
395
444
445
415
470
482
486
425
500
504
530
541
392
577
588
493
607
Wales
No.
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
Name 1983 to 1987
PANo.
ABERAVON
ALYN & DEESIDE
BLAENAU GWENT
BRECON & RADNOR
BRIDGEND
CAERNARFON
CAERPHILLY
CARMARTHEN
CEREDIGION & PEMBROKE N.
CLWYD NORTH WEST
CLWYD SOUTH WEST
CONWY
CYNON VALLEY
DELYN
GOWER
ISLWYN
LLANELLI
MEIRIONNYDD NANT CONWY
MERTHYR TYDFIL & RHYMNEY
MONMOUTH
MONTGOMERY
NEATH
NEWPORT EAST
NEWPORT WEST
OGMORE
PEMBROKE
PONTYPRIDD
RHONDDA
TORFAEN
VALE OF GLAMORGAN
WREXHAM
YNYS MON
CARDIFF CENTRAL
CARDIFF NORTH
CARDIFF SOUTH & PENARTH
CARDIFF WEST
SWANSEA EAST
SWANSEA WEST
1
178
219
84
444
109
110
125
122
367
145
156
172
249
275
337
363
400
401
409
410
456
426
497
446
511
465
479
464
590
645
649
119
120
121
127
565
566
Name 1974 to 1979
ABERAVON
EAST FLINT
EBBW VALE
BRECON AND RADNOR
CAERNARVON
CAERPHILLY
CARMARTHEN
CARDIGAN
DENBIGH
CONWAY
ABERDARE
WEST FLINT
GOWER
BEDWELLTY
LLANELLY
MERIONETH
MERTHYR TYDFIL
MONMOUTH
MONTGOMERY
NEATH
NEWPORT
OGMORE
PEMBROKE
PONTYPRIDD
RHONDDA
PONTYPOOL
BARRY
WREXHAM
ANGLESEY
CARDIFF,NORTH
CARDIFF NW
CARDIFF,SOUTH EAST
CARDIFF,WEST
SWANSEA,EAST
SWANSEA,WEST
PANo.
1
243
214
95
0
118
119
132
129
176
0
158
2
244
272
50
383
405
406
411
412
418
431
0
448
460
471
486
470
36
631
14
125
126
127
128
565
566
Name 1955 to 1970
ABERAVON
EAST FLINT
EBBW VALE
BRECON AND RADNOR
CAERNARVON
CAERPHILLY
CARMARTHEN
CARDIGAN
DENBIGH
CONWAY
ABERDARE
WEST FLINT
GOWER
BEDWELLTY
LLANELLY
MERIONETH
MERTHYR TYDFIL
MONMOUTH
MONTGOMERY
NEATH
NEWPORT
OGMORE
PEMBROKE
PONTYPRIDD
RHONDDA,EAST
PONTYPOOL
BARRY
WREXHAM
ANGLESEY
CARDIFF,NORTH
CARDIFF,SOUTH EAST
CARDIFF,WEST
SWANSEA,EAST
SWANSEA,WEST
PANo.
1
235
206
95
0
118
119
133
130
170
0
155
2
236
264
49
376
399
400
407
408
414
424
0
442
454
464
477
463
37
626
13
127
0
128
129
558
559
Scotland
No.Name 1983 to 1987PANo.Name 1974 to 1979
562
563
564
565
566
567
568
569
570
571
572
ANGUS EAST
9
ARGYLL & BUTE
13
AYR
19
BANFF & BUCHAN
21
CAITHNESS & SUTHERLAND
112
CARRICK CUMNOCK & DOON VA124
FIFE CENTRAL
247
CLACKMANNAN
540
CLYDEBANK & MILNGAVIE
169
CLYDESDALE
352
CUMBERNAULD & KILSYTH
201
PANo.
Name 1955 to 1970
SOUTH ANGUS
ARGYLL
AYR
EAST ABERDEENSHIRE
CAITHNESS AND SUTHE
SOUTH AYRSHIRE
FIFE CENTRAL
CLACKMANNAN AND EAS
DUNBARTONSHIRE CEN
LANARK
-
16
19
26
5
120
29
240
544
200
354
0
PANo.
SOUTH ANGUS
ARGYLL
AYR
EAST ABERDEENSHIRE
CAITHNESS AND SUTHE
SOUTH AYRSHIRE
WEST FIFE
CLACKMANNAN AND EAS
-
15
18
25
5
120
28
233
539
0
0
0
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
CUNNINGHAME NORTH
170
CUNNINGHAME SOUTH
171
DUMBARTON
202
DUMFRIES
548
DUNFERMLINE EAST
246
DUNFERMLINE WEST
503
EAST KILBRIDE
214
EAST LOTHIAN
217
EASTWOOD
477
FALKIRK EAST
240
FALKIRK WEST
144
GALLOWAY & UPPER NITHSDAL 255
GORDON
272
INVERNESS NAIRN & LOCHABE 333
KILMARNOCK & LOUDOUN
345
KINCARDINE & DEESIDE
344
KIRKCALDY
148
LINLITHGOW
617
LIVINGSTON
404
MIDLOTHIAN
381
MORAY
411
FIFE NORTH EAST
248
TAYSIDE NORTH
459
ORKNEY & SHETLAND
348
PERTH & KINROSS
569
RENFREW WEST & INVERCLYDE218
ROSS CROMARTY AND SKYE
486
ROXBURGH & BERWICKSHIRE 582
STIRLING
239
STRATHKELVIN & BEARSDEN
203
TWEEDDALE ETTRICK & LAUDE 490
WESTERN ISLES
620
ABERDEEN NORTH
2
ABERDEEN SOUTH
3
DUNDEE EAST
204
DUNDEE WEST
206
EDINBURGH CENTRAL
222
EDINBURGH EAST
223
EDINBURGH LEITH
224
EDINBURGH PENTLANDS
225
EDINBURGH SOUTH
226
EDINBURGH WEST
227
GLASGOW CATHCART
262
GLASGOW CENTRAL
268
GLASGOW GARSCADDEN
263
GLASGOW GOVAN
261
GLASGOW HILLHEAD
264
GLASGOW MARYHILL
265
GLASGOW POLLOK
266
GLASGOW PROVAN
260
GLASGOW RUTHERGLEN
537
GLASGOW SHETTLESTON
269
GLASGOW SPRINGBURN
270
GREENOCK & PORT GLASGOW 280
HAMILTON
288
MONKLANDS EAST
150
MONKLANDS WEST
406
MOTHERWELL NORTH
76
MOTHERWELL SOUTH
414
PAISLEY NORTH
453
PAISLEY SOUTH
454
BUTE AND NORTH AYRS
AYR CENTRAL
WEST DUNBARTONSHIRE
DUMFRIES
DUNFERMLINE BURGHS
EAST KILBRIDE
BERWICK AND EAST LO
EAST RENFREWSHIRE
STIRLING AND FALKIR
GALLOWAY
WEST ABERDEENSHIRE
INVERNESS
KILMARNOCK
NORTH ANGUS AND MEA
KIRKCALDY BURGHS
WEST LOTHIAN
MIDLOTHIAN
MORAY AND NAIRN
EAST FIFE
KINROSS AND WEST PE
ORKNEY AND ZETLAND
PERTH AND EAST PERT
WEST RENFREWSHIRE
ROSS AND CROMARTY
WEST STIRLINGSHIRE
EAST DUNBARTONSHIRE
ROXBURGH, SELKIRK A
WESTERN ISLES
ABERDEEN NORTH
ABERDEEN SOUTH
DUNDEE EAST
DUNDEE WEST
EDINBURGH CENTRAL
EDINBURGH EAST
EDINBURGH LEITH
EDINBURGH PENTLANDS
EDINBURGH SOUTH
EDINBURGH WEST
GLASGOW CATHCART
GLASGOW QUEENS P
GLASGOW GARSCADD
GLASGOW GOVAN
GLASGOW HILLHEAD
GLASGOW MARYHILL
GLASGOW POLLOK
GLASGOW PROVAN
RUTHERGLEN
GLASGOW SHETTLESTON
GLASGOW SPRINGBURN
GREENOCK
HAMILTON
COATBRIDGE AND AIRD
NORTH LANARKSHIRE
BOTHWELL
MOTHERWELL
PAISLEY
28
27
202
199
0
205
212
58
484
0
543
250
6
335
347
15
351
608
0
409
413
241
350
451
463
485
493
0
545
201
497
606
3
4
203
204
216
217
218
220
221
222
254
264
257
258
259
261
262
263
502
265
266
275
285
154
355
88
416
0
458
BUTE AND NORTH AYRS
CENTRAL AYRSHIRE
WEST DUNBARTONSHIRE
DUMFRIES
DUNFERMLINE BURGHS
LANARK
BERWICK AND EAST LO
EAST RENFREWSHIRE
STIRLING AND FALKIR
GALLOWAY
WEST ABERDEENSHIRE
INVERNESS
KILMARNOCK
NORTH ANGUS AND MEA
KIRKCALDY BURGHS
WEST LOTHIAN
MIDLOTHIAN
MORAY AND NAIRN
EAST FIFE
KINROSS AND WEST PE
ORKNEY AND ZETLAND
PERTH AND EAST PERT
WEST RENFREWSHIRE
ROSS AND CROMARTY
WEST STIRLINGSHIRE
EAST DUNBARTONSHIRE
ROXBURGH, SELKIRK A
WESTERN ISLES
ABERDEEN NORTH
ABERDEEN SOUTH
DUNDEE EAST
DUNDEE WEST
EDINBURGH CENTRAL
EDINBURGH EAST
EDINBURGH LEITH
EDINBURGH PENTLANDS
EDINBURGH SOUTH
EDINBURGH WEST
GLASGOW CATHCART
GLASGOW GORBALS
GLASGOW SCOTSTOUN
GLASGOW GOVAN
GLASGOW HILLHEAD
GLASGOW MARYHILL
GLASGOW POLLOK
GLASGOW PROVAN
RUTHERGLEN
GLASGOW SHETTLESTON
GLASGOW SPRINGBURN
GREENOCK
HAMILTON
COATBRIDGE AND AIRD
NORTH LANARKSHIRE
BOTHWELL
MOTHERWELL
PAISLEY
27
26
192
190
0
195
344
56
475
0
538
240
6
318
332
14
339
602
0
405
409
232
338
446
457
476
485
0
540
191
490
598
3
4
193
194
208
209
210
212
213
214
245
248
255
249
250
252
253
254
495
256
257
267
274
151
345
88
412
0
453
Seats Lost 1979 - 1983
No.
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
Name 1983 to 1987
-
PANo.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Name 1974 to 1979
ABERTILLERY
BANFF
BATTERSEA,SOUTH
BIRMINGHAM,HANDSWOR
BRISTOL,NORTH EAST
CONSETT
DARWEN
EDINBURGH NORTH
GLASGOW CENTRAL
GLASGOW CRAIGTON
GLASGOW KELVINGROVE
HACKNEY CENTRAL
HARROW,CENTRAL
HUDDERSFIELD,WEST
ISLINGTON CENTRAL
LAMBETH CENTRAL
LEEDS,SOUTH EAST
LIVERPOOL,KIRKDALE
LIVERPOOL,SCOTLAND
MANCHESTER,OPENSHAW
NEWCASTLE/TYNE CENT
RIPON
SALFORD,WEST
ST.MARYLEBONE
ST.PANCRAS NORTH
STOCKPORT SOUTH
WOOD GREEN
PANo.
7
31
43
66
107
157
173
219
255
256
260
279
291
325
339
353
361
377
378
399
423
489
512
509
510
547
623
Name 1955 to 1970
ABERTILLERY
BANFF
BATTERSEA,SOUTH
BIRMINGHAM,HANDSWOR
BRISTOL,NORTH EAST
CONSETT
DARWEN
EDINBURGH NORTH
GLASGOW CENTRAL
GLASGOW CRAIGTON
GLASGOW KELVINGROVE
HACKNEY CENTRAL
HARROW,CENTRAL
HUDDERSFIELD,WEST
ISLINGTON,EAST
LAMBETH,BRIXTON
LEEDS,SOUTH EAST
LIVERPOOL,KIRKDALE
LIVERPOOL,SCOTLAND
MANCHESTER,OPENSHAW
NEWCASTLE/TYNE CENT
RIPON
SALFORD,WEST
ST.MARYLEBONE
ST.PANCRAS NORTH
STOCKPORT SOUTH
WOOD GREEN
PANo.
7
30
43
67
105
154
168
211
246
247
251
271
279
311
323
341
351
370
371
393
419
481
505
502
503
542
618
Seats Lost 1970 - 1974
No.
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
Name 1983 to 1987
-
PANo.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Name 1974 to 1979
-
PANo.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Name 1955 to 1970
BARONS COURT
BIRMINGHAM,ALL SAIN
BRADFORD,EAST
BRISTOL,CENTRAL
CENTRAL NORFOLK
EALING,SOUTH
EAST HAM,SOUTH
ENFIELD,EAST
GLASGOW BRIDGETON
GLASGOW WOODSIDE
HESTON AND ISLEWORT
KENSINGTON,SOUTH
LEICESTER,SOUTH WES
LEWISHAM,SOUTH
LIVERPOOL,EXCHANGE
MANCHESTER,EXCHANGE
MERTON AND MORDEN
PADDINGTON,SOUTH
POPLAR
RHONDDA,WEST
SEDGEFIELD
SOUTHWARK
PANo.
35
63
91
104
426
199
204
216
244
258
295
329
357
362
368
390
401
452
466
478
508
532
683
684
685
686
687
688
-
0
0
0
0
0
0
-
0
0
0
0
0
0
STOCKTON-ON-TEES
TORRINGTON
WALTHAMSTOW,EAST
WANDSWORTH,CLAPHAM
WEDNESBURY
WEMBLEY,SOUTH
543
568
581
584
591
595
Northern Ireland 1955 - 1987
No.
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
Name 1983 to 1987
PANo.
ANTRIM EAST
0
ANTRIM NORTH
0
ANTRIM SOUTH
0
BELFAST EAST
0
BELFAST NORTH
0
BELFAST SOUTH
0
BELFAST WEST
0
DOWN NORTH
0
DOWN SOUTH
0
FERMANAGH AND SOUTH TYRONE0
FOYLE
0
LAGAN VALLEY
0
LONDONDERRY EAST
0
NEWRY AND ARMAGH
0
STRANGFORD
0
ULTER MID
0
UPPER BANN
0
Name 1974 to 1979
PANo.
0
ANTRIM NORTH
17
ANTRIM SOUTH
18
BELFAST EAST
52
BELFAST NORTH
53
BELFAST SOUTH
54
BELFAST WEST
55
DOWN NORTH
194
DOWN SOUTH
195
FERMANAGH AND SOUTH TYRONE239
LONDONDERRY COUNTY
384
0
0
ARMAGH
20
0
ULTER MID
584
0
Name 1955 to 1970
PANo.
0
ANTRIM NORTH
16
ANTRIM SOUTH
17
BELFAST EAST
50
BELFAST NORTH
51
BELFAST SOUTH
52
BELFAST WEST
53
DOWN NORTH
187
DOWN SOUTH
188
FERMANAGH AND SOUTH TYRONE231
LONDONDERRY COUNTY
378
0
0
ARMAGH
19
0
ULTER MID
574
0
259
Appendix C
Appendix C: Parliamentary Constituencies 1955-1987
- Results
A table of results had be constructed and checked for all seven hundred
and five constituencies identified in each of the general elections contested
between 1955 and 1987 inclusive. The identification number and current
name of each constituency is followed by the statistics from the elections
of 1955, 1959, 1964, 1966, 1970, February 1974, October 1974, 1979,
1983 and 1987. The BBC/ITN (1983) estimate of what the 1979 result
would have been, if it had been fought on the 1983 boundaries, is also
included for completeness. For each election the total votes cast for each of
the Conservative, Labour, Liberal or Alliance, Nationalist and any other
parties (combined) is given, along with the total electorate, listing over
forty thousand separate counts of the wishes of almost half a billion
electors across four decades.
The figures shown were mostly collated from studies deposited in the
Economic and Social Research Council's data archive in Essex. In all
cases, except February 1974, two independent versions of each set of
election results were available to be compared. The studies used were
those of Latham (1982) covering 1955-1979, Curtice (1983) covering
1955-1970, and Payne & Butler (1983) covering October 1974 and 1979.
Personal communications from Ron Johnson and Ivor Crewe (via Martin
Harrop) provided two versions of each of the 1983 and 1987 results.
Inconsistent records were subsequently compared with the paper lists of
F.W.S. Craig (1971, 1977, 1980, 1981, 1984, 1989), the Nuffield Election
Studies Publications (Butler D.E. et al 1955, 1960, 1965, 1966, 1971,
1974, 1975, 1979, 1984, 1988) and the Times Guide to the House of
Commons (1987). The final table presented here is thought to be an
extremely accurate record.
Parliamentary Constituencies 1955-1987 Results
260
Where a constituency did not exist at a given election, blanks have been
inserted. "%" signs are placed where the vote for the other parties was too
large to be printed in that column. Each page of the table spans two A4
sheets and is printed at the minimum legible font size, to allow so many
numbers to be included.
P.C.A. stands for "Parliamentary Constituency Area" code and can be used
to refer from this table, to those in Appendices B and D.
Appendix C
British General election results 1955 to 1987
Political party and electoral roll Conservativ Labour
Parliamentary constituency area
1955
ENGLAND
1 BARROW & FURNESS
2 BERWICK-UPON-TWEED
3 BISHOP AUCKLAND
4 DURHAM CITY OF
5 COPELAND
6 EASINGTON
7 HEXHAM
8 LANGBAURGH
9 DURHAM NORTH
10 DURHAM NORTH WEST
11 PENRITH & THE BORDER
12 SEDGEFIELD
13 WANSBECK
14 WESTMORLAND & LONSDALE
15 WORKINGTON
16 BLAYDON
17 BLYTH VALLEY
18 CARLISLE
19 DARLINGTON
20 GATESHEAD EAST
21 HARTLEPOOL
22 HOUGHTON & WASHINGTON
23 JARROW
24 MIDDLESBROUGH
25 NEWCASTLE UPON TYNE CENTL
26 NEWCASTLE UPON TYNE EAST
27 NEWCASTLE UPON TYNE NORTH
28 REDCAR
29 SOUTH SHIELDS
30 STOCKTON NORTH
31 STOCKTON SOUTH
32 SUNDERLAND NORTH
33 SUNDERLAND SOUTH
34 TYNE BRIDGE
35 TYNEMOUTH
36 WALLSEND
37 BARNSLEY WEST & PENISTONE
38 BEVERLEY
39 BOOTHFERRY
40 BRIDLINGTON
41 BRIGG & CLEETHORPES
42 CALDER VALLEY
43 COLNE VALLEY
44 DEWSBURY
45 DONCASTER NORTH
46 DON VALLEY
47 ELMET
48 HARROGATE
49 HEMSWORTH
50 KEIGHLEY
51 NORMANTON
52 PONTEFRACT & CASTLEFORD
53 RICHMOND (YORKS)
54 ROTHER VALLEY
55 RYEDALE
56 SCARBOROUGH
57 SELBY
58 SHEFFIELD HALLAM
59 SHEFFIELD HILLSBOROUGH
60 SHIPLEY
61 SKIPTON & RIPON
62 WENTWORTH
63 BARNSLEY CENTRAL
64 BARNSLEY EAST
65 BATLEY & SPEN
66 BRADFORD NORTH
67 BRADFORD SOUTH
68 BRADFORD WEST
69 DONCASTER CENTRAL
70 GLANFORD & SCUNTHORPE
71 GREAT GRIMSBY
72 HALIFAX
73 HUDDERSFIELD
74 HULL EAST
75 HULL NORTH
76 HULL WEST
77 LEEDS CENTRAL
78 LEEDS EAST
79 LEEDS NORTH EAST
80 LEEDS NORTH WEST
81 LEEDS WEST
82 LEEDS SOUTH & MORLEY
83 PUDSEY
84 ROTHERHAM
85 SHEFFIELD ATTERCLIFFE
Liberal
20033 22792
18301 12024
15959 21804
16640 32412
16154 22348
9095 34352
23462 13198
27468 27649
10047 32323
13110 27116
22791
9119
Did not exist until 1979
10619 25452
21048
7901
17182 25110
12750 25273
13429 36522
20071 19701
25765 23184
16706 21653
23560 25145
10476 33375
14304 24706
16278 27036
25236 14303
20994 22816
20217 25401
Did not exist until 1974
21482 31734
Did not exist until 1974
25495 18134
21401 24237
24727 22953
11709 22040
30949 20113
25275 34625
17796 29432
26162 11820
20486
9088
25880 10614
21796 15276
17309 20092
19512 23108
15869 23286
24598 22938
10402 36718
24194 18027
26799 10258
8561 42603
16011 19414
10040 27846
10183 32646
24979
8974
12916 39968
25467 11382
27133 10488
15456 25420
30069 15330
16428 23438
22582 17251
25101 15919
Did not exist until 1979
14776 39485
Did not exist until 1979
22048 23674
21084 21015
16768 20478
22306 19147
13701 38433
22826 27847
21404 24926
28306 26771
18611 22835
16284 28990
25780 25190
20262 25785
13817 25833
21144 26083
24902 15623
31923 16594
18312 24576
17970 27178
20445 15881
15882 27423
13503 33071
Nationalis Other par Registered
electors
Conservativ Labour
Liberal
Nationalis Other par Registered
1959
electors
0
0
0
0
0
0
0
0
0
0
7342
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
368
53073
41664
49051
61729
45957
54748
47438
68208
53247
50885
50875
19220
19904
13377
17106
16653
9259
25500
30445
10838
13172
23551
23194
11637
21706
33795
22783
36552
14980
28790
33901
28064
9342
0
0
4377
0
0
0
0
0
0
0
7602
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
51904
40951
48865
62192
46650
56690
49906
71281
53884
50629
51190
0
7688
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
44866
47222
49094
47138
62235
48324
59448
48692
59512
55166
49340
59563
50955
53097
57142
10716
20676
16894
13719
13122
21948
24318
17654
25463
11398
15286
18365
24588
21457
23933
27435
7359
25537
25969
38616
19950
19901
25319
25281
35960
25638
29391
13316
21359
28956
0
8984
0
0
0
0
5863
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
45361
46991
49401
47854
62599
49519
59342
52662
60888
56780
50965
62666
47930
50616
64509
0
0
0
74340
23638
32577
0
0
0
75538
0
0
0
0
5082
0
0
0
5575
0
0
7046
0
5516
0
0
0
0
0
6310
0
0
0
0
0
9215
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
52916
60255
61615
46567
70758
73928
59029
50790
47676
52583
50329
54631
52540
55257
58117
58473
52066
51570
64060
49750
48514
53877
50490
67132
50212
64531
52190
61231
54643
46205
49710
24602
22133
27825
11509
32810
29096
19809
26102
20681
27438
24211
16993
13030
17201
26521
11205
26200
29466
9788
20626
11169
10884
28270
15369
27413
25276
16581
28747
16845
22536
20278
15892
24341
26835
21277
18866
37862
31117
9750
7809
10047
15408
18949
19284
20870
22935
39088
18647
10196
45153
20456
29672
35194
9203
43962
12318
10468
26352
11938
21888
17025
11178
4336
0
0
0
6525
0
0
7562
7384
0
0
7654
11254
7321
0
0
0
0
0
0
0
0
0
0
0
10759
0
5119
0
0
10543
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
53059
57763
68014
42643
72273
80235
61397
53906
47310
53621
51773
52560
51777
54894
58505
59444
54448
53248
65705
47981
49139
54677
52416
71652
52517
63938
53191
60225
51023
45460
49037
0
0
0
68997
15189
42565
0
0
0
69833
0
0
6029
0
0
0
0
0
0
7242
0
0
0
0
0
0
3699
0
6526
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
54616
51472
53849
50726
64210
67808
63176
68714
51672
69423
65880
65670
54424
61944
55441
64142
60202
56513
50175
55971
64601
23243
22850
18158
23012
16787
26893
24638
29212
19389
17648
23612
23011
12956
23922
26240
35210
21285
19115
22752
16759
15304
23290
20179
21172
17906
40935
28997
24729
26697
22474
30667
22910
25446
24442
28707
14709
18508
25878
26781
16241
28298
33676
0
0
6850
0
0
0
0
0
0
10043
5604
0
4340
0
0
0
0
0
6429
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
54422
51957
57018
50044
68876
71138
64350
67149
51929
73826
63918
64100
52822
66074
54594
69243
60269
56031
52285
57080
65024
Page 1
Appendix C
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
SHEFFIELD BRIGHTSIDE
SHEFFIELD CENTRAL
SHEFFIELD HEELEY
WAKEFIELD
YORK
AMBER VALLEY
ASHFIELD
BASSETLAW
BLABY
BOLSOVER
BOSWORTH
BROXTOWE
CORBY
DAVENTRY
LINDSEY EAST
EREWASH
GAINSBOROUGH & HORNCASTLE
GEDLING
GRANTHAM
HARBOROUGH
HIGH PEAK
HOLLAND WITH BOSTON
KETTERING
LOUGHBOROUGH
MANSFIELD
NEWARK
DERBYSHIRE NORTH EAST
LEICESTERSHIRE NORTH WEST
RUSHCLIFFE
RUTLAND & MELTON
SHERWOOD
DERBYSHIRE SOUTH
STAMFORD & SPALDING
WELLINGBOROUGH
DERBYSHIRE WEST
CHESTERFIELD
DERBY NORTH
DERBY SOUTH
LEICESTER EAST
LEICESTER SOUTH
LEICESTER WEST
LINCOLN
NORTHAMPTON NORTH
NORTHAMPTON SOUTH
NOTTINGHAM EAST
NOTTINGHAM NORTH
NOTTINGHAM SOUTH
BURY ST. EDMUNDS
SUFFOLK CENTRAL
GREAT YARMOUTH
HUNTINGDON
NORFOLK MID
CAMBRIDGESHIRE NORTH EAST
NORFOLK NORTH
NORFOLK NORTH WEST
CAMBRIDGESHIRE SOUTH EAST
NORFOLK SOUTH
SUFFOLK SOUTH
CAMBRIDGESHIRE SOUTH WEST
NORFOLK SOUTH WEST
SUFFOLK COASTAL
WAVENEY
CAMBRIDGE
IPSWICH
NORWICH NORTH
NORWICH SOUTH
PETERBOROUGH
BARKING
BATTERSEA
BECKENHAM
BETHNAL GREEN & STEPNEY
BEXLEYHEATH
BOW & POPLAR
BRENT EAST
BRENT NORTH
BRENT SOUTH
BRENTFORD & ISLEWORTH
CARSHALTON & WALLINGTON
CHELSEA
CHINGFORD
CHIPPING BARNET
CHISLEHURST
CROYDON CENTRAL
CROYDON NORTH EAST
CROYDON NORTH WEST
CROYDON SOUTH
DAGENHAM
DULWICH
EALING ACTON
EALING NORTH
12239 27643
10565 28904
30798 19747
18435 28180
31402 30298
17268 38961
12836 32905
19375 26873
27257 17073
8055 30074
23526 27626
23509 21866
Did not exist until 1979
21497 17339
20392 10122
24039 25620
21576 17107
27521 20664
24188 21813
Did not exist until 1974
19094 13652
28412 19329
25495 31398
Did not exist until 1979
13610 29643
20916 23057
17621 34965
19781 24044
29145 22092
30074 19294
Did not exist until 1979
24115 30214
17675 14856
21819 22745
21052 14296
21748 29602
19156 24162
16572 23081
17094 22264
26070 14529
19297 22807
18551 23773
28771 32119
Did not exist until 1974
20903 20145
20462 26552
21631 25539
24532 19962
21317 20428
21317 20400
20609 14670
Did not exist until 1979
24862 18416
19657 20899
20949 19611
25025 21051
18690 17215
25185 17995
Did not exist until 1979
16588 16781
Did not exist until 1979
21672 23587
27059 19932
28724 32306
12087 18682
18659 16901
26319 23081
12082 27129
8058 20980
38614 17377
6504 27205
28610 24111
5733 27677
22738 23397
22701 12592
18074 29185
18489 16384
30429 18924
23598
8546
26065 22542
30299 19570
Did not exist until 1974
27359 20659
25097 16616
26297 15760
37276 12567
13718 38811
25333 23482
20120 20645
23040 22794
0
0
0
0
0
0
0
0
6524
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1461
0
0
0
0
0
0
0
0
0
0
0
58156
55373
65667
59828
73849
69967
59820
58203
61019
49144
63360
54760
12269
10598
33236
20114
33099
18286
14690
20162
29281
9076
26341
27392
28302
26078
23109
29705
29025
39930
35432
27875
16767
32536
27734
22952
0
0
0
0
0
0
0
0
11333
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1373
0
0
0
0
0
0
0
0
0
0
0
57090
51533
72648
60791
73717
69719
61139
59907
67790
50455
65115
58971
0
0
0
0
0
1624
0
0
0
0
0
0
0
0
0
0
0
0
47614
42967
60476
50399
59479
57546
24226
19799
25374
20056
30722
27482
18292
9928
25362
13247
22645
20867
0
0
4980
7147
0
0
0
0
0
0
0
0
0
0
0
0
0
0
51403
42262
65457
50051
64554
59026
6712
5581
0
0
0
0
0
0
0
49612
70040
69764
18738
29013
29448
13827
17839
32933
8138
7334
0
0
0
0
0
0
0
49196
70588
74696
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
55215
52655
68537
53183
65449
60986
14700
22300
22112
17749
29607
34997
31066
24072
37444
21496
22235
22176
0
0
0
6303
0
0
0
0
0
0
0
0
0
0
0
0
0
0
56674
54597
73678
54225
65459
70233
0
0
0
0
0
0
3408
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
66585
40818
51811
44170
64250
57201
54675
50121
51747
53472
49729
73712
27007
19078
22964
22034
17084
20266
17345
17990
28390
19742
19240
25106
31344
14137
22358
13925
30534
22673
20776
19421
13760
21515
23629
27823
0
0
0
0
6360
0
4746
0
0
0
0
7170
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
69336
41061
52261
43881
65270
55976
54131
47733
53810
51922
50933
72521
0
0
0
0
5582
0
0
0
0
0
0
0
0
0
0
916
0
0
0
0
0
56463
60234
61969
56854
57092
52350
45757
24004
18952
22052
26730
22333
22827
20254
21869
24005
21888
18768
19849
19248
11983
0
6581
0
0
5215
0
5389
0
0
0
0
0
0
0
0
1331
0
0
0
0
0
62475
63163
54582
57908
56395
52847
46794
0
0
0
0
0
3760
0
0
0
0
0
0
0
0
0
0
0
0
61188
49674
51867
58518
43887
58890
26173
19126
21671
27407
19275
26130
19705
19784
19906
19928
16542
16248
0
0
0
0
0
6914
0
0
0
0
0
0
0
0
0
0
0
0
61387
48756
52125
60698
43458
60756
0
0
0
40396
16780
16858
0
0
0
40283
0
0
0
0
0
0
0
0
0
5541
0
1615
0
4916
0
0
5277
0
7528
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
622
0
0
0
2888
0
0
0
0
0
0
0
0
56850
59868
75792
40843
45402
59513
53314
42766
73177
61410
63863
65601
60604
48874
63559
42528
67655
49049
68184
61255
24324
24350
22623
12609
19128
27414
11454
9289
36528
7412
32025
8566
22709
22211
17946
17869
30454
20985
31507
33136
22835
17543
25858
19092
16884
22830
23454
19595
13395
24228
23392
26875
20499
11131
25680
14950
17210
6308
27114
19737
0
5792
14359
0
0
0
5648
0
9365
5508
0
0
0
6171
0
0
8744
3662
11913
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
899
0
2548
0
0
1324
0
0
0
0
0
57814
59745
77633
41221
43789
60545
51654
40937
73421
57617
64906
63932
58865
47554
61534
39881
68391
47077
83647
64739
0
3892
4139
0
0
3501
0
3770
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
62177
58663
59575
65179
76198
66495
49373
58245
29284
24345
25111
36310
16626
24991
19358
27312
21069
15440
14658
10102
37009
22740
18438
23036
0
6109
6061
10376
0
5324
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63636
57174
58177
69996
73968
66988
46835
59768
Page 2
Appendix C
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
EALING SOUTHALL
EDMONTON
ELTHAM
ENFIELD NORTH
ENFIELD SOUTHGATE
ERITH & CRAYFORD
FELTHAM & HESTON
FINCHLEY
FULHAM
GREENWICH
HACKNEY NORTH & STOKE NEW
HACKNEY SOUTH & SHORED'H
HAMMERSMITH
HAMPSTEAD & HIGHGATE
HARROW EAST
HARROW WEST
HAYES & HARLINGTON
HENDON NORTH
HENDON SOUTH
HOLBORN & ST PANCRAS
HORNCHURCH
HORNSEY & WOOD GREEN
ILFORD NORTH
ILFORD SOUTH
ISLINGTON NORTH
ISLINGTON SOUTH & FINSBUR
KENSINGTON
KINGSTON UPON THAMES
LEWISHAM DEPTFORD
LEWISHAM EAST
LEWISHAM WEST
LEYTON
MITCHAM & MORDEN
NEWHAM NORTH EAST
NEWHAM NORTH WEST
NEWHAM SOUTH
NORWOOD
OLD BEXLEY & SIDCUP
ORPINGTON
PECKHAM
PUTNEY
RAVENSBOURNE
RICHMOND & BARNES
ROMFORD
RUISLIP - NORTHWOOD
SOUTHWARK & BERMONDSEY
STREATHAM
SURBITON
SUTTON & CHEAM
CITY OF LONDON & WESTMIN
TOOTING
TOTTENHAM
TWICKENHAM
UPMINSTER
UXBRIDGE
VAUXHALL
WALTHAMSTOW
WANSTEAD & WOODFORD
WESTMINSTER NORTH
WIMBLEDON
WOOLWICH
AYLESBURY
BANBURY
BEACONSFIELD
BILLERICAY
BRAINTREE
BRENTWOOD & ONGAR
BUCKINGHAM
CHELMSFORD
CHESHAM & AMERSHAM
EPPING FOREST
HARLOW
HARWICH
HENLEY
HERTFORD & STORTFORD
HERTSMERE
BEDFORDSHIRE MID
MILTON KEYNES
BEDFORDSHIRE NORTH
COLCHESTER NORTH
HERTFORDSHIRE NORTH
LUTON NORTH
OXFORD WEST & ABINGDON
ROCHFORD
SAFFRON WALDEN
ST ALBANS
COLCHESTER SOUTH & MALDON
BEDFORDSHIRE SOUTH WEST
HERTFORDSHIRE SOUTH WEST
STEVENAGE
18872 25207
23194 30232
23981 22101
22021 10503
26794
8584
16339 24957
18171 21521
30233 17408
19578 23972
18484 26423
15165 25253
9216 25500
15417 24280
28226 16040
22243 18621
30321 13024
13440 19588
21934 17874
25354 14918
16195 17126
29205 27833
33294 20568
28749 18248
27292 20814
14522 22100
11667 24935
17283 20226
31069 16104
12472 23925
22070 18834
24066 19741
21543 29747
32798 25208
12416 17961
10712 27249
5997 29451
24831 19799
24514 20644
22166 10230
12547 26315
28969 21774
24612 11473
27628 14673
24701 27326
24806 13251
4309 21709
25862 13594
22863 12380
29538 15205
31314 13270
25484 24391
17753 26636
33726 17450
Did not exist until 1974
21368 22244
10492 19220
10077 19327
25069
9261
14370 16462
22112 11622
12929 23275
20330 14569
25598 21473
29165 11184
Did not exist until 1979
22002 21452
Did not exist until 1974
Did not exist until 1979
25450 20301
Did not exist until 1974
19503 17628
Did not exist until 1974
23889 14425
24061 16980
Did not exist until 1979
Did not exist until 1974
23012 19048
23250 22110
24733 19792
24796 19898
26371 25406
Did not exist until 1974
Did not exist until 1979
Did not exist until 1974
20671 14253
21828 16107
Did not exist until 1979
23365 20897
28847 21878
Did not exist until 1974
0
0
0
0
7614
0
0
7775
0
0
2388
0
0
6222
0
0
0
3467
0
1193
6117
0
4702
0
0
0
0
0
0
0
0
4421
0
0
3393
0
0
4120
4610
0
0
0
5266
0
0
1554
0
0
0
0
0
0
6626
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1525
0
0
0
0
0
886
0
0
0
0
1442
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
715
0
0
0
0
0
0
0
57663
71739
55329
41595
55745
52253
50650
70757
55373
61314
65281
56393
56677
72423
49460
54616
44259
53653
54983
51282
77041
72484
67496
63866
56574
58998
53789
61762
52282
52485
55056
74944
72028
40548
61346
53862
59385
58063
46581
61050
66776
47954
61365
68942
47698
40695
52727
44331
58529
74162
64276
63242
75106
19966
25497
24373
24861
25704
18763
18070
29697
18581
19679
14415
11178
14662
25506
23554
30512
14149
21898
22971
17065
34852
30048
29609
23876
14820
11974
14048
31649
13038
22125
22466
24448
33661
12175
9318
5188
22958
25748
24303
13007
28236
27055
27161
24951
23480
6187
23479
24058
27344
27489
23655
15688
33677
22285
25958
20678
11058
7613
24523
20320
13437
21525
25204
22950
22744
21409
13500
17607
12512
18301
16566
11016
16409
27530
17710
15962
16569
18718
22362
14925
15408
21226
17512
16233
28367
23845
16001
24096
28017
15975
19069
9543
24389
23115
11603
12975
25558
10424
20528
10773
11633
11946
10301
21683
22325
16638
0
0
0
0
8968
0
4533
12701
0
0
6076
0
0
8759
0
0
4235
4598
7134
0
11056
5706
7915
6832
0
0
3118
0
0
2921
4721
0
0
0
7271
4020
4744
6366
9092
0
6166
0
7359
8228
7295
0
5039
0
7600
4409
4287
5030
8589
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1189
0
0
0
0
0
0
0
0
0
0
0
0
0
527
0
0
0
0
1107
0
0
0
0
2821
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
55290
67837
54563
44983
54869
53077
53417
69123
52088
60561
64723
53210
51680
69438
49273
54295
46244
52729
53545
48504
87544
71151
67208
60678
54120
56620
51492
60403
49412
52415
54069
70996
70463
38014
57828
52341
57807
59646
51872
57850
71772
48937
59852
73082
49198
37921
50916
45165
58898
68896
61831
59794
73852
0
0
0
0
0
0
0
5869
0
6885
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
53372
47354
40570
45193
42689
43099
48964
49833
61019
60501
22360
11312
7872
24815
13629
21538
12638
22504
26413
34154
20970
18437
15980
10018
14397
10678
22353
13549
19699
13050
4746
0
5229
0
0
0
0
7897
6074
10589
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
56997
45802
38226
45070
40952
42151
46349
54089
64414
72466
0
0
0
52027
21772
19532
3860
0
0
54378
0
0
0
55920
29992
20124
0
0
0
61630
0
0
0
46583
23422
17860
0
0
0
50213
4010
0
0
0
0
0
56003
54459
23653
24417
11588
15014
5507
6261
0
0
3744
0
58194
58319
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
51699
53298
54439
55527
62258
21301
22304
23495
24592
30193
16127
20558
16728
17096
25818
8099
4577
5966
5942
8481
0
0
0
0
0
0
0
0
0
0
53889
54905
55278
57776
75493
3209
0
0
0
0
0
47922
47827
20955
23157
14173
14650
4245
5948
0
0
0
0
48477
52823
0
0
0
0
0
0
54050
62383
25861
29724
21102
19487
7912
9278
0
0
0
0
65416
69291
Page 3
Appendix C
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
WANTAGE
WELWYN HATFIELD
HERTFORDSHIRE WEST
WITNEY
WYCOMBE
BASILDON
BROXBOURNE
CASTLE POINT
LUTON SOUTH
OXFORD EAST
SOUTHEND EAST
SOUTHEND WEST
THURROCK
WATFORD
ALDERSHOT
ARUNDEL
ASHFORD
BASINGSTOKE
BEXHILL & BATTLE
CANTERBURY
CHICHESTER
DARTFORD
DOVER
BERKSHIRE EAST
HAMPSHIRE EAST
SURREY EAST
FAREHAM
FAVERSHAM
FOLKESTONE & HYTHE
GRAVESHAM
GUILDFORD
HASTINGS & RYE
HORSHAM
ISLE OF WIGHT
LEWES
MAIDSTONE
MEDWAY
KENT MID
SUSSEX MID
MOLE VALLEY
NEWBURY
NEW FOREST
THANET NORTH
HAMPSHIRE NORTH WEST
SURREY NORTH WEST
READING EAST
READING WEST
ROMSEY & WATERSIDE
SEVENOAKS
SHOREHAM
THANET SOUTH
SURREY SOUTH WEST
TONBRIDGE & MALLING
TUNBRIDGE WELLS
WEALDEN
WINCHESTER
WINDSOR & MAIDENHEAD
WOKING
WOKINGHAM
BRIGHTON KEMPTOWN
BRIGHTON PAVILION
CHERTSY & WALTON
CRAWLEY
EASTBOURNE
EASTLEIGH
EPSOM & EWELL
ESHER
GILLINGHAM
GOSPORT
HAVANT
HOVE
PORTSMOUTH NORTH
PORTSMOUTH SOUTH
REIGATE
SLOUGH
SOUTHAMPTON ITCHEN
SOUTHAMPTON TEST
SPELTHORNE
WORTHING
BRIDGWATER
CHRISTCHURCH
CIRENCESTER & TEWKESBURY
DEVIZES
FALMOUTH & CAMBORNE
HONITON
NORTHAVON
CORNWALL NORTH
DEVON NORTH
DORSET NORTH
WILTSHIRE NORTH
25613 16979
4270
25014 19030
0
25648 19512
5111
Did not exist until 1974
29845 21905
0
24327 20121
0
26936 20418
0
20531 13841
0
24722 20304
3140
27708 19930
5336
23958 17200
0
27326
8866
6375
16046 31375
0
22546 20829
0
22701 13129
4232
Did not exist until 1974
23992 15685
0
24973 18683
0
28500 10560
0
28739 14444
0
30857 12735
0
21730 25928
0
27316 24298
0
25843 12895
4679
24826 10736
0
Did not exist until 1974
30918 18432
0
23922 23981
0
23851 12849
0
22058 19149
0
27113 15785
0
20469 11933
4303
Did not exist until 1979
31335 18698
0
24938 12392
0
27267 19861
0
24198 26645
0
Did not exist until 1979
Did not exist until 1974
24451 11942
0
26080 18843
0
Did not exist until 1979
Did not exist until 1974
Did not exist until 1979
Did not exist until 1974
Did not exist until 1974
24990 25228
0
27027 12285
0
28936 17858
0
35180 15188
0
31270 18981
0
23717 12811
0
Did not exist until 1974
29521 19325
0
28450 11750
6034
23827 12591
0
25390 14666
0
27860 15393
0
Did not exist until 1979
23142 17885
0
27128 12742
0
23021 14656
0
28598 17088
0
29779 15561
0
20215 19670
0
36779 14706
0
33774 13132
6146
20984 16839
0
Did not exist until 1974
32014 17859
0
34314 11961
0
23729 20060
0
27887 13600
0
27210 16903
0
18124 20567
0
23378 29149
0
26707 22865
2583
20888 14906
0
31106
9231
0
24887 17170
0
Did not exist until 1974
0
0
0
20317 18242
0
20540 21587
0
25808
7907 11067
21760 20034
0
16824
3465 15220
16784
7272 11558
18906
5633 11747
20847 14152
5208
0
0
0
0
0
0
58487
53556
60013
27943
31148
30189
16971
22597
21954
6651
0
8358
0
0
0
0
0
0
63844
64106
70962
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63094
58872
59857
47132
57932
67721
55635
57424
63030
52662
54209
30774
29224
28201
28124
27153
26798
24712
27612
20188
21216
25161
19904
24402
18020
17991
22134
18310
16987
9219
32270
18315
12270
7068
9347
8656
0
0
7491
0
10577
0
5753
5679
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
68199
78328
66913
60316
56769
66655
55265
60999
67054
53388
56820
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50821
57025
53223
59431
60712
58854
63064
56657
50994
25383
25314
27465
30846
30755
24047
27939
30896
23687
14983
14070
7359
15746
9546
25323
24698
14905
8278
0
9126
7549
0
6913
5881
0
7899
6912
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
52097
60979
54599
62011
63958
66599
63512
67144
52796
0
0
0
0
0
0
0
0
0
6514
0
0
66475
57543
50392
59099
56118
48493
35808
24074
21726
27124
27198
22458
19654
24327
9346
24962
13756
13576
0
0
7351
0
6318
0
0
0
0
0
0
0
0
0
0
0
0
0
73284
57760
50825
63299
58963
48569
0
0
0
0
0
0
0
0
67297
49885
61144
61819
31228
29642
30115
26510
18396
13065
19652
25487
0
0
0
0
0
0
0
0
0
0
0
0
66939
56338
63311
64386
0
0
0
0
47328
57404
24564
29703
9605
19787
6582
0
0
0
0
0
51092
62854
0
0
0
0
0
0
0
0
0
0
0
0
59678
53724
59937
69034
69910
47861
26314
29949
28186
37034
29453
23538
22372
13667
14265
12745
17555
9800
0
0
7819
8081
6998
6538
0
0
0
0
0
0
0
0
0
0
0
0
58772
58958
62701
75601
71952
50249
0
0
0
0
0
0
0
0
0
0
64709
61567
47464
54649
57119
31687
31759
24924
29942
33521
21181
10104
12132
15864
16210
0
9100
0
0
0
0
0
0
0
0
0
0
0
0
0
67320
65437
48321
60673
64295
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
58622
59053
48812
59776
59810
48929
66379
66925
47561
25411
27972
24836
37275
27874
24949
35484
37155
23142
19665
11998
14150
24012
11837
21693
11039
12934
15863
0
0
5146
0
8955
0
9910
8730
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
61119
57238
55609
76618
62971
55215
69592
72183
48390
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
68299
65209
56597
57311
56013
48459
67098
66256
46050
55456
53920
38834
36150
23600
27892
26966
20763
25390
30176
25221
31396
23002
20553
12206
17334
11979
14465
20851
29123
23410
17128
7618
14706
0
0
0
0
8205
0
0
0
0
7045
7893
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
79885
67018
53206
55121
60266
52114
69886
67087
52115
60505
55770
0
0
0
0
0
0
0
0
0
37766
0
0
0
0
0
0
0
0
55305
49047
53791
56203
51166
43145
43906
44142
50278
28169
20682
15886
25959
26168
16701
15469
20255
21696
16314
16844
20083
6928
21567
3389
5567
6548
12911
0
0
7890
12906
0
15712
15831
11604
7059
0
0
0
0
0
0
0
0
0
0
2707
0
0
0
0
0
0
0
58099
50779
53763
57172
57026
42764
43486
46844
51923
Page 4
Appendix C
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
ST IVES
SALISBURY
SOMERTON & FROME
DORSET SOUTH
CORNWALL SOUTH EAST
SOUTH HAMS
STROUD
TAUNTON
TEIGNBRIDGE
TIVERTON
DEVON WEST & TORRIDGE
TRURO
WANSDYKE
WELLS
WESTBURY
DORSET WEST
GLOUCESTERSHIRE WEST
WESTON-SUPER-MARE
WOODSPRING
YEOVIL
BATH
BOURNEMOUTH EAST
BOURNEMOUTH WEST
BRISTOL EAST
BRISTOL NORTH WEST
BRISTOL SOUTH
BRISTOL WEST
CHELTENHAM
EXETER
GLOUCESTER
KINGSWOOD
PLYMOUTH DEVONPORT
PLYMOUTH DRAKE
PLYMOUTH SUTTON
POOLE
SWINDON
TORBAY
BROMSGROVE
BURTON
CANNOCK & BURNTWOOD
HEREFORD
LEOMINSTER
LUDLOW
MERIDEN
STAFFORDSHIRE MID
WORCESTERSHIRE MID
SHROPSHIRE NORTH
WARWICKSHIRE NORTH
NUNEATON
RUGBY & KENILWORTH
SHREWSBURY & ATCHAM
STAFFORDSHIRE SOUTH EAST
STAFFORDSHIRE SOUTH
WORCESTERSHIRE SOUTH
STAFFORD
STAFFORDSHIRE MOORLANDS
STRATFORD-ON-AVON
WARWICK & LEAMINGTON
WYRE FOREST
ALDRIDGE-BROWNHILLS
BIRMINGHAM EDGBASTON
BIRMINGHAM ERDINGTON
BIRMINGHAM HALL GREEN
BIRMINGHAM HODGE HILL
BIRMINGHAM LADYWOOD
BIRMINGHAM NORTHFIELD
BIRMINGHAM PERRY BARR
BIRMINGHAM SELLY OAK
BIRMINGHAM SMALL HEATH
BIRMINGHAM SPARKBROOK
BIRMINGHAM YARDLEY
COVENTRY NORTH EAST
COVENTRY NORTH WEST
COVENTRY SOUTH EAST
COVENTRY SOUTH WEST
DUDLEY EAST
DUDLEY WEST
HALESOWEN & STOURBRIDGE
NEWCASTLE UNDER LYME
SOLIHULL
STOKE-ON-TRENT CENTRAL
STOKE-ON-TRENT NORTH
STOKE-ON-TRENT SOUTH
SUTTON COLDFIELD
WREKIN THE
WALSALL NORTH
WALSALL SOUTH
WARLEY EAST
WARLEY WEST
WEST BROMWICH EAST
17063
9728
6020
20271 12632
5037
25624 19745
0
22119 16702
4798
17858
8304 10199
Did not exist until 1979
23318 19375
4489
22962 17420
3684
26381 14787
9471
23475 13051
0
18991
8755
6939
19900 15183
8056
Did not exist until 1979
Did not exist until 1979
19684 16295
7165
21007 14244
0
18346 22366
0
27357 16275
0
26985 22802
0
24059 19793
6089
24489 17646
5011
28757 10259
4851
31931 15147
0
17210 25257
0
21295 22950
0
13978 24954
0
32767 10766
0
24259 16638
0
24147 18759
0
20606 21354
0
Did not exist until 1974
24821 24721
3100
30051 26241
0
Did not exist until 1974
26594 17032
5750
17987 21926
0
29777 12547
7012
27461 22287
0
24519 21546
0
18379 26677
0
18058
8154
8658
18487
9740
0
20816 12937
0
21691 22796
0
Did not exist until 1979
Did not exist until 1979
22859 12434
0
Did not exist until 1979
14828 25112
5048
18331 19709
0
21319 13726
0
17966 21071
0
Did not exist until 1974
26811 13831
0
26206 17550
0
27214 28273
0
24587 11275
0
29979 16513
0
26142 17918
0
Did not exist until 1974
26991 12897
0
17284 25546
0
28543 17846
0
16618 23358
0
9665 18476
0
24188 27072
0
17052 18732
0
25774 19054
0
14484 22444
0
16821 20032
0
20598 23722
0
21608 27712
0
21392 24565
0
Did not exist until 1974
25761 27449
0
20333 31384
5479
24064 25013
0
19068 24123
9171
21569 28314
0
29323 11300
0
16097 28452
0
14599 29473
0
17739 31003
0
31552 13565
0
19019 18541
0
15970 26665
0
24077 21651
0
16656 23151
0
14998 28166
0
16222 26242
0
0
0
0
0
0
0
0
0
0
0
44374
48823
57057
55039
45715
15700
20641
23357
22050
16853
8802
12932
16452
15357
5769
8258
5516
8220
6887
14052
0
0
0
0
0
0
0
0
0
0
44010
49997
57455
56196
45000
0
0
0
0
0
0
0
0
0
0
0
0
55962
51564
62710
47858
45096
54798
23448
22680
26925
21714
19778
19544
18336
16182
13116
9836
8022
15057
6988
7031
10719
7504
9008
9637
0
0
0
0
0
0
0
0
0
0
0
0
57220
52675
63071
48416
46908
55185
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
52334
44026
51772
59135
58282
58714
57175
58092
65651
54499
55942
52142
58359
51491
54101
51841
20396
19747
16223
27881
30432
23771
24048
29014
33575
20446
24938
17428
27768
21997
21579
16679
14570
11536
21634
10977
23649
17638
17515
9222
15957
26273
23019
27010
7651
12725
15918
19450
9816
4850
5921
9609
0
9484
6214
8308
0
0
0
0
5835
8428
6852
7336
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
53238
44109
54202
60795
63231
59737
57150
60657
68209
57416
57831
58671
56080
52946
54084
52836
0
0
0
0
68235
71367
28481
32752
22027
25991
0
0
0
0
0
0
64236
74078
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
61004
49879
65353
60898
57025
58553
44242
40098
47040
54596
26956
20178
29527
32473
26926
22485
17763
16642
21464
26498
15325
24087
11784
23433
21032
29624
8097
6475
14138
26235
8735
0
10685
0
0
0
10185
6905
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63554
55339
67608
66924
58229
65472
45340
39306
46735
62449
0
0
50289
21055
10531
6068
0
0
50772
0
0
0
0
0
1274
0
0
56452
46011
45239
48250
15354
17429
19970
19791
24894
16959
11338
21341
7227
6413
6387
0
0
0
0
0
0
142
0
0
58038
47809
46846
50240
0
0
0
0
0
0
0
0
0
0
0
0
55730
55395
68062
47451
59019
56216
25824
28107
29947
26146
32513
27699
10884
18034
31096
12017
19434
18356
6890
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
57687
57078
72777
49660
62849
58223
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
928
0
0
0
0
0
0
58469
59889
61640
55004
46904
65219
50500
60356
56101
51057
59078
60769
55845
26401
18984
29148
18996
8393
28647
16628
24950
14282
17751
23482
24982
21794
11473
21518
15431
21919
14717
29587
16811
16594
19213
16865
22097
32744
23035
0
0
0
0
0
0
5611
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1955
0
0
0
424
0
0
0
0
0
0
55719
57593
61066
55674
39131
74269
50306
58017
51004
47731
59135
70689
53598
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
65404
71651
62179
66622
61776
51897
62444
58518
66212
58839
46897
55357
56990
52748
59908
60485
28584
26101
31202
21478
23838
35862
18205
16522
20318
33064
22030
17741
30471
17126
17174
19809
26754
31826
27069
23861
29840
12682
28630
29336
29578
11310
19052
27693
21689
20670
27151
26702
0
0
0
10343
0
0
0
0
0
7543
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
67394
72829
71161
68892
63623
60227
62220
58336
63777
65347
48789
59257
62804
49794
59895
64111
Page 5
Appendix C
446 WEST BROMWICH WEST
447 WOLVERHAMPTON NORTH EAST
448 WOLVERHAMPTON SOUTH EAST
449 WOLVERHAMPTON SOUTH WEST
450 WORCESTER
451 BOLTON WEST
452 CHORLEY
453 CHESTER CITY OF
454 CONGLETON
455 CREWE & NANTWICH
456 EDDISBURY
457 ELLESMERE PORT & NESTON
458 FYLDE
459 HALTON
460 HAZEL GROVE
461 HEYWOOD & MIDDLETON
462 KNOWSLEY NORTH
463 KNOWSLEY SOUTH
464 LANCASTER
465 LITTLEBOROUGH & SADDLEWO'
466 MACCLESFIELD
467 MAKERFIELD
468 MORECOMBE & LUNESDALE
469 RIBBLE VALLEY
470 SOUTH RIBBLE
471 STALYBRIDGE & HYDE
472 TATTON
473 LANCASHIRE WEST
474 WIRRAL SOUTH
475 WIRRAL WEST
476 WORSLEY
477 WYRE
478 ALTRINCHAM & SALE
479 ASHTON-UNDER-LYNE
480 BIRKENHEAD
481 BLACKBURN
482 BLACKPOOL NORTH
483 BLACKPOOL SOUTH
484 BOLTON NORTH EAST
485 BOLTON SOUTH EAST
486 BOOTLE
487 BURNLEY
488 BURY NORTH
489 BURY SOUTH
490 CHEADLE
491 CROSBY
492 DAVYHULME
493 DENTON & REDDISH
494 ECCLES
495 HYNDBURN
496 LEIGH
497 LIVERPOOL BROADGREEN
498 LIVERPOOL GARSTON
499 LIVERPOOL MOSSLEY HILL
500 LIVERPOOL RIVERSIDE
501 LIVERPOOL WALTON
502 LIVERPOOL WEST DERBY
503 MANCHESTER BLACKLEY
504 MANCHESTER CENTRAL
505 MANCHESTER GORTON
506 MANCHESTER WITHINGTON
507 MANCHESTER WYTHENSHAWE
508 OLDHAM CENTRAL & ROYTON
509 OLDHAM WEST
510 PENDLE
511 PRESTON
512 ROCHDALE
513 ROSSENDALE & DARWEN
514 ST HELENS NORTH
515 ST HELENS SOUTH
516 SALFORD EAST
517 SOUTHPORT
518 STOCKPORT
519 STRETFORD
520 WALLASEY
521 WARRINGTON NORTH
522 WARRINGTON SOUTH
523 WIGAN
WALES
524 ABERAVON
525 ALYN & DEESIDE
526 BLAENAU GWENT
527 BRECON & RADNOR
528 BRIDGEND
529 CAERNARFON
530 CAERPHILLY
531 CARMARTHEN
532 CEREDIGION & PEMBROKE N.
533 CLWYD NORTH WEST
534 CLWYD SOUTH WEST
Did not exist until 1974
14387 23596
0
19482 26490
0
25318 16898
0
25610 19508
0
17848 27900
0
23656 24994
0
24905 13903
5145
15273 21629
4306
20250 12884
0
20697 14142
0
Did not exist until 1979
33204 10809
0
18374 19823
0
30940 10966
7756
Did not exist until 1979
Did not exist until 1979
22300 24858
0
19873 15324
0
25824 22614
0
27551 18362
0
11183 29830
0
29706 12005
0
21615 16671
0
21497 21023
0
23462 23617
0
29074
9588
0
27066 12527
0
31700 22277
0
33027 15976
0
18231 24829
0
23812
9152
0
30586 12174
6436
24251 26216
0
21352 24526
0
25752 26241
0
24773 12548
0
26899 10869
0
26145 22634
0
0 20014 24827
17582 19020
0
22229 27865
0
28080 24331
0
27096 16989
0
Did not exist until 1974
29161 13725
0
33101 21267
0
20833 21102
0
23025 25351
0
21157 22502
0
18142 30098
0
28172 12552
0
28130 16161
0
18940 20060
0
20576 16037
0
23851 20989
0
21124 18540
0
25395 19959
0
13190 21721
0
20740 22822
0
25707 13054
5077
26200 23378
0
19185 18805
5506
19625 23164
0
19844 22135
0
22310 19407
0
26518 24928
0
20561 23472
0
21344 29299
0
19854 35737
0
18623 20351
0
30268 12827
0
23547 18980
0
23631 13103
0
33537 19319
0
17075 22721
0
24682 13852
0
14883 29755
0
12706 29003
0
20554 22828
0
6822 26058
0
16412 23953
4745
Did not exist until 1979
8461 17682
3277
9180 27852
0
0 21077 24410
0 10090 18907
18312 10421 13671
Did not exist until 1979
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
53171
61824
54303
58012
55151
57700
56452
50577
42679
43691
16639
23523
25696
27024
18634
24965
27847
19030
17613
20396
20436
27068
14529
19832
29359
25641
17492
22811
10876
12426
0
0
0
0
0
0
0
0
7983
4602
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
51217
65861
51293
59117
56948
59086
57617
50971
43655
44305
0
0
0
0
0
0
60623
45990
61626
36988
19620
32787
12521
21218
11373
0
0
15468
0
0
0
0
0
0
65310
48966
71205
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60036
43811
59203
56991
50614
56089
44893
51933
56359
49756
54198
68186
64082
52818
48081
61525
62468
60587
62548
55100
56648
61601
56724
48381
60023
65379
58416
27184
20783
19742
28978
11795
30228
22314
21954
22309
27270
32952
33705
39807
19356
27045
29992
23239
19361
24490
25767
25297
25885
0
18379
20902
28623
31416
33111
15255
17588
19652
30752
14253
16103
18935
23732
7945
14701
23884
18805
27393
11307
14141
25991
22990
27356
13337
9440
23153
19545
21294
27675
24715
21248
0
0
11713
0
0
0
0
0
0
8117
0
0
0
0
0
9415
0
4658
0
0
8990
0
23533
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
77371
43714
57868
58892
51273
57654
44350
49809
55183
52999
61420
70374
71025
56092
52521
64860
60706
59960
60362
52927
58421
60580
54035
50647
57990
64897
65855
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1567
58188
71410
54824
59382
50938
60256
57489
62373
57391
52575
59522
54100
58653
53169
60737
60941
64968
55980
54352
50355
51220
62126
52288
61885
75588
55853
62618
53271
53194
73149
53826
47487
57575
29801
32888
22480
23580
21642
16897
26624
31441
19026
19575
24288
22719
22163
11605
17392
23170
28934
17499
18505
19143
23990
11665
18152
23065
21956
17171
26905
23487
22090
35567
17791
26615
14615
14745
23538
23337
25566
22242
31672
10392
17284
19725
15660
20254
19386
17790
20941
24134
13476
27625
19329
22624
20407
19529
21689
20743
31041
35961
20639
9805
20265
13371
20501
22890
13837
30664
0
0
0
0
0
0
5161
0
0
0
0
0
7223
0
0
7675
0
6660
0
0
0
18949
4752
0
0
0
11292
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1889
0
0
0
0
0
0
0
0
0
0
0
0
945
57495
71304
55846
59317
49933
58911
55679
65506
54824
49686
57312
54804
57851
47156
57166
59457
69925
54520
51845
48472
52212
61091
50577
65124
75280
51231
62466
53287
51271
72660
52884
49584
55155
0
0
0
0
0
0
0
0
52616
51560
39305
51969
12759
22701
6404
18939
30397
22776
27326
25411
0
0
0
0
3066
0
0
0
0
0
0
0
56316
52635
39299
51357
5815
0
3835
0
0
0
0
0
0
0
42753
47131
57956
39902
53589
9564
7181
6147
0
17893
17506
28154
23399
8559
8620
0
0
16766
17868
13268
7293
3420
2545
3880
3077
0
0
0
0
0
41202
46671
57195
38878
53000
Page 6
Appendix C
535 CONWY
536 CYNON VALLEY
537 DELYN
538 GOWER
539 ISLWYN
540 LLANELLI
541 MEIRIONNYDD NANT CONWY
542 MERTHYR TYDFIL & RHYMNEY
543 MONMOUTH
544 MONTGOMERY
545 NEATH
546 NEWPORT EAST
547 NEWPORT WEST
548 OGMORE
549 PEMBROKE
550 PONTYPRIDD
551 RHONDDA
552 TORFAEN
553 VALE OF GLAMORGAN
554 WREXHAM
555 YNYS MON
556 CARDIFF CENTRAL
557 CARDIFF NORTH
558 CARDIFF SOUTH & PENARTH
559 CARDIFF WEST
560 SWANSEA EAST
561 SWANSEA WEST
SCOTLAND
562 ANGUS EAST
563 ARGYLL & BUTE
564 AYR
565 BANFF & BUCHAN
566 CAITHNESS & SUTHERLAND
567 CARRICK CUMNOCK & DOON VA
568 FIFE CENTRAL
569 CLACKMANNAN
570 CLYDEBANK & MILNGAVIE
571 CLYDESDALE
572 CUMBERNAULD & KILSYTH
573 CUNNINGHAME NORTH
574 CUNNINGHAME SOUTH
575 DUMBARTON
576 DUMFRIES
577 DUNFERMLINE EAST
578 DUNFERMLINE WEST
579 EAST KILBRIDE
580 EAST LOTHIAN
581 EASTWOOD
582 FALKIRK EAST
583 FALKIRK WEST
584 GALLOWAY & UPPER NITHSDAL
585 GORDON
586 INVERNESS NAIRN & LOCHABE
587 KILMARNOCK & LOUDOUN
588 KINCARDINE & DEESIDE
589 KIRKCALDY
590 LINLITHGOW
591 LIVINGSTON
592 MIDLOTHIAN
593 MORAY
594 FIFE NORTH EAST
595 TAYSIDE NORTH
596 ORKNEY & SHETLAND
597 PERTH & KINROSS
598 RENFREW WEST & INVERCLYDE
599 ROSS CROMARTY AND SKYE
600 ROXBURGH & BERWICKSHIRE
601 STIRLING
602 STRATHKELVIN & BEARSDEN
603 TWEEDDALE ETTRICK & LAUDE
604 WESTERN ISLES
605 ABERDEEN NORTH
606 ABERDEEN SOUTH
607 DUNDEE EAST
608 DUNDEE WEST
609 EDINBURGH CENTRAL
610 EDINBURGH EAST
611 EDINBURGH LEITH
612 EDINBURGH PENTLANDS
613 EDINBURGH SOUTH
614 EDINBURGH WEST
615 GLASGOW CATHCART
616 GLASGOW CENTRAL
617 GLASGOW GARSCADDEN
618 GLASGOW GOVAN
619 GLASGOW HILLHEAD
620 GLASGOW MARYHILL
621 GLASGOW POLLOK
622 GLASGOW PROVAN
623 GLASGOW RUTHERGLEN
18705 13881
3217
6162 29528
0
20980 12628
4060
8135 26304
0
6412 30104
0
10640 34021
0
3001
9056
6374
7548 25630
0
22970 17173
0
0
7521 16021
9467 30581
0
27177 31537
0
Did not exist until 1979
10751 33275
0
0 27002
0
11718 28881
0
3711 21859
0
9800 26372
0
27085 19722
0
16286 27945
0
3333 13986
9413
29409 20224
0
Did not exist until 1974
22482 25722
0
21080 26042
0
10726 28198
0
21626 22647
0
3019
3703
0
4101
0
6398
5243
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
45846
50333
46529
50193
44753
64858
27472
42933
49252
31983
51422
71989
17795
6584
20446
9837
6817
10128
0
7885
25422
8176
10263
27477
13260
30889
12925
27441
30697
34625
9095
26608
19165
6950
30469
31125
3845
0
4319
0
0
0
8119
0
0
10970
0
0
2852
3367
1594
3744
0
7176
5127
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1962
0
45660
49124
47490
49480
44890
64048
26435
42153
53628
31152
51711
71342
0
0
0
0
0
0
5139
2183
0
0
25410
0
4544
0
0
0
0
0
55976
62381
54214
39059
46920
56003
64788
35980
61352
11905
22301
13896
3629
8903
30313
17144
7005
28737
35170
27623
29853
20565
26755
20790
30101
13249
18054
0
0
0
0
0
0
0
3796
0
0
2253
0
2776
2519
0
6579
4121
2553
0
0
0
4580
0
0
0
0
408
57192
62372
53903
37908
47452
60206
66150
36281
59986
0
0
0
0
0
0
0
0
60767
61446
54010
58923
26047
22258
9754
24043
26915
25390
29884
23640
0
0
0
0
0
0
4651
0
0
0
0
0
64574
59524
55301
58045
23967
8996
0
19006
9091
0
20006 13866
0
18600
8543
0
10453
5364
2674
13569 21778
0
10638 26849
0
16579 23588
0
Did not exist until 1974
Did not exist until 1974
Did not exist until 1979
20338 11183
0
19713 19546
0
19902 21854
0
24550 15472
0
Did not exist until 1979
14170 22146
0
21828 20870
0
21739 19029
0
30959 14371
0
Did not exist until 1979
19345 20651
0
15893
7879
0
20216
9288
4705
14352
6891 13386
14983 23324
0
18516
8323
0
16392 23861
0
17347 25654
0
Did not exist until 1979
17208 25994
0
14667
9538
0
26104 10872
0
18133
5975
0
3760
2914 11753
22948
8313
0
21283 17243
0
9929
6003
0
Did not exist until 1979
15721 18935
0
23086 24216
0
21925
9296 14755
6315
8487
0
16357 33153
0
26817 19627
0
21606 25646
0
24208 26082
0
15796 16735
0
19198 21240
0
10693 16337
0
23496 16011
0
24836 11949
0
26000 12784
0
25265
9514
0
11839 22567
0
18654 18226
0
15216 24818
0
20106
9648
0
12536 21174
0
23975 15130
0
15353 15533
0
19141 17040
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5389
0
44796
42182
43931
45423
26619
46007
53417
50342
19435
16599
19659
18982
0
14105
11257
17132
6477
7356
16303
10980
6438
24774
25554
25004
8139
4469
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12163
0
8714
0
44840
40015
45444
44628
26716
48063
55992
52200
0
0
0
0
0
0
0
0
44065
47112
49217
54285
20270
20225
19964
25867
12218
21901
22105
18437
0
0
0
0
0
0
0
0
0
0
0
0
44291
48596
50277
57212
0
0
0
0
0
0
0
0
46827
49726
50764
58024
14744
24631
22472
29672
23478
25171
19622
14579
0
0
0
6339
0
0
0
0
0
0
0
0
47737
57094
50569
61060
2885
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
53832
34396
47125
51197
47256
37146
53553
57045
19797
15454
22937
15728
15087
17536
14186
18083
22423
5590
10542
8073
25379
8486
25428
27454
0
6412
0
11653
0
0
4020
0
2983
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
55759
36296
46429
49546
49090
36513
54232
58457
0
0
0
0
0
9227
0
0
0
0
0
0
0
0
0
0
55307
35663
50522
34219
27868
55054
46407
25746
18797
13742
26585
16256
3487
24217
20959
7813
28457
6539
11421
4008
3275
7781
18206
4815
0
5831
0
0
12099
0
0
3918
0
0
0
3568
0
9637
0
0
0
0
0
0
0
0
0
0
58092
35487
50537
33582
26435
55064
47395
25350
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2448
0
0
0
0
0
1335
0
0
6055
0
0
0
0
2491
0
0
0
0
0
0
0
43098
61003
56907
24856
66385
57291
57403
62415
47251
53655
42531
51119
47626
51256
45969
56627
46370
55743
40802
48197
51800
41326
43016
15497
24553
22275
7496
15137
25471
22082
25143
15232
21932
12018
25742
22799
25976
30743
10072
21320
13319
20094
12311
24338
17241
19146
21008
27942
9336
8663
32793
17349
26263
25857
15849
22244
15092
16950
11285
14044
21169
20731
24690
23139
9317
21893
17072
21608
17624
0
0
12762
0
0
4558
0
0
0
0
4475
0
5505
5962
0
0
0
0
0
0
0
0
0
0
0
0
0
2964
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2200
0
0
0
0
0
1086
0
0
0
0
0
0
0
1932
0
1869
0
0
0
0
0
43686
64961
55459
25178
66351
58086
58537
62804
42781
54756
39750
53178
48767
57293
64703
48004
56278
51084
38154
46422
52472
49284
42833
Page 7
Appendix C
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
GLASGOW SHETTLESTON
15645 21464
0
GLASGOW SPRINGBURN
10358 16131
0
GREENOCK & PORT GLASGOW
18345 19378
0
HAMILTON
12661 26187
0
MONKLANDS EAST
17605 22269
0
MONKLANDS WEST
14784 20307
0
MOTHERWELL NORTH
19755 23365
0
MOTHERWELL SOUTH
17262 20147
0
PAISLEY NORTH
Did not exist until 1979
PAISLEY SOUTH
20725 26823
0
The following constituencies did not exist after the 1979 general election
ABERTILLERY
4081 25599
0
BANFF
14643
6337
0
BATTERSEA,SOUTH
15044 14365
2219
BIRMINGHAM,HANDSWOR
24349 14064
0
BRISTOL,NORTH EAST
21864 22740
4236
CONSETT
15224 30979
0
DARWEN
26729 18813
0
EDINBURGH NORTH
20425 12664
0
GLASGOW CENTRAL
10307 16674
0
GLASGOW CRAIGTON
19120 18910
0
GLASGOW KELVINGROVE
14854 11966
0
HACKNEY CENTRAL
15212 27012
0
HARROW,CENTRAL
23996 15955
0
HUDDERSFIELD,WEST
0 16418 24345
ISLINGTON CENTRAL
12910 19612
0
LAMBETH CENTRAL
15559 20594
0
LEEDS,SOUTH EAST
13142 25714
0
LIVERPOOL,KIRKDALE
22356 20542
0
LIVERPOOL,SCOTLAND
11821 21928
0
MANCHESTER,OPENSHAW
16596 24638
0
NEWCASTLE/TYNE CENT
13099 26102
0
RIPON
21977
9912
0
SALFORD,WEST
21554 22413
0
ST.MARYLEBONE
26302 10903
0
ST.PANCRAS NORTH
17588 24670
0
STOCKPORT SOUTH
20698 16612
0
WOOD GREEN
21811 25523
0
The following constituencies did not exist after the 1970 general election
BARONS COURT
20623 20748
0
BIRMINGHAM,ALL SAIN
17560 18867
0
BRADFORD,EAST
14713 23588
0
BRISTOL,CENTRAL
16406 25158
0
CENTRAL NORFOLK
21851 16288
0
EALING,SOUTH
25992 13462
4182
EAST HAM,SOUTH
11109 19808
0
ENFIELD,EAST
13957 21658
0
GLASGOW BRIDGETON
12375 20476
0
GLASGOW WOODSIDE
19846 15543
0
HESTON AND ISLEWORT
25705 19193
0
KENSINGTON,SOUTH
32051
6804
0
LEICESTER,SOUTH WES
16998 21487
0
LEWISHAM,SOUTH
17478 23821
0
LIVERPOOL,EXCHANGE
12271 19457
0
MANCHESTER,EXCHANGE
12922 20203
0
MERTON AND MORDEN
25373 18983
0
PADDINGTON,SOUTH
18479 11432
0
POPLAR
5814 25642
0
RHONDDA,WEST
3134 21288
0
SEDGEFIELD
18368 27221
0
SOUTHWARK
10944 28174
0
STOCKTON-ON-TEES
19607 23422
0
TORRINGTON
20124 10812
0
WALTHAMSTOW,EAST
16873 15744
3882
WANDSWORTH,CLAPHAM
22173 22398
0
WEDNESBURY
17120 26064
0
WEMBLEY,SOUTH
22052 15596
0
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
NORTHERN IRELAND
689 ANTRIM EAST
690 ANTRIM NORTH
691 ANTRIM SOUTH
692 BELFAST EAST
693 BELFAST NORTH
694 BELFAST SOUTH
695 BELFAST WEST
696 DOWN NORTH
697 DOWN SOUTH
698 FERMANAGH AND SOUTH TYRONE
699 FOYLE
700 LAGAN VALLEY
701 LONDONDERRY EAST
702 NEWRY AND ARMAGH
703 STRANGFORD
704 ULTER MID
705 UPPER BANN
Did not exist until 1979
41763
0
50347
0
26938 13041
33745 15065
33392
7508
34191
0
50315
0
37921
0
30268
0
35673
0
Did not exist until 1979
Did not exist until 1979
38617
0
Did not exist until 1979
29477
0
Did not exist until 1979
0
0
0
0
0
0
0
0
0
1532
0
0
0
0
0
0
53533
40537
48400
51066
50220
43050
54628
48875
14743
10167
8616
11510
21953
14883
20767
17613
22916
16297
19320
27423
22747
21152
25119
22009
0
0
10238
0
0
0
0
0
0
0
0
2586
0
0
0
0
0
1235
0
0
0
0
0
1331
49987
38147
48366
51995
53223
43505
55845
50503
0
0
62376
21250
28519
0
0
0
63097
1259
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2148
0
0
0
0
0
0
0
1530
0
0
0
0
0
0
0
0
0
0
0
0
1303
0
0
39111
38378
39239
58322
62614
59607
55322
45952
42068
48067
39672
66183
50344
51427
49230
53045
57211
60738
56176
56720
55309
40451
58701
57648
62739
47251
63015
4740
14359
14203
23243
24258
16037
27483
19991
8712
19047
12355
15905
23813
0
13097
16005
12146
22416
12384
16537
12485
22757
20306
23278
15949
20522
21735
26934
5992
12451
13116
21574
32307
19141
11235
15918
19649
11254
25407
14049
15621
17766
18117
21795
19669
20051
24975
24051
9791
23167
8507
22257
17982
22869
0
0
2774
0
5030
0
0
0
0
0
0
0
0
25273
0
0
0
0
0
0
0
0
0
4304
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1867
0
0
0
0
0
0
740
0
0
0
0
0
0
0
0
0
0
0
0
0
2915
0
0
38674
32129
37320
55596
64319
59206
55461
42270
36540
46768
34319
62561
47635
51284
48613
52262
48457
57102
51914
54610
49929
41184
56490
55080
59194
47265
59380
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4424
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2619
0
0
0
0
1400
2928
0
0
0
0
0
0
959
0
0
0
0
0
0
54613
51562
51453
56326
49268
56046
42280
47197
53733
48632
56883
62724
50602
54981
55458
52376
54332
46505
49038
35943
57031
66592
51366
44712
45169
59015
59260
46650
18658
17235
14529
17209
21918
24761
11422
16477
12139
16567
24486
26606
14652
19273
11945
10604
25603
16006
6635
3242
21771
12696
20684
17283
16622
22266
17464
19733
17745
17215
20056
19905
15131
12039
18230
20101
21048
14483
15636
4525
17395
22354
18916
19328
17444
8719
22506
21130
30642
25036
23961
5633
13721
20390
24147
12166
0
0
0
0
6465
4842
0
0
0
2583
4867
4666
5438
0
0
0
0
0
0
0
0
0
0
15018
4974
0
4780
5403
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4978
0
0
0
0
0
0
0
0
1766
0
0
0
0
0
0
0
0
0
0
0
0
788
0
0
0
0
0
0
0
1395
0
0
183
0
0
0
50032
48611
47514
49476
54436
53296
39764
47183
48473
44746
55121
58023
47762
53962
51052
46072
52178
40951
44412
34450
63535
61747
53224
44029
43892
55894
60297
45150
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6809
5155
3156
4534
1679
24497
1637
19624
30529
19640
67315
84939
61258
76990
64844
78589
84968
77832
65770
71297
42807
52786
26510
32173
30164
28898
51773
36875
32080
37529
0
0
16412
18640
9318
0
0
0
0
0
0
0
0
0
3253
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2280
2745
1204
2156
434
24478
1039
6928
7348
13872
69880
93634
58663
74494
59864
73405
89886
77627
64022
73241
0
0
21363
72492
40325
0
0
0
6823
73416
0
0
29737
66847
33093
0
0
0
14170
67647
Page 8
Appendix C
British General election results 1955 to 1987
Political party and electoral roll Conservativ Labour
Parliamentary constituency area
1964
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
BARROW & FURNESS
BERWICK-UPON-TWEED
BISHOP AUCKLAND
DURHAM CITY OF
COPELAND
EASINGTON
HEXHAM
LANGBAURGH
DURHAM NORTH
DURHAM NORTH WEST
PENRITH & THE BORDER
SEDGEFIELD
WANSBECK
WESTMORLAND & LONSDALE
WORKINGTON
BLAYDON
BLYTH VALLEY
CARLISLE
DARLINGTON
GATESHEAD EAST
HARTLEPOOL
HOUGHTON & WASHINGTON
JARROW
MIDDLESBROUGH
NEWCASTLE UPON TYNE CENTL
NEWCASTLE UPON TYNE EAST
NEWCASTLE UPON TYNE NORTH
REDCAR
SOUTH SHIELDS
STOCKTON NORTH
STOCKTON SOUTH
SUNDERLAND NORTH
SUNDERLAND SOUTH
TYNE BRIDGE
TYNEMOUTH
WALLSEND
BARNSLEY WEST & PENISTONE
BEVERLEY
BOOTHFERRY
BRIDLINGTON
BRIGG & CLEETHORPES
CALDER VALLEY
COLNE VALLEY
DEWSBURY
DONCASTER NORTH
DON VALLEY
ELMET
HARROGATE
HEMSWORTH
KEIGHLEY
NORMANTON
PONTEFRACT & CASTLEFORD
RICHMOND (YORKS)
ROTHER VALLEY
RYEDALE
SCARBOROUGH
SELBY
SHEFFIELD HALLAM
SHEFFIELD HILLSBOROUGH
SHIPLEY
SKIPTON & RIPON
WENTWORTH
BARNSLEY CENTRAL
BARNSLEY EAST
BATLEY & SPEN
BRADFORD NORTH
BRADFORD SOUTH
BRADFORD WEST
DONCASTER CENTRAL
GLANFORD & SCUNTHORPE
GREAT GRIMSBY
HALIFAX
HUDDERSFIELD
HULL EAST
HULL NORTH
HULL WEST
LEEDS CENTRAL
LEEDS EAST
LEEDS NORTH EAST
LEEDS NORTH WEST
LEEDS WEST
LEEDS SOUTH & MORLEY
PUDSEY
ROTHERHAM
SHEFFIELD ATTERCLIFFE
Liberal
Nationalis Other par Registered
electors
Conservativ Labour
Liberal
Nationalis Other par Registered
1966
electors
18068
15851
13782
15209
15440
8270
22468
24124
10851
11280
21228
22197
8218
22310
32818
23267
34028
14127
28596
32895
26006
10490
0
7681
0
0
0
0
7722
11387
0
0
9279
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
51601
39915
47338
60984
47193
56229
54122
76693
55076
47812
50840
15453
14281
11936
13383
13935
7350
20889
22423
9720
9070
20982
23485
9908
22015
32200
22726
32097
16105
34303
32467
25260
12081
0
5796
0
0
0
0
6434
7229
0
0
6757
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50711
39155
46256
61021
46532
55923
55320
78832
56345
46789
50621
9805
19125
15565
12932
11832
17049
19841
14654
23016
11076
14503
12917
19502
19556
21149
26114
6752
25522
25296
37336
19169
21751
26633
25883
32914
26053
29432
12515
21200
29603
0
11078
0
0
0
4617
6578
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1227
0
0
0
0
0
0
0
0
0
44451
46888
49220
48566
62805
49121
58051
51705
59703
55897
50668
58062
42331
48886
63943
8698
17907
14475
11849
10179
17638
19546
12084
18857
9304
12449
9420
15243
15082
18002
25223
8465
24981
26629
36493
22565
23909
27628
27509
32067
26006
28404
12550
22408
30219
0
9052
0
0
0
0
3891
0
0
0
0
0
2902
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
44096
46944
49078
49682
62767
48144
57557
52442
59079
56001
50158
55407
40900
46663
63628
16344
29694
7837
0
0
72697
17340
31829
0
0
0
71578
18759
18195
24334
9623
33342
26096
13095
26131
19367
22729
21227
18220
7207
15046
22732
9069
27897
24474
8668
15115
10785
10128
25345
14813
28272
22632
15435
23719
13278
19076
18561
19904
24024
25900
21390
25894
39841
29784
10360
7974
9002
14188
21582
18537
21284
23845
38101
19533
8655
42528
17816
28477
32357
8908
43101
14315
11818
25256
11635
22071
15545
11715
5816
0
0
0
0
0
8372
9986
9067
8494
7949
0
18350
7679
0
0
0
9332
0
8529
0
0
8787
0
0
14725
0
7807
0
5165
9886
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1157
0
0
0
0
0
0
0
0
0
0
0
0
1201
0
0
0
0
0
0
0
0
0
0
0
1165
0
0
0
0
52905
56856
66239
41388
75017
80863
63196
58501
47973
55268
55677
50065
52006
55341
57746
59617
58246
55141
64957
48415
50655
54774
56926
74833
57697
65611
54050
58226
47478
45905
47827
19756
16423
20398
6878
29210
21205
11817
25566
17701
21976
19977
16361
3786
12361
19689
6121
28183
22932
7165
18027
9084
8927
23541
13167
25089
21141
13969
21593
10774
18466
17532
23649
25438
27567
20381
25814
39744
31419
13017
9421
11939
15885
21591
19507
23027
25777
36735
21841
9267
41887
22039
29416
32328
10210
43634
15647
11848
26117
13663
22799
16966
13276
0
0
0
0
0
0
7191
8277
7885
6349
7222
0
22006
7593
0
0
0
9518
0
0
0
0
7824
0
0
15599
0
6799
0
4304
8104
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3846
0
0
0
0
0
0
0
0
0
0
2170
0
0
0
0
0
0
0
0
0
429
952
0
0
0
0
53276
56197
63554
38878
75044
78666
63943
61232
48768
56333
57946
48834
52555
55180
56013
58721
62650
56021
64521
48006
51807
54551
58315
77320
57967
66143
56112
56078
47788
45895
47448
9417
37250
9089
0
0
69658
12456
38744
0
0
0
69751
19812
16507
17097
21121
16593
22674
21577
22085
12232
14284
19483
18825
12123
21474
23613
29859
15697
13477
21581
13907
10223
20734
17905
21004
17974
42452
29480
25675
23143
20501
30634
20664
24855
22339
29480
15288
18862
22968
23362
16100
27585
30318
6411
6642
7286
0
0
7088
0
7664
7494
9781
7570
0
0
0
0
8728
6787
7564
8732
0
4831
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
928
0
0
0
0
0
0
0
0
55063
51062
57768
50912
73120
76420
62263
64455
51193
73151
61783
61577
49151
66944
54740
74417
60973
56436
54939
57937
63046
21216
17528
15435
18170
14738
22391
18662
19687
11081
11385
17871
14551
9813
18796
20813
30168
13883
12435
20782
11925
9511
25740
21727
22932
19704
43973
33699
26788
25391
21960
34457
26640
26816
23171
30073
15851
24044
24391
24086
18410
27402
32336
0
0
5291
0
0
0
0
5423
6303
6795
3747
0
0
0
0
0
5062
6366
7353
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
714
0
0
0
0
0
0
0
0
55925
50512
57456
49440
74946
77484
61270
62754
50509
71694
61112
58743
49474
67189
53824
74191
60176
56936
55860
57229
61889
Page 9
1
2
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5
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7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
Appendix C
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
SHEFFIELD BRIGHTSIDE
SHEFFIELD CENTRAL
SHEFFIELD HEELEY
WAKEFIELD
YORK
AMBER VALLEY
ASHFIELD
BASSETLAW
BLABY
BOLSOVER
BOSWORTH
BROXTOWE
CORBY
DAVENTRY
LINDSEY EAST
EREWASH
GAINSBOROUGH & HORNCASTLE
GEDLING
GRANTHAM
HARBOROUGH
HIGH PEAK
HOLLAND WITH BOSTON
KETTERING
LOUGHBOROUGH
MANSFIELD
NEWARK
DERBYSHIRE NORTH EAST
LEICESTERSHIRE NORTH WEST
RUSHCLIFFE
RUTLAND & MELTON
SHERWOOD
DERBYSHIRE SOUTH
STAMFORD & SPALDING
WELLINGBOROUGH
DERBYSHIRE WEST
CHESTERFIELD
DERBY NORTH
DERBY SOUTH
LEICESTER EAST
LEICESTER SOUTH
LEICESTER WEST
LINCOLN
NORTHAMPTON NORTH
NORTHAMPTON SOUTH
NOTTINGHAM EAST
NOTTINGHAM NORTH
NOTTINGHAM SOUTH
BURY ST. EDMUNDS
SUFFOLK CENTRAL
GREAT YARMOUTH
HUNTINGDON
NORFOLK MID
CAMBRIDGESHIRE NORTH EAST
NORFOLK NORTH
NORFOLK NORTH WEST
CAMBRIDGESHIRE SOUTH EAST
NORFOLK SOUTH
SUFFOLK SOUTH
CAMBRIDGESHIRE SOUTH WEST
NORFOLK SOUTH WEST
SUFFOLK COASTAL
WAVENEY
CAMBRIDGE
IPSWICH
NORWICH NORTH
NORWICH SOUTH
PETERBOROUGH
BARKING
BATTERSEA
BECKENHAM
BETHNAL GREEN & STEPNEY
BEXLEYHEATH
BOW & POPLAR
BRENT EAST
BRENT NORTH
BRENT SOUTH
BRENTFORD & ISLEWORTH
CARSHALTON & WALLINGTON
CHELSEA
CHINGFORD
CHIPPING BARNET
CHISLEHURST
CROYDON CENTRAL
CROYDON NORTH EAST
CROYDON NORTH WEST
CROYDON SOUTH
DAGENHAM
DULWICH
EALING ACTON
EALING NORTH
9963
7816
29587
14385
26521
13542
12989
19167
32905
8131
19583
27936
27317
24196
27883
26315
25428
33924
34841
27612
20389
31234
25334
25137
0
0
0
6753
7565
8930
0
0
13533
0
10652
0
0
0
0
0
0
0
0
0
0
0
0
0
1356
0
0
0
0
0
0
0
0
0
0
0
54927
46633
75582
60863
71719
68796
61960
60752
80122
49900
67114
63606
7476
5017
27267
15299
26067
15582
11991
17195
32450
6815
19654
25243
26653
24550
31996
28907
32167
36522
33477
27623
25453
31114
27427
25623
0
0
0
0
0
0
0
0
12475
0
7526
5085
0
0
0
0
0
0
0
0
0
0
0
0
989
1022
0
0
0
0
0
0
0
0
0
0
53015
47165
75345
60245
70431
68378
62030
61154
86198
49491
68462
65493
24823
15854
28655
19235
27896
27634
20916
8044
29528
12126
21546
21770
0
8069
0
8930
10034
0
0
0
0
0
0
0
0
0
0
0
0
0
55080
42778
70245
51499
71285
62677
25023
15090
26911
18770
28635
24748
22332
9715
32407
14904
24589
22590
0
7552
0
6064
9570
4503
0
0
0
0
0
0
0
0
0
0
0
0
57626
42625
72551
52428
75931
64423
15753
29082
29405
14416
23451
36210
11147
0
0
0
0
0
0
0
0
48445
71064
80469
16124
26683
23877
16938
26367
35337
7990
0
7903
0
0
0
0
0
0
48747
70765
82509
10021
21975
21564
17671
23594
32842
29055
26171
38657
22081
21046
19578
6628
0
0
6558
6690
11392
0
0
0
0
0
0
0
0
0
0
0
0
58698
57906
77285
55193
65663
77285
9987
20913
19123
16911
24264
30776
28849
27402
38723
22935
24580
23181
5483
0
0
5875
0
10108
0
0
0
0
0
0
590
0
0
0
0
0
60024
59492
78331
55583
64597
79616
24169
18720
19545
16825
14944
13991
16420
12195
23236
16740
15015
24128
30481
14990
19592
9669
29452
21386
22432
15494
11090
21134
19737
28568
9807
0
7227
11559
7738
5057
0
5712
7205
0
6519
5557
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
74891
42514
53224
44344
66138
54318
51944
43107
54143
49707
51420
73129
30221
17991
22472
18383
13443
14215
11857
10769
23615
14015
16469
24052
34495
15704
24705
13791
31542
23033
21433
17007
15819
21822
23006
31541
0
0
0
4874
6227
0
3966
3703
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
76914
43419
54566
44414
66748
52601
51348
41772
53803
48605
50165
72781
18912
20578
20763
25206
21555
20310
20320
21040
29535
23055
20216
16129
18381
12456
0
0
0
4840
9437
4680
6992
0
0
0
0
0
0
0
0
1579
0
0
0
0
0
55988
66477
53542
61143
57087
54000
50483
14922
18509
17311
27782
21044
21499
20504
21348
30260
23859
23140
17431
22296
15276
0
0
0
0
8661
0
5900
0
0
0
0
0
0
0
0
1070
0
0
0
0
0
53542
67321
52561
64549
57851
54836
53745
25317
19307
21356
24883
17178
26370
19692
19360
21460
17636
15012
17778
0
0
0
9347
4819
8044
0
0
0
0
0
0
0
0
0
0
0
0
61004
48488
53186
65015
44774
63785
21320
20059
21305
25600
16968
26689
19566
20796
23324
20433
16849
19680
5250
0
0
7698
4079
6839
0
0
0
0
0
0
0
0
0
0
0
0
60758
49108
53833
67869
46558
66329
16728
16605
0
0
427
41192
17880
17105
0
0
0
41651
23976
20720
22216
11620
17362
24045
8296
5847
30070
5593
25716
6466
18755
18325
12961
14019
26118
16802
31753
25537
21272
19331
24648
18111
17973
21428
23055
14930
13338
19914
21127
22284
20543
11960
23862
13475
16105
6868
34991
17024
4911
7723
14755
0
0
6181
5463
2187
12821
5296
6161
0
0
6805
0
2951
11207
3635
12093
10172
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
349
0
0
0
0
471
0
0
1263
2454
0
0
1130
0
0
0
0
706
60775
60365
78463
39886
42744
63181
50326
35659
72692
51904
64240
60807
57153
46037
59800
38467
66637
43515
94655
65493
24063
20972
23440
9851
15808
23944
7584
5350
28837
4925
26377
5049
14761
17497
10362
14031
24615
16377
31406
24833
23705
21963
30313
18777
19163
23941
22994
15522
14972
20178
24044
23098
21767
13290
24944
14638
18746
7674
38914
19347
4513
4928
6200
0
0
4093
4181
0
12155
3841
4405
0
2765
5587
0
2063
8988
3285
10162
8539
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
439
769
0
0
0
0
650
0
0
0
2209
556
0
1140
0
0
0
0
0
62881
60380
78351
38580
42045
63839
48281
34048
71952
50180
63885
59846
56085
44944
57276
37454
65971
43336
97645
65487
24854
19930
19577
31827
9461
19469
14423
20782
22265
16099
13967
9020
32851
22320
17022
20809
0
6567
8201
16049
7301
5627
3049
6532
0
0
0
0
0
0
0
0
0
0
0
0
1070
265
0
0
63443
56765
56122
71818
71424
64568
44557
59421
21415
18302
18578
30900
10530
18173
13600
21153
21496
17714
15882
9347
35055
24469
18541
23730
5146
6007
6466
16407
0
4458
0
3858
0
0
0
0
0
0
0
0
0
0
0
0
1373
0
0
0
63146
55094
55042
71417
69671
63891
43464
59315
Page 10
86
87
88
89
90
91
92
93
94
95
96
97
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99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
Appendix C
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
EALING SOUTHALL
EDMONTON
ELTHAM
ENFIELD NORTH
ENFIELD SOUTHGATE
ERITH & CRAYFORD
FELTHAM & HESTON
FINCHLEY
FULHAM
GREENWICH
HACKNEY NORTH & STOKE NEW
HACKNEY SOUTH & SHORED'H
HAMMERSMITH
HAMPSTEAD & HIGHGATE
HARROW EAST
HARROW WEST
HAYES & HARLINGTON
HENDON NORTH
HENDON SOUTH
HOLBORN & ST PANCRAS
HORNCHURCH
HORNSEY & WOOD GREEN
ILFORD NORTH
ILFORD SOUTH
ISLINGTON NORTH
ISLINGTON SOUTH & FINSBUR
KENSINGTON
KINGSTON UPON THAMES
LEWISHAM DEPTFORD
LEWISHAM EAST
LEWISHAM WEST
LEYTON
MITCHAM & MORDEN
NEWHAM NORTH EAST
NEWHAM NORTH WEST
NEWHAM SOUTH
NORWOOD
OLD BEXLEY & SIDCUP
ORPINGTON
PECKHAM
PUTNEY
RAVENSBOURNE
RICHMOND & BARNES
ROMFORD
RUISLIP - NORTHWOOD
SOUTHWARK & BERMONDSEY
STREATHAM
SURBITON
SUTTON & CHEAM
CITY OF LONDON & WESTMIN
TOOTING
TOTTENHAM
TWICKENHAM
UPMINSTER
UXBRIDGE
VAUXHALL
WALTHAMSTOW
WANSTEAD & WOODFORD
WESTMINSTER NORTH
WIMBLEDON
WOOLWICH
AYLESBURY
BANBURY
BEACONSFIELD
BILLERICAY
BRAINTREE
BRENTWOOD & ONGAR
BUCKINGHAM
CHELMSFORD
CHESHAM & AMERSHAM
EPPING FOREST
HARLOW
HARWICH
HENLEY
HERTFORD & STORTFORD
HERTSMERE
BEDFORDSHIRE MID
MILTON KEYNES
BEDFORDSHIRE NORTH
COLCHESTER NORTH
HERTFORDSHIRE NORTH
LUTON NORTH
OXFORD WEST & ABINGDON
ROCHFORD
SAFFRON WALDEN
ST ALBANS
COLCHESTER SOUTH & MALDON
BEDFORDSHIRE SOUTH WEST
HERTFORDSHIRE SOUTH WEST
STEVENAGE
16144
19245
20639
19612
22251
13951
14927
24591
14842
12592
10843
8412
10936
19888
20307
23132
13158
17784
18452
13117
30933
22590
24096
18152
8912
8023
12771
23973
8248
17144
18167
15714
25087
9524
6844
3835
17624
22251
19565
11226
23274
20417
22203
21046
21036
4568
19408
20499
22975
21588
18336
11577
27427
18041
24373
22420
8853
8787
22806
20733
12408
19788
22814
21777
18207
18547
18053
18048
10725
20018
16660
11441
15873
30699
18528
16563
16660
15525
17589
15283
13611
17676
16801
17281
23640
21175
14501
21228
23599
17173
20736
4609
20111
24581
9090
14053
27143
11331
17481
12085
13337
11839
11309
20581
19458
15231
0
5917
0
8885
9600
6189
6141
15789
0
5205
5324
0
0
8019
0
9055
0
5719
8430
0
12725
6015
10692
8547
3634
0
2819
7827
0
3798
5123
7598
6902
0
7005
4264
3929
7291
22637
0
6856
8650
7800
8133
7806
0
5261
0
8827
4087
4369
4526
12306
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3410
0
1112
635
0
0
0
0
632
0
0
0
0
0
0
0
873
0
0
226
0
1258
0
0
0
1377
0
0
2386
0
0
0
657
0
0
0
0
0
0
0
0
695
0
811
0
0
497
0
0
0
0
0
1073
53558
64348
54359
46882
53198
53967
54147
67522
48147
56741
65191
47791
46718
67990
47954
53756
46512
51137
52009
43272
90828
68691
66769
58066
51315
52373
50349
58884
47124
50624
54227
66905
67967
35656
55824
49574
57090
60678
54846
53925
71084
49915
57622
73473
49358
34845
51910
44846
58763
61988
58338
55644
72154
14642
18697
19256
20675
21171
15033
13932
23968
13094
13200
10221
5957
8857
20710
16996
22660
11883
18468
17176
10982
35373
21116
23736
18093
8357
5903
10749
22781
7033
17989
18984
18157
24234
7729
5527
3410
16830
21947
20993
8023
23114
20117
21831
23160
20731
3990
19872
19989
22331
19242
16331
11222
26512
19989
26422
23344
10518
9743
24243
22389
14504
20080
24359
24221
17456
19522
22963
17374
12313
20707
17868
13120
16128
38406
20501
20392
20613
16188
16206
16012
14915
17893
20352
21018
26803
23706
14911
21778
22902
19103
22757
4870
20630
26601
10290
15608
31221
13455
16605
16505
14561
13235
12349
22159
21111
18884
0
0
0
7202
8679
3827
5206
13070
0
0
0
0
0
5182
4749
7676
0
3503
7632
0
0
5026
6953
4606
2682
0
2462
6722
0
0
0
3851
4470
0
5882
3367
3256
5761
22615
0
5420
8060
6661
0
6128
0
0
0
8134
3576
3429
0
10160
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2768
0
906
0
0
556
0
0
1261
0
1491
1126
0
211
0
0
698
0
0
0
0
1184
0
0
0
2843
0
0
1906
0
0
441
580
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
52811
62520
53474
47940
52705
53466
53697
68422
45085
55477
64389
45883
44397
67798
47267
53210
45797
49784
52112
41366
90969
65740
66569
56302
50203
48995
47081
57705
44668
49532
52858
64727
66709
35016
53672
47990
54592
59895
55776
51526
69870
49533
55534
72089
49334
33811
51668
44894
57227
58630
56522
54079
70675
20519
8653
6780
19580
10639
15952
10303
23856
27281
33905
19866
15458
14405
6917
14607
8891
22158
16467
22159
14216
6644
0
4437
8901
0
5817
0
10301
7851
16151
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
534
0
58226
40745
35512
44612
38779
40947
47061
63262
70178
81466
20903
7648
5940
19063
7981
15191
8798
23673
28932
33997
21793
15233
14665
8785
14445
9517
22241
19766
24529
17005
5241
0
3370
6150
2287
5475
0
9272
7407
15348
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
58070
39042
33755
44256
37240
40248
46310
65968
74279
82728
21547
20016
5924
0
0
57020
22572
22066
5015
0
0
59616
27849
20816
9414
0
0
70158
28600
23625
8419
0
0
73535
20699
16978
6058
0
0
53398
20906
18338
5007
0
0
54443
25102
24898
14877
16614
9824
9081
0
0
0
0
66350
64617
24975
28994
18335
23320
9219
0
0
0
0
0
70597
69584
22414
21604
21404
23319
30649
17096
23085
18256
19780
34034
9184
5578
7712
7566
9564
0
0
0
0
0
0
0
0
0
0
58640
58109
58912
61742
87825
23447
22600
21879
24320
32483
20369
24854
22257
23305
42233
7138
4914
5080
5714
0
0
0
0
0
0
0
0
0
0
0
61923
60966
60352
64843
90840
20610
22063
15655
16672
5539
7231
0
0
0
0
50724
55658
20441
22260
17176
19428
5487
4977
0
0
0
0
52221
56247
33838
28308
33499
22237
0
11301
0
0
0
0
83307
74502
30319
28378
34549
25186
7484
8596
0
0
0
0
86403
74777
Page 11
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
Appendix C
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
WANTAGE
WELWYN HATFIELD
HERTFORDSHIRE WEST
WITNEY
WYCOMBE
BASILDON
BROXBOURNE
CASTLE POINT
LUTON SOUTH
OXFORD EAST
SOUTHEND EAST
SOUTHEND WEST
THURROCK
WATFORD
ALDERSHOT
ARUNDEL
ASHFORD
BASINGSTOKE
BEXHILL & BATTLE
CANTERBURY
CHICHESTER
DARTFORD
DOVER
BERKSHIRE EAST
HAMPSHIRE EAST
SURREY EAST
FAREHAM
FAVERSHAM
FOLKESTONE & HYTHE
GRAVESHAM
GUILDFORD
HASTINGS & RYE
HORSHAM
ISLE OF WIGHT
LEWES
MAIDSTONE
MEDWAY
KENT MID
SUSSEX MID
MOLE VALLEY
NEWBURY
NEW FOREST
THANET NORTH
HAMPSHIRE NORTH WEST
SURREY NORTH WEST
READING EAST
READING WEST
ROMSEY & WATERSIDE
SEVENOAKS
SHOREHAM
THANET SOUTH
SURREY SOUTH WEST
TONBRIDGE & MALLING
TUNBRIDGE WELLS
WEALDEN
WINCHESTER
WINDSOR & MAIDENHEAD
WOKING
WOKINGHAM
BRIGHTON KEMPTOWN
BRIGHTON PAVILION
CHERTSY & WALTON
CRAWLEY
EASTBOURNE
EASTLEIGH
EPSOM & EWELL
ESHER
GILLINGHAM
GOSPORT
HAVANT
HOVE
PORTSMOUTH NORTH
PORTSMOUTH SOUTH
REIGATE
SLOUGH
SOUTHAMPTON ITCHEN
SOUTHAMPTON TEST
SPELTHORNE
WORTHING
BRIDGWATER
CHRISTCHURCH
CIRENCESTER & TEWKESBURY
DEVIZES
FALMOUTH & CAMBORNE
HONITON
NORTHAVON
CORNWALL NORTH
DEVON NORTH
DORSET NORTH
WILTSHIRE NORTH
26707
29134
31119
20334
25161
26273
8627
8722
11986
0
0
0
0
0
0
69102
74450
82087
27749
32302
31742
24447
31508
29704
7703
0
9970
0
0
0
0
0
0
30877
35347
29749
33494
23028
22212
19775
25555
14615
18744
25797
21534
33755
21887
25293
23751
20783
16408
10423
30372
20224
13718
9330
10706
10088
0
0
8797
6296
14548
8094
5797
10066
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
567
0
0
0
0
0
0
75902
96762
76186
78364
59299
67011
55763
64132
71519
53543
66120
31577
38371
29618
31942
20605
21987
19125
25713
14094
19996
25672
24498
40013
24412
26208
23069
24412
18608
13856
31998
23832
16776
8037
7587
9501
7706
3049
6152
4495
10958
6648
0
10025
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
586
0
0
0
0
0
0
21026
26466
27240
26827
30222
22496
23697
32777
23603
11989
18490
8014
15211
10155
27371
24115
17954
8477
9531
8708
10264
9582
11912
9047
5843
13875
11338
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
645
292
54879
68698
58777
67639
70637
72305
64876
82375
57983
21362
26076
27056
27160
31358
22638
24040
34011
23933
13249
22417
9155
15372
13784
29547
27256
24437
10874
8121
8379
9957
11962
9714
7094
3981
12464
10931
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
32369
20279
23587
25326
24277
16902
18321
24243
14314
26074
13365
11324
11684
4882
0
6015
10052
9716
0
0
0
0
0
0
0
0
0
0
0
0
82053
60500
53388
71408
60714
49701
32752
23886
22964
25484
24116
15324
21726
26375
15562
30276
15771
12984
8849
0
0
5092
7992
9744
0
0
0
0
0
0
0
0
0
0
0
0
27497
26818
25079
25148
16244
12757
17143
26161
7666
8924
11244
0
0
0
0
0
0
0
0
0
69215
63452
68539
67139
25862
27529
29208
25692
15411
14561
24214
27938
11915
9328
0
0
0
0
0
0
0
0
0
0
23862
24936
9806
18943
8773
11124
0
0
476
0
54032
69338
23087
25908
12201
21762
7629
9571
0
0
0
0
20815
27884
28678
36943
27870
21382
20805
12924
14958
15624
20520
8500
5759
11497
11480
11671
9979
11876
0
0
0
0
0
0
0
0
0
0
0
0
59371
68955
68820
84026
78664
53054
21205
27292
28651
36913
29302
21028
25338
14260
18338
18817
24416
9988
3127
11757
9746
10816
7952
12036
0
0
0
0
0
0
0
0
0
0
0
0
27802
29094
21502
25274
31170
19037
10859
12495
13632
17834
9682
14753
6510
11336
11285
0
0
0
0
0
0
0
0
0
0
71789
70172
50786
65770
77207
26896
31595
21162
25630
32057
20068
11938
12485
17300
19210
10586
13611
7390
8744
11104
0
0
0
0
0
0
0
0
0
0
22301
20998
22497
32318
26410
23429
31959
33226
20228
22308
11148
14513
22450
12034
21341
12131
13644
14584
0
7362
8844
12570
15441
6685
13968
12259
4052
0
0
0
0
0
0
0
0
0
0
0
0
865
0
0
0
0
527
61820
56391
58960
85816
70251
61334
72626
74669
50471
24105
22687
22584
32139
26039
24337
31434
32649
20158
24936
16333
16231
26098
12620
23636
13841
15023
17018
0
0
7852
11930
16746
5617
12305
11310
3546
0
0
0
0
0
0
0
0
0
0
0
0
0
883
0
0
0
0
33208
32923
18762
24387
24380
22681
18974
25700
22230
30203
20822
23365
15214
18265
13904
14991
22670
28949
25352
16797
7976
14645
12212
0
0
0
11058
0
7007
0
8252
11320
9009
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2038
91491
69143
49517
53915
63240
56725
72170
66572
59000
64931
57941
34446
28799
17458
22713
24163
21890
0
22188
22473
29903
20850
26197
12909
18685
14738
16649
26553
0
24628
19986
10281
17864
10540
8037
0
0
10197
0
0
4102
6624
8955
8205
0
0
0
0
0
0
0
0
0
0
0
0
574
0
0
0
0
35680
0
0
1044
0
24786
21118
15921
26475
26504
16352
13895
19898
18089
15518
17170
18847
9273
22790
3497
4603
6253
10086
7790
6881
7559
12354
8611
15683
19031
14768
16546
0
0
0
0
0
0
0
0
0
0
0
0
0
0
265
0
0
0
61626
55514
54569
61067
68781
43076
44510
50065
55071
27690
21429
18131
26966
28224
16952
15631
20520
18275
19919
18832
21394
13257
26800
2647
6127
7090
10257
0
7730
6144
9342
7421
18460
16797
15005
17581
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Page 12
72575 266
76234 267
84310 268
269
79426 270
102198 271
79322 272
85151 273
59725 274
66303 275
54879 276
64487 277
72502 278
52888 279
69612 280
281
56669 282
72397 283
60965 284
71604 285
74951 286
73359 287
65664 288
89627 289
60404 290
291
84033 292
62896 293
54573 294
74175 295
60815 296
49802 297
298
70877 299
67413 300
71882 301
68661 302
303
304
54296 305
72348 306
307
308
309
310
311
59132 312
71884 313
71644 314
87743 315
81498 316
53814 317
318
74464 319
74383 320
52697 321
67694 322
80887 323
324
61250 325
55532 326
59844 327
88872 328
72870 329
63992 330
72684 331
75593 332
51874 333
334
96166 335
69807 336
47247 337
52941 338
63687 339
56795 340
72846 341
65174 342
60676 343
66279 344
58515 345
346
63568 347
59237 348
55323 349
63047 350
74023 351
43480 352
45192 353
51885 354
54717 355
Appendix C
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
ST IVES
SALISBURY
SOMERTON & FROME
DORSET SOUTH
CORNWALL SOUTH EAST
SOUTH HAMS
STROUD
TAUNTON
TEIGNBRIDGE
TIVERTON
DEVON WEST & TORRIDGE
TRURO
WANSDYKE
WELLS
WESTBURY
DORSET WEST
GLOUCESTERSHIRE WEST
WESTON-SUPER-MARE
WOODSPRING
YEOVIL
BATH
BOURNEMOUTH EAST
BOURNEMOUTH WEST
BRISTOL EAST
BRISTOL NORTH WEST
BRISTOL SOUTH
BRISTOL WEST
CHELTENHAM
EXETER
GLOUCESTER
KINGSWOOD
PLYMOUTH DEVONPORT
PLYMOUTH DRAKE
PLYMOUTH SUTTON
POOLE
SWINDON
TORBAY
BROMSGROVE
BURTON
CANNOCK & BURNTWOOD
HEREFORD
LEOMINSTER
LUDLOW
MERIDEN
STAFFORDSHIRE MID
WORCESTERSHIRE MID
SHROPSHIRE NORTH
WARWICKSHIRE NORTH
NUNEATON
RUGBY & KENILWORTH
SHREWSBURY & ATCHAM
STAFFORDSHIRE SOUTH EAST
STAFFORDSHIRE SOUTH
WORCESTERSHIRE SOUTH
STAFFORD
STAFFORDSHIRE MOORLANDS
STRATFORD-ON-AVON
WARWICK & LEAMINGTON
WYRE FOREST
ALDRIDGE-BROWNHILLS
BIRMINGHAM EDGBASTON
BIRMINGHAM ERDINGTON
BIRMINGHAM HALL GREEN
BIRMINGHAM HODGE HILL
BIRMINGHAM LADYWOOD
BIRMINGHAM NORTHFIELD
BIRMINGHAM PERRY BARR
BIRMINGHAM SELLY OAK
BIRMINGHAM SMALL HEATH
BIRMINGHAM SPARKBROOK
BIRMINGHAM YARDLEY
COVENTRY NORTH EAST
COVENTRY NORTH WEST
COVENTRY SOUTH EAST
COVENTRY SOUTH WEST
DUDLEY EAST
DUDLEY WEST
HALESOWEN & STOURBRIDGE
NEWCASTLE UNDER LYME
SOLIHULL
STOKE-ON-TRENT CENTRAL
STOKE-ON-TRENT NORTH
STOKE-ON-TRENT SOUTH
SUTTON COLDFIELD
WREKIN THE
WALSALL NORTH
WALSALL SOUTH
WARLEY EAST
WARLEY WEST
WEST BROMWICH EAST
14040
20071
20663
21209
14910
9265
14311
15080
20274
4172
9641
7176
12132
7100
18046
0
0
0
0
0
0
0
0
0
0
43890
52865
58588
59963
44906
14312
22601
20528
22997
16121
10713
18462
16989
21120
4674
9593
0
10224
5862
18144
0
0
0
0
0
0
0
0
0
0
44419
53895
58609
61700
46115
21802
21367
25417
19280
19493
18328
18889
16619
14542
7397
7226
14224
8747
7944
12297
14053
14093
12575
0
0
0
0
0
0
0
0
0
0
0
0
57906
54202
66292
50854
52124
56980
21804
22369
25623
20351
21644
18701
20259
19216
16900
11325
8902
17093
8397
5460
11066
10225
13461
10450
0
0
0
0
0
0
0
0
0
0
0
0
58779
55108
67466
51907
54045
58362
19950
17841
15300
27143
27814
21919
20255
26852
26114
19282
22129
15282
21230
19797
18035
15514
15049
10631
22420
12248
23896
17171
16464
10447
13975
29117
21030
26569
7306
14557
16673
19631
11232
8242
7191
11771
8253
12426
8795
10304
11681
0
5883
0
7366
7568
8815
7581
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
318
0
0
0
0
0
709
0
0
380
55847
44951
56407
64762
70186
61838
56806
63750
70238
62150
59025
57449
50052
54120
54176
54905
20989
17709
15476
27733
28824
22664
19344
27047
25740
19435
23526
12998
19783
22683
18613
15678
18192
11757
23181
15340
26526
20584
18544
12598
16334
30851
24195
26552
8265
19768
22199
20951
8962
7676
6137
10173
6745
9248
7095
8698
9389
0
0
0
6850
0
4869
6540
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
595
0
0
0
0
0
58341
45452
57443
67348
72803
62771
55891
64706
70141
66034
58894
56915
48361
54964
54624
55703
24241
24722
20615
24312
0
7383
0
0
0
0
60959
73591
22760
26345
22441
31567
0
0
0
0
0
0
59313
73398
24440
16651
28801
29616
25236
23334
17780
15238
17290
29062
16158
26464
12530
22673
22161
31608
12020
5750
10763
29425
12234
0
13652
8485
0
0
9322
8941
8768
0
0
0
0
0
0
0
0
0
0
0
0
944
0
0
0
0
0
0
0
0
66000
58923
73276
73272
59083
72149
49462
38805
47482
70085
25451
15523
28693
32400
23773
22594
17529
15045
19603
29250
19630
25966
16594
28704
23496
33621
14782
6536
16123
33831
8394
0
12750
0
0
0
6996
7647
0
0
0
0
0
0
0
0
0
0
0
0
0
838
0
0
0
0
0
0
0
0
67687
57582
74985
76220
60034
76299
50853
38880
48370
73621
18184
11407
8745
0
0
50066
17727
13011
6010
0
0
50102
14357
19221
18517
18828
26059
17532
12658
22644
8953
5522
7180
5206
0
0
0
0
0
304
0
0
61627
50332
49025
57679
16049
21388
17569
23837
27452
21797
14603
27971
7356
0
6660
0
0
0
0
0
0
397
0
0
63826
51330
50784
65728
23740
25373
29409
23236
29749
24425
11137
18587
33558
12646
18865
17571
11503
6593
0
7307
6676
5824
0
0
0
0
0
0
0
0
0
0
0
310
60030
62417
77497
53989
68803
60606
24198
25259
27573
22381
28918
24628
13114
20218
35334
12954
20221
21451
9476
5623
0
6556
6912
0
0
0
0
0
0
0
0
0
0
1733
0
1292
61918
64319
79880
55907
71022
62688
22818
16146
23879
17033
5879
25063
18483
21443
10233
15033
22619
23208
19825
11059
19512
14477
22421
10098
29301
18156
16232
17010
16287
22788
36246
23355
0
0
7113
0
0
7682
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
926
0
0
1138
1112
50966
54544
59984
55541
29735
80377
49454
56798
46268
45877
58934
77821
54401
18869
13316
20628
12727
2621
24899
16557
16533
7471
11868
19809
18061
17263
11335
20716
17295
24598
8895
36801
20222
15756
18075
18266
25568
36757
25170
4829
0
5617
0
3580
0
0
4333
0
0
0
4235
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
998
0
1029
0
0
477
0
0
1368
0
51654
52975
59131
54565
25294
83522
48261
55187
43686
45148
58458
78131
53948
27407
19980
33370
21182
22073
32355
15322
15025
18839
31772
21765
17518
32602
16690
16751
18664
29240
30250
28968
22099
30470
11969
27424
27584
28928
11399
19078
27842
24532
14916
25352
22942
0
6829
0
11210
0
10097
0
0
0
14745
3839
0
0
3172
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1820
0
0
0
0
0
0
0
0
0
0
262
0
0
70992
74562
80218
69109
63934
67639
59140
55886
63138
73933
54519
63061
72216
47305
59842
64289
25697
22671
34026
25020
19497
34008
12515
11335
14769
30350
22846
15953
30161
14950
14175
18413
31237
32693
32459
28490
31548
17760
26653
28481
27380
14257
23692
29710
26280
18440
27269
25287
0
0
0
0
0
0
0
0
0
13237
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2262
0
0
0
0
508
0
0
70983
74957
84210
69780
63872
69211
57379
54978
62530
75779
57265
64449
73083
44960
59890
63489
Page 13
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
Appendix C
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
WEST BROMWICH WEST
WOLVERHAMPTON NORTH EAST
WOLVERHAMPTON SOUTH EAST
WOLVERHAMPTON SOUTH WEST
WORCESTER
BOLTON WEST
CHORLEY
CHESTER CITY OF
CONGLETON
CREWE & NANTWICH
EDDISBURY
ELLESMERE PORT & NESTON
FYLDE
HALTON
HAZEL GROVE
HEYWOOD & MIDDLETON
KNOWSLEY NORTH
KNOWSLEY SOUTH
LANCASTER
LITTLEBOROUGH & SADDLEWO'
MACCLESFIELD
MAKERFIELD
MORECOMBE & LUNESDALE
RIBBLE VALLEY
SOUTH RIBBLE
STALYBRIDGE & HYDE
TATTON
LANCASHIRE WEST
WIRRAL SOUTH
WIRRAL WEST
WORSLEY
WYRE
ALTRINCHAM & SALE
ASHTON-UNDER-LYNE
BIRKENHEAD
BLACKBURN
BLACKPOOL NORTH
BLACKPOOL SOUTH
BOLTON NORTH EAST
BOLTON SOUTH EAST
BOOTLE
BURNLEY
BURY NORTH
BURY SOUTH
CHEADLE
CROSBY
DAVYHULME
DENTON & REDDISH
ECCLES
HYNDBURN
LEIGH
LIVERPOOL BROADGREEN
LIVERPOOL GARSTON
LIVERPOOL MOSSLEY HILL
LIVERPOOL RIVERSIDE
LIVERPOOL WALTON
LIVERPOOL WEST DERBY
MANCHESTER BLACKLEY
MANCHESTER CENTRAL
MANCHESTER GORTON
MANCHESTER WITHINGTON
MANCHESTER WYTHENSHAWE
OLDHAM CENTRAL & ROYTON
OLDHAM WEST
PENDLE
PRESTON
ROCHDALE
ROSSENDALE & DARWEN
ST HELENS NORTH
ST HELENS SOUTH
SALFORD EAST
SOUTHPORT
STOCKPORT
STRETFORD
WALLASEY
WARRINGTON NORTH
WARRINGTON SOUTH
WIGAN
524
525
526
527
528
529
530
531
532
533
534
ABERAVON
ALYN & DEESIDE
BLAENAU GWENT
BRECON & RADNOR
BRIDGEND
CAERNARFON
CAERPHILLY
CARMARTHEN
CEREDIGION & PEMBROKE N.
CLWYD NORTH WEST
CLWYD SOUTH WEST
14914
24686
21736
24345
18738
20997
23172
17657
17171
17277
18997
27986
11880
17038
30249
24710
16708
23579
11254
12892
0
0
4233
6448
0
5331
7583
0
8613
6331
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
49843
71005
50244
60287
59797
60330
59654
52175
45423
44001
12965
22541
21466
25398
16927
22575
21673
15430
16543
16483
21067
29794
14881
22057
31387
27319
18870
24140
14310
15780
0
0
0
0
0
0
6516
0
6950
4310
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
49109
71482
49390
60925
61349
61551
60295
52370
47685
44412
31824
18572
33911
10971
24446
13379
11885
0
25220
0
0
0
0
0
0
71011
55242
86743
29779
17235
31416
13455
26613
12244
11532
0
32071
0
0
0
0
0
0
73462
60269
91893
22940
18811
19358
24824
12077
24756
18559
19004
19739
26826
33704
26943
32084
17421
27801
24982
20550
18133
19650
23769
19633
18785
13522
13285
12365
22639
22192
42213
16330
20174
18464
31042
12392
14278
19352
23164
10882
20156
24734
17445
28493
14777
14945
24657
23994
26543
16986
10543
21937
16519
21677
25244
23865
20066
0
0
9914
8975
0
0
5209
0
0
12499
0
7765
14574
0
0
13429
0
0
0
0
11462
6873
10086
0
6833
7589
13064
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
899
0
0
0
0
8818
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
86129
44068
59733
62175
54140
59626
44594
48685
54496
62495
71050
73474
78856
58275
59008
65716
58411
56594
57034
57343
55729
59227
51306
49284
54411
65708
69658
20182
16357
19048
24736
11075
24138
17244
17931
18153
26550
32763
28208
31477
15329
24217
24736
17396
15438
18133
21564
19173
18331
14473
10813
11710
22298
20121
41132
18168
24701
20533
30915
13838
15014
20720
23974
12174
22983
30545
21624
30015
14045
17899
24728
24188
25381
18166
13863
26613
19390
19412
25583
26769
23938
0
0
6732
7545
0
8526
5168
0
0
12839
0
0
12313
0
6058
8891
0
0
0
0
7699
0
4483
0
5045
4694
9457
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
585
0
0
0
0
0
0
0
0
0
0
0
0
0
826
0
0
604
0
0
0
0
0
1931
0
0
0
88288
43611
63438
64639
55703
60764
44822
48343
57266
65160
75464
73652
82056
60634
61526
66083
57159
55225
54911
56351
55854
58401
48980
47130
52948
66168
68602
21538
22004
19465
19277
15143
14478
20598
24100
13335
14296
18546
17519
18348
8163
12834
18259
22093
14181
15152
17561
20566
11968
18230
17980
16826
12498
23917
17067
14875
24784
11297
21586
11648
14158
20080
23895
25915
20561
30102
12338
17626
19221
17080
21452
21134
19570
16046
20248
13117
26870
18112
21588
20205
20552
22927
21371
32932
34137
19641
11572
18969
10647
18663
20551
14127
28640
8590
12884
0
0
5663
0
8719
6708
0
0
0
0
7002
0
0
9860
7336
7574
0
0
0
14212
0
7919
0
0
10609
6560
7297
10432
4119
8343
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
988
57771
69369
56721
58138
48969
57470
56490
66464
49657
46451
52892
52650
56481
40276
52228
56996
70704
51898
48933
46718
52233
59695
48392
71734
70465
48109
60288
52283
50142
70311
50368
52664
51986
21980
21374
16418
15776
14508
13490
19179
24716
10662
12643
15617
15150
15271
5844
9251
16676
18548
12796
13076
13829
19121
13239
16984
21845
13776
9000
22324
18262
13436
22901
8918
21472
9876
18674
24739
24726
25033
21330
29552
13529
20746
18203
16488
20950
19988
21571
14206
17274
16029
27485
18431
20648
18406
21539
24481
21093
36901
33325
18409
12798
21598
12353
22312
21930
16290
28754
0
6382
0
0
4375
0
6771
0
0
0
0
0
4297
0
0
6150
5717
5262
0
0
0
9004
0
0
0
0
8630
0
3801
7207
3070
6606
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1239
0
0
0
0
0
0
0
0
0
0
796
0
0
0
0
5117
0
0
0
0
0
0
0
0
0
0
0
0
858
56318
68093
56706
56714
48412
57930
55631
66678
47704
44261
51373
51948
54498
35201
46208
54585
69229
50167
46584
46144
50140
59176
47357
75328
68675
44849
60218
50370
45243
68919
49207
54036
51967
9424
21513
4949
15415
33103
25469
25220
23967
0
0
0
0
2118
0
0
2165
1260
0
0
0
56777
54076
37936
50159
9369
15960
4352
14523
33763
24442
24936
22902
0
6348
0
0
0
902
0
2410
1620
0
0
0
57179
55119
36953
49464
7915
6086
4996
5897
17970
17777
26011
21424
9281
8754
0
0
15210
11500
13331
6998
3956
5495
3262
3444
0
0
0
0
0
40671
45969
55786
37964
54032
6972
5182
5338
5893
17382
17650
26330
21221
11302
11305
0
0
11988
10779
12725
6834
3949
7416
2469
2695
0
0
0
0
0
40121
46240
55407
37553
54715
Page 14
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
Appendix C
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
CONWY
CYNON VALLEY
DELYN
GOWER
ISLWYN
LLANELLI
MEIRIONNYDD NANT CONWY
MERTHYR TYDFIL & RHYMNEY
MONMOUTH
MONTGOMERY
NEATH
NEWPORT EAST
NEWPORT WEST
OGMORE
PEMBROKE
PONTYPRIDD
RHONDDA
TORFAEN
VALE OF GLAMORGAN
WREXHAM
YNYS MON
CARDIFF CENTRAL
CARDIFF NORTH
CARDIFF SOUTH & PENARTH
CARDIFF WEST
SWANSEA EAST
SWANSEA WEST
18753
5780
18515
8822
5810
6300
2656
4767
25635
6768
8342
23649
15234
29106
13298
27895
29425
32546
8420
23275
21921
5696
29692
31962
0
0
7482
0
0
6031
7171
0
6764
10738
0
0
3058
2723
1195
2562
0
3469
3697
2878
0
2167
0
0
0
0
0
0
0
1061
0
0
0
0
2342
0
46151
47519
50147
49219
44538
62235
26392
40542
60803
30154
50318
70387
17622
4204
18179
8852
4739
7143
1948
4082
25654
6784
6312
21599
18203
26322
15137
29910
29723
33674
9628
21737
28619
5891
31183
32098
0
0
7137
0
0
0
7733
0
0
10278
0
0
2552
3073
1585
0
0
5132
2490
3361
0
1841
0
0
0
2305
0
0
0
1211
0
0
0
0
1632
0
45825
46618
51346
49731
44944
61868
25395
39474
64356
29951
49694
68131
10250
15340
11859
2548
8169
28600
17240
7016
21837
34178
23926
29533
20510
27852
24334
30478
13553
18215
0
9679
0
0
0
0
0
5730
7806
2470
1717
0
2361
0
0
4673
1817
1058
0
0
0
3385
1329
0
0
0
0
58848
62196
53859
36228
48024
64319
66530
35793
60632
6872
17921
10325
1857
7418
27957
12596
9576
22997
33545
23852
30840
21567
27909
26563
30039
14874
23669
6632
5308
0
0
0
0
6351
0
0
0
2460
0
2088
0
0
2297
2596
0
0
0
0
2349
897
0
0
0
0
60003
62110
55088
35509
48040
65194
66441
36950
59092
22288
17941
7863
20382
30129
25998
30904
23019
0
0
0
4672
0
0
3556
0
0
0
0
0
65632
57511
55505
59091
18476
16714
6241
20650
29313
26139
30290
26703
3829
0
0
0
0
0
2749
0
0
0
902
0
65394
57088
54459
58889
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
ANGUS EAST
ARGYLL & BUTE
AYR
BANFF & BUCHAN
CAITHNESS & SUTHERLAND
CARRICK CUMNOCK & DOON VA
FIFE CENTRAL
CLACKMANNAN
CLYDEBANK & MILNGAVIE
CLYDESDALE
CUMBERNAULD & KILSYTH
CUNNINGHAME NORTH
CUNNINGHAME SOUTH
DUMBARTON
DUMFRIES
DUNFERMLINE EAST
DUNFERMLINE WEST
EAST KILBRIDE
EAST LOTHIAN
EASTWOOD
FALKIRK EAST
FALKIRK WEST
GALLOWAY & UPPER NITHSDAL
GORDON
INVERNESS NAIRN & LOCHABE
KILMARNOCK & LOUDOUN
KINCARDINE & DEESIDE
KIRKCALDY
LINLITHGOW
LIVINGSTON
MIDLOTHIAN
MORAY
FIFE NORTH EAST
TAYSIDE NORTH
ORKNEY & SHETLAND
PERTH & KINROSS
RENFREW WEST & INVERCLYDE
ROSS CROMARTY AND SKYE
ROXBURGH & BERWICKSHIRE
STIRLING
STRATHKELVIN & BEARSDEN
TWEEDDALE ETTRICK & LAUDE
WESTERN ISLES
ABERDEEN NORTH
ABERDEEN SOUTH
DUNDEE EAST
DUNDEE WEST
EDINBURGH CENTRAL
EDINBURGH EAST
EDINBURGH LEITH
EDINBURGH PENTLANDS
EDINBURGH SOUTH
EDINBURGH WEST
GLASGOW CATHCART
GLASGOW CENTRAL
GLASGOW GARSCADDEN
GLASGOW GOVAN
GLASGOW HILLHEAD
GLASGOW MARYHILL
GLASGOW POLLOK
GLASGOW PROVAN
GLASGOW RUTHERGLEN
19566
13277
20047
14621
4550
12392
11880
12815
7590
8120
18346
6840
6619
24795
28806
23927
6472
6707
0
7088
7894
0
0
0
0
0
0
1925
0
0
0
5106
0
0
0
0
2795
0
3273
0
44495
39965
46269
43502
27291
47936
55948
52424
22407
12178
19988
12067
4662
11442
8300
10037
9404
8486
19504
6422
8308
23495
27123
22557
0
7512
0
8034
8244
0
0
0
0
0
0
2584
0
0
6046
8225
0
0
0
0
0
0
1542
0
44705
38949
46285
42661
26776
46504
56028
52640
16497
18523
15448
22816
11934
23999
21079
18360
4671
0
0
0
0
0
5004
5726
0
0
0
0
44352
50510
50608
57502
16138
17637
13724
20779
13482
24035
21636
16358
3539
0
0
2679
0
0
6042
5727
0
0
0
0
43625
50744
50522
56797
14033
24922
21669
27846
22468
30242
21044
16503
0
0
0
8655
0
0
0
0
0
0
0
0
47288
64165
50251
64146
9446
21995
20931
28017
20709
29735
22620
17426
0
0
0
7252
5304
5838
0
0
0
0
0
0
46472
68763
50584
65971
17070
14530
16429
12099
10796
13401
11756
8919
23766
6401
7203
9402
25173
4513
24263
24933
0
6619
11754
14235
4443
9268
0
0
4526
0
0
0
0
0
4423
15087
0
0
0
0
0
0
0
610
56806
37331
45744
50067
48824
35324
52355
62328
13726
15137
13956
11961
11949
13286
10539
5726
23146
9283
6008
10069
26036
5318
23273
26662
0
0
15151
14356
0
7756
0
0
6322
0
0
0
0
0
5223
17955
767
0
0
0
0
0
0
567
57015
36683
46011
50462
48073
34583
51765
63967
18861
12741
21001
16659
3704
23912
18507
5516
29820
6830
9765
4687
3232
10184
19518
4767
0
5478
5075
0
11604
0
4253
6923
0
0
2635
3522
0
7186
0
0
0
0
257
127
0
0
0
0
61689
36098
49782
32931
25481
54582
51018
24777
13192
11842
19323
14466
3630
22129
20060
4820
27608
8384
9229
4461
3021
10911
23849
5304
0
4368
3574
0
9605
0
0
7348
7974
0
5394
4884
0
6128
0
0
0
0
0
0
0
0
0
0
62940
36154
49311
32412
24927
54159
53796
24530
14834
25137
18924
2217
14366
25824
21499
22473
12082
19376
12777
20181
21375
26298
27299
5455
16856
9571
16993
8403
17793
15524
15391
21144
32949
7007
8740
31844
21926
26062
27090
14174
24808
15934
17794
13555
18359
24294
16931
27036
20326
9572
20796
18089
29889
18892
0
0
17185
4894
0
0
0
0
0
0
0
5862
5272
7352
0
0
0
0
0
0
4670
0
0
0
0
1093
0
0
2197
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1648
0
1771
0
0
0
0
0
1228
0
0
0
0
0
0
0
1339
0
1378
0
1231
0
0
0
44315
72528
53753
23699
61785
61639
59451
62325
36588
54581
36977
53743
50055
64279
65074
36768
56819
44517
35580
43190
52094
60027
41769
9148
23001
18396
2832
8768
21492
19804
18345
9667
16614
11443
19176
20820
24882
26549
4513
14493
7677
15899
6075
19282
12986
13607
17513
32985
6131
8565
28799
23291
25530
26705
13682
25423
15407
19132
15487
20073
25330
14453
27320
18533
9384
19936
21257
28201
18621
0
0
20607
2638
4350
5797
0
3454
0
0
0
4363
0
6571
0
0
0
0
0
0
0
0
0
9381
5715
0
0
0
0
0
0
0
0
0
0
2856
0
0
0
0
0
0
3387
0
0
2194
0
1548
0
0
719
0
0
1217
0
0
279
0
0
0
516
819
2395
1103
0
0
0
988
0
43728
78453
53224
22823
59157
62206
57502
62230
33910
54311
35652
53020
50450
65507
65759
32050
59478
40481
34388
42912
51301
59575
40870
Page 15
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
Appendix C
624
625
626
627
628
629
630
631
632
633
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
GLASGOW SHETTLESTON
10598 22494
0
GLASGOW SPRINGBURN
5632 16828
0
GREENOCK & PORT GLASGOW
6473 19627
9055
HAMILTON
11806 28964
0
MONKLANDS EAST
16580 27178
0
MONKLANDS WEST
15192 23385
0
MOTHERWELL NORTH
18068 27556
0
MOTHERWELL SOUTH
14789 23281
0
PAISLEY NORTH
PAISLEY SOUTH
6583 26318 16837
The following constituencies did not exist after the 1979 general election
ABERTILLERY
3973 24204
0
BANFF
9995
5574
5354
BATTERSEA,SOUTH
10615 12253
3294
BIRMINGHAM,HANDSWOR
16841 11909
6249
BRISTOL,NORTH EAST
22423 21212
0
CONSETT
13901 29676
0
DARWEN
20343 15559 12641
EDINBURGH NORTH
17094 12264
0
GLASGOW CENTRAL
5679 13343
0
GLASGOW CRAIGTON
15518 21775
0
GLASGOW KELVINGROVE
8791 10340
0
HACKNEY CENTRAL
11734 23110
0
HARROW,CENTRAL
16534 12067
7168
HUDDERSFIELD,WEST
13054 14808 13528
ISLINGTON CENTRAL
7715 14192
3081
LAMBETH CENTRAL
11934 16518
0
LEEDS,SOUTH EAST
7964 16672
0
LIVERPOOL,KIRKDALE
16120 20128
0
LIVERPOOL,SCOTLAND
7393 17984
0
MANCHESTER,OPENSHAW
13387 22589
0
NEWCASTLE/TYNE CENT
7896 20547
0
RIPON
18503
7341
7814
SALFORD,WEST
16446 20490
0
ST.MARYLEBONE
18117
9324
4776
ST.PANCRAS NORTH
11954 20516
0
STOCKPORT SOUTH
13718 16755
7107
WOOD GREEN
16939 22131
0
The following constituencies did not exist after the 1970 general election
BARONS COURT
14800 15966
2821
BIRMINGHAM,ALL SAIN
14505 14975
0
BRADFORD,EAST
11075 17945
0
BRISTOL,CENTRAL
11616 16207
0
CENTRAL NORFOLK
24486 18481
6961
EALING,SOUTH
22121 14121
0
EAST HAM,SOUTH
8797 17069
0
ENFIELD,EAST
11447 17958
5723
GLASGOW BRIDGETON
7492 18879
0
GLASGOW WOODSIDE
11954 13521
2443
HESTON AND ISLEWORT
19181 15651
6409
KENSINGTON,SOUTH
21668
5300
4916
LEICESTER,SOUTH WES
12851 16957
4533
LEWISHAM,SOUTH
12486 20078
5706
LIVERPOOL,EXCHANGE
7239 16985
0
MANCHESTER,EXCHANGE
6242 13952
0
MERTON AND MORDEN
19032 16234
5781
PADDINGTON,SOUTH
10838
7439
2278
POPLAR
5813 20271
0
RHONDDA,WEST
2754 20713
0
SEDGEFIELD
20931 32273
0
SOUTHWARK
8563 22426
0
STOCKTON-ON-TEES
15424 22011
6130
TORRINGTON
16899
5867 14831
WALTHAMSTOW,EAST
14140 13745
5042
WANDSWORTH,CLAPHAM
17101 17657
2611
WEDNESBURY
20251 23473
0
WEMBLEY,SOUTH
16512 12199
5713
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
ANTRIM EAST
ANTRIM NORTH
ANTRIM SOUTH
BELFAST EAST
BELFAST NORTH
BELFAST SOUTH
BELFAST WEST
DOWN NORTH
DOWN SOUTH
FERMANAGH AND SOUTH TYRONE
FOYLE
LAGAN VALLEY
LONDONDERRY EAST
NEWRY AND ARMAGH
STRANGFORD
ULTER MID
UPPER BANN
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
0
2366
0
0
0
0
0
0
0
950
458
0
0
0
0
1565
46358
37248
46549
52588
53863
47972
56718
50209
6857
4499
5835
11289
14777
14857
16198
13100
20208
15998
18988
27865
26491
23160
27166
22658
0
0
7727
0
0
0
0
0
3732
2222
0
0
0
0
0
0
0
867
702
0
0
0
1209
1508
0
0
62336
10892
28160
7889
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
459
4346
0
0
0
0
0
0
0
0
0
2053
0
0
0
725
1947
532
0
0
0
1140
0
0
37310
30880
36186
53243
62229
56792
58014
39888
30465
46118
28407
61036
45512
50748
47349
49903
38326
52426
43830
53195
41913
41773
50514
50003
53670
45955
55593
3151
8139
9861
16225
21727
10858
20598
13765
3924
11970
6793
7440
15971
13514
7490
10500
5743
13219
4730
10465
5474
17352
13257
17433
10440
15387
14133
23353
4775
13651
14931
25699
29753
18863
10730
11673
21174
9311
21466
14341
17990
15009
16634
14633
19233
14244
22103
19291
8607
19237
9382
20951
19456
21922
0
6762
2276
0
0
0
9339
2871
0
0
0
4762
5118
9470
2288
0
0
0
0
0
0
7301
0
3258
0
0
0
0
0
0
0
0
0
0
0
0
3425
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1337
0
0
0
0
0
0
0
0
0
0
1127
0
0
0
779
1479
404
0
0
697
1253
0
0
36122
30216
35350
51383
61554
55246
59066
37056
26579
45472
24299
58513
44195
49813
45416
47615
33199
49429
38176
51682
38209
42141
48392
47294
51468
45406
53559
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
0
0
0
0
0
0
0
0
0
1600
0
0
0
0
0
0
0
0
0
2668
0
0
0
0
0
0
0
0
0
0
0
1936
0
0
0
0
0
88
0
0
0
0
0
0
0
0
0
0
0
1599
0
0
0
847
0
0
46048
44594
43216
41367
61254
51714
38121
46043
41482
40035
52703
56157
44311
51175
44542
36175
49854
35226
44757
32401
66886
58334
53263
44176
41504
52826
61395
43899
13551
11595
8091
9410
27935
18968
7540
11245
5619
11202
18222
21050
13268
11247
5372
3761
20028
10297
3863
1955
18620
6454
15547
17912
13896
15379
18213
15377
17021
16350
18435
15399
23529
13885
17543
18772
16219
13540
17296
6419
18822
21165
15089
10425
19608
8854
21071
19060
34058
21855
24248
5891
15703
19555
26041
14194
2384
0
0
0
0
4743
0
4189
0
0
5559
4871
0
4779
0
0
0
2170
0
0
0
0
0
14260
3229
2968
0
4386
0
0
0
0
0
0
0
0
0
1916
0
0
0
0
0
0
0
0
0
2172
0
0
0
0
0
0
0
0
0
0
0
1322
0
0
0
0
0
122
0
0
0
0
0
0
0
0
0
1853
0
1404
710
0
0
0
0
0
43830
42896
40731
37363
64492
51283
38197
45487
37159
36678
51400
55660
43373
49501
40319
26400
48807
34176
43236
31189
69287
54997
52345
44375
40981
51885
61481
42843
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
72039
113645
57077
71434
56390
67588
100755
78096
63188
78191
40372
47325
24804
29976
27422
21337
45091
32922
30010
37700
0
16531
15555
17564
8792
12571
11571
6260
2339
0
0
0
0
0
1941
0
3797
5610
6006
0
0
0
0
0
0
0
0
0
0
0
4424
3830
1827
2743
1159
17934
855
11031
16138
21123
70762
105304
58196
72400
57558
69399
97151
77391
63642
76918
31927
40840
21283
26891
23329
24281
38706
32876
29352
34729
0
22672
17650
19927
12364
0
0
0
0
0
8941
0
0
0
0
0
10582
9586
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
26292
0
8917
25015
25027
35223
6523
0
0
12432
74184
34687
0
0
0
13467
29715
5053
0
0
22810
66607
29728
0
0
0
27168
Page 16
44873
35428
45164
53393
53422
48269
57137
50070
624
625
626
627
628
629
630
631
632
61363 633
689
690
691
692
693
694
695
696
697
698
699
700
701
76111 702
703
67796 704
705
Appendix C
British General election results 1955 to 1987
Political party and electoral roll Conservativ Labour
Parliamentary constituency area
1970
BARROW & FURNESS
BERWICK-UPON-TWEED
BISHOP AUCKLAND
DURHAM CITY OF
COPELAND
EASINGTON
HEXHAM
LANGBAURGH
DURHAM NORTH
DURHAM NORTH WEST
PENRITH & THE BORDER
SEDGEFIELD
WANSBECK
WESTMORLAND & LONSDALE
WORKINGTON
BLAYDON
BLYTH VALLEY
CARLISLE
DARLINGTON
GATESHEAD EAST
HARTLEPOOL
HOUGHTON & WASHINGTON
JARROW
MIDDLESBROUGH
NEWCASTLE UPON TYNE CENTL
NEWCASTLE UPON TYNE EAST
NEWCASTLE UPON TYNE NORTH
REDCAR
SOUTH SHIELDS
STOCKTON NORTH
STOCKTON SOUTH
SUNDERLAND NORTH
SUNDERLAND SOUTH
TYNE BRIDGE
TYNEMOUTH
WALLSEND
BARNSLEY WEST & PENISTONE
BEVERLEY
BOOTHFERRY
BRIDLINGTON
BRIGG & CLEETHORPES
CALDER VALLEY
COLNE VALLEY
DEWSBURY
DONCASTER NORTH
DON VALLEY
ELMET
HARROGATE
HEMSWORTH
KEIGHLEY
NORMANTON
PONTEFRACT & CASTLEFORD
RICHMOND (YORKS)
ROTHER VALLEY
RYEDALE
SCARBOROUGH
SELBY
SHEFFIELD HALLAM
SHEFFIELD HILLSBOROUGH
SHIPLEY
SKIPTON & RIPON
WENTWORTH
BARNSLEY CENTRAL
BARNSLEY EAST
BATLEY & SPEN
BRADFORD NORTH
BRADFORD SOUTH
BRADFORD WEST
DONCASTER CENTRAL
GLANFORD & SCUNTHORPE
GREAT GRIMSBY
HALIFAX
HUDDERSFIELD
HULL EAST
HULL NORTH
HULL WEST
LEEDS CENTRAL
LEEDS EAST
LEEDS NORTH EAST
LEEDS NORTH WEST
LEEDS WEST
LEEDS SOUTH & MORLEY
PUDSEY
ROTHERHAM
SHEFFIELD ATTERCLIFFE
Liberal
Nationalis Other par Registered
electors
Conservativ Labour
Liberal
Nationalis Other par Registered
February 1974
electors
17536
15558
13769
16707
16418
8457
24516
31130
13363
10590
23800
22400
8413
21257
33766
22974
33418
16645
36213
33694
24245
10256
0
6741
0
0
0
0
6021
0
0
0
6316
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
54126
41669
49506
68886
50326
60441
62564
92328
63872
47817
54270
14800
15300
19200
16200
15900
13100
24100
21100
8300
10900
26400
19900
4300
27100
31400
23200
33600
16100
17400
33500
28300
9100
8500
15700
10000
12200
0
0
12700
11000
14800
8800
8200
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
235
54100
41500
71900
74100
50500
63200
64400
60200
67900
60700
55100
9515
21253
15532
13926
12550
19241
19447
15489
20188
11914
14847
9623
15978
14832
21644
21826
7757
24975
25724
36118
21866
23208
28524
27704
32888
25861
23581
12518
20612
30805
4825
9426
0
0
0
0
5222
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
47556
53453
52329
54838
67908
52294
62584
62231
64403
62569
54659
54899
42085
47110
74274
19960
30191
0
0
0
75129
22374
16738
20722
7328
30773
24650
14897
30042
22102
25053
25659
16114
10417
17468
19431
6848
35198
26167
9534
20957
13132
10687
30471
17418
30892
26154
17457
25134
11445
20938
20817
21986
25779
26840
15622
23927
39065
31615
15862
9567
11546
16403
16583
18896
22015
22658
33966
23861
8797
40013
20341
28421
31774
12702
44322
15309
9802
26424
12884
18775
16161
12011
0
0
0
0
5221
0
7347
10129
6951
6495
6279
5137
18040
5688
2648
4426
0
8825
0
0
0
0
5354
0
0
16517
0
2972
0
4468
7733
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
154
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
59037
60921
67751
34464
79000
85677
72850
74814
54719
61715
67540
50031
58604
60544
59755
62935
78127
62617
69059
51140
58059
60145
70908
87422
63863
73400
63226
58735
46483
50435
51554
9000
22000
16200
15700
8900
18100
18500
12000
22700
10300
13800
13900
12800
14300
22400
19000
18800
25500
19800
17500
19700
5400
26800
24600
14100
31700
21900
25700
25200
14377
10900
15800
17600
7000
34000
27500
9200
17700
14400
10600
27000
19100
27600
21900
17000
29100
10800
19400
19300
22000
6400
24000
27300
16800
23100
20500
27300
27000
34300
27700
27200
9800
20400
33800
28300
30700
37900
21500
28900
28300
13800
20400
41800
27800
11000
7300
9800
15148
14492
20265
21200
23000
34700
20800
6100
44100
18600
29600
34400
7700
52500
10900
7000
30200
16100
22100
15300
8100
8000
15500
0
0
10200
0
11400
10200
0
0
0
0
6800
0
0
0
0
0
7800
9000
9100
3500
13400
0
13100
19900
15700
14700
15440
11254
20984
12900
7500
7900
14700
15700
0
7800
0
0
11700
0
13200
16800
0
14200
6900
10200
17200
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
22900
628
0
0
0
0
0
0
0
0
0
0
1958
791
0
0
0
0
0
1108
867
0
0
0
0
0
0
0
0
906
0
2061
0
348
0
991
0
0
0
216
2150
0
0
192
0
48100
55500
52700
56500
74000
51800
62400
62900
64700
59300
54200
59400
40000
45300
76300
61700
71900
84500
61700
75000
76000
30600
75800
89400
66500
75600
57100
65300
70000
48400
60300
61000
58900
62700
83100
64200
69100
51300
58500
59700
61500
91100
63400
58100
64100
76900
51700
51600
52100
10811
34956
8186
0
0
75678
16000
40600
0
0
0
75900
22953
18511
19009
20475
18673
27449
17460
23828
15632
14736
17912
14537
9311
21112
20720
29227
14749
15753
24308
12770
10986
22894
20141
20985
18936
42496
31434
23571
24026
20629
36859
26302
24050
19536
28827
15653
20795
21618
23024
18313
25246
26482
3781
0
5694
0
0
0
3850
0
4569
0
0
0
3810
0
0
6048
5341
6893
6754
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
847
308
0
1808
0
0
513
0
0
0
0
0
0
581
61616
52224
63297
53252
83626
87058
65636
66222
54496
75678
65661
59553
49520
76591
55660
80309
63491
62802
62335
60471
59949
20600
15800
18200
18600
20800
25700
15900
18000
12900
17700
19200
12800
7800
15000
20800
22000
11200
14400
21800
10400
12900
22100
22400
25900
20800
48700
28800
21600
21000
20200
41300
26900
20700
21400
25600
13600
15300
19400
21500
15300
27100
34100
11000
13100
13000
7200
0
15500
12100
12300
8500
0
0
8500
9500
9900
8800
11900
15500
11500
18000
7700
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
169
386
749
1364
0
0
816
0
1042
0
0
0
0
0
300
0
0
828
0
0
424
63200
64500
71600
61200
87900
89400
65700
63100
53100
80700
62700
57100
52300
67100
58500
64500
59900
61500
64800
60800
63400
Page 17
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
Appendix C
SHEFFIELD BRIGHTSIDE
SHEFFIELD CENTRAL
SHEFFIELD HEELEY
WAKEFIELD
YORK
AMBER VALLEY
ASHFIELD
BASSETLAW
BLABY
BOLSOVER
BOSWORTH
BROXTOWE
CORBY
DAVENTRY
LINDSEY EAST
EREWASH
GAINSBOROUGH & HORNCASTLE
GEDLING
GRANTHAM
HARBOROUGH
HIGH PEAK
HOLLAND WITH BOSTON
KETTERING
LOUGHBOROUGH
MANSFIELD
NEWARK
DERBYSHIRE NORTH EAST
LEICESTERSHIRE NORTH WEST
RUSHCLIFFE
RUTLAND & MELTON
SHERWOOD
DERBYSHIRE SOUTH
STAMFORD & SPALDING
WELLINGBOROUGH
DERBYSHIRE WEST
CHESTERFIELD
DERBY NORTH
DERBY SOUTH
LEICESTER EAST
LEICESTER SOUTH
LEICESTER WEST
LINCOLN
NORTHAMPTON NORTH
NORTHAMPTON SOUTH
NOTTINGHAM EAST
NOTTINGHAM NORTH
NOTTINGHAM SOUTH
BURY ST. EDMUNDS
SUFFOLK CENTRAL
GREAT YARMOUTH
HUNTINGDON
NORFOLK MID
CAMBRIDGESHIRE NORTH EAST
NORFOLK NORTH
NORFOLK NORTH WEST
CAMBRIDGESHIRE SOUTH EAST
NORFOLK SOUTH
SUFFOLK SOUTH
CAMBRIDGESHIRE SOUTH WEST
NORFOLK SOUTH WEST
SUFFOLK COASTAL
WAVENEY
CAMBRIDGE
IPSWICH
NORWICH NORTH
NORWICH SOUTH
PETERBOROUGH
BARKING
BATTERSEA
BECKENHAM
BETHNAL GREEN & STEPNEY
BEXLEYHEATH
BOW & POPLAR
BRENT EAST
BRENT NORTH
BRENT SOUTH
BRENTFORD & ISLEWORTH
CARSHALTON & WALLINGTON
CHELSEA
CHINGFORD
CHIPPING BARNET
CHISLEHURST
CROYDON CENTRAL
CROYDON NORTH EAST
CROYDON NORTH WEST
CROYDON SOUTH
DAGENHAM
DULWICH
EALING ACTON
EALING NORTH
8572
7024
27950
15668
27422
15870
15089
20698
44933
8371
30732
30966
23941
23302
27257
27352
29619
32961
32372
28959
25728
28830
29677
24798
0
0
4220
4071
0
6157
0
3125
9079
0
0
4180
0
0
0
0
0
0
0
0
0
0
0
0
665
637
0
0
0
0
0
0
0
0
0
0
53499
52935
80943
64715
74811
74083
67681
69049
103125
52571
78192
75268
6800
7700
18300
15600
24843
17300
14200
22500
26900
9500
28200
26500
27400
31300
25300
27000
25674
31500
36000
33700
13700
30800
26500
23900
5300
8600
9100
10000
12793
11700
10500
0
14600
0
16900
12100
0
0
0
0
0
0
0
0
0
0
0
0
513
521
0
0
0
0
0
0
0
0
0
0
53700
67000
64800
66000
76500
74400
74100
71100
64600
51400
83200
73700
29070
19299
32185
22163
39840
33070
21131
8860
29461
14454
27043
23296
6626
6707
0
7543
0
0
0
0
0
0
0
0
0
0
0
0
0
0
72980
46959
81866
59099
89570
73728
19558
33580
30613
18054
24241
34803
7119
0
6695
0
0
0
0
0
0
55400
77245
94954
31300
19300
20000
22200
27300
31900
28000
19200
30600
21900
21500
6800
17000
12000
20100
20600
11600
17000
18200
31700
16800
12600
8400
16000
11300
0
15500
12100
15500
15400
0
0
0
0
0
0
0
0
0
0
0
367
0
0
1449
10800
0
0
0
0
82600
49300
53300
61200
71200
77700
65300
56600
79800
85000
15027
25235
24550
22272
26762
38782
30554
26455
38181
22806
23031
20907
0
0
0
5185
0
9465
0
0
0
0
0
0
628
0
0
0
0
0
67291
68238
89892
63564
71102
90971
18200
27100
22300
21946
29800
32200
34400
31600
29600
22643
12100
16200
0
0
0
14100
11700
19500
0
0
0
0
0
0
675
0
0
0
0
0
68900
70800
66300
69700
63500
81400
35757
22803
27459
22692
16217
16635
16258
14125
26483
15584
15340
26183
33633
15136
25107
13976
30386
20114
19407
15016
15788
18226
20090
27424
0
0
0
0
4891
0
0
0
0
2862
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1616
0
935
3937
0
86642
50337
64563
47830
71051
57228
52999
43339
58820
53080
52827
74502
14079
18616
19003
36688
26099
23088
27398
17638
25898
21255
23286
17735
19931
17588
0
3763
0
0
5962
3523
5082
0
0
0
0
0
0
0
0
741
0
0
0
0
0
52729
70672
56763
77665
63739
60226
66511
28600
21100
29100
19900
15600
24700
19500
22100
22900
18500
13299
15300
14321
13300
21000
21800
33400
23500
24700
29000
30600
12200
26800
9500
31000
26000
26600
23500
21200
27200
13487
16300
14142
17300
25400
27600
20200
13900
19800
17100
0
11300
15000
11500
9900
14000
10100
0
9100
0
0
8500
7100
6300
9600
11300
16800
15800
12500
19000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
897
0
0
0
0
3662
1639
2579
14780
0
0
0
754
0
0
615
0
0
70700
54200
84600
48700
70700
81900
73800
63300
71900
64100
52500
48600
44000
53600
75900
76900
86600
65200
70200
79100
28972
24587
23822
32264
22614
32393
19366
19903
23789
19993
17172
19829
0
0
0
6861
3811
7136
0
0
0
0
0
0
0
0
0
0
0
0
67226
55381
60857
78271
55538
78250
19300
35700
27823
32600
33100
32000
9500
21400
27020
17900
22000
18228
27600
17900
10852
18800
19100
18286
0
0
0
0
0
0
0
0
0
0
337
0
67900
89900
79100
83800
90100
83700
22220
16572
0
0
0
48213
20400
14400
9000
0
380
53300
28842
26252
27704
11868
17067
30227
9309
4927
30763
5578
27075
4922
15564
18345
10163
13538
27342
15852
43615
26845
23319
21191
27691
18564
16241
25662
21097
11621
13031
15483
19017
18993
20073
11916
21918
14051
16896
5737
41040
18166
4737
0
5147
0
3031
0
0
1012
9404
3030
3222
0
0
4083
0
0
6411
2136
0
6329
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2322
658
0
0
0
179
0
0
1771
1468
0
0
515
0
0
514
0
0
72320
65000
86495
43621
46451
71593
49317
30106
77385
47830
66980
56519
57109
47632
54983
37709
71119
44138
115542
71988
25986
20351
19260
35773
11976
19250
13300
23139
22283
16373
14687
10186
31335
20145
13960
23459
3673
4210
4666
11749
0
3301
1583
0
0
0
0
0
0
0
0
0
303
0
0
0
982
0
258
0
73166
58768
57064
78908
74942
66265
43670
64092
26200
24100
29893
9800
14700
20353
7200
8100
23000
4800
18500
5500
13400
25700
12400
22700
24400
23300
19900
22100
20600
21400
19400
17900
28900
12300
17200
18500
22900
22600
19400
29634
17100
15400
20331
22800
18500
11000
21400
14700
28900
21100
17800
23000
22000
18800
6800
14200
12200
15100
20000
15600
14800
7000
35800
22500
17000
25400
15300
15500
11900
7800
7200
10772
8000
4700
12800
6400
9600
0
8200
12500
5800
9500
11700
8000
12100
11700
9100
11300
10700
9700
13000
0
9900
7200
10900
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
369
1161
544
0
0
0
208
0
0
0
1278
0
1570
2232
1741
0
416
0
0
0
0
0
0
0
1169
0
0
93
76400
75500
86800
44700
44500
62500
49600
44500
59000
53400
50600
60100
62800
71500
60800
70800
66200
64300
56600
56000
53200
66200
58000
54800
59500
69300
67100
56400
73400
Page 18
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
Appendix C
EALING SOUTHALL
EDMONTON
ELTHAM
ENFIELD NORTH
ENFIELD SOUTHGATE
ERITH & CRAYFORD
FELTHAM & HESTON
FINCHLEY
FULHAM
GREENWICH
HACKNEY NORTH & STOKE NEW
HACKNEY SOUTH & SHORED'H
HAMMERSMITH
HAMPSTEAD & HIGHGATE
HARROW EAST
HARROW WEST
HAYES & HARLINGTON
HENDON NORTH
HENDON SOUTH
HOLBORN & ST PANCRAS
HORNCHURCH
HORNSEY & WOOD GREEN
ILFORD NORTH
ILFORD SOUTH
ISLINGTON NORTH
ISLINGTON SOUTH & FINSBUR
KENSINGTON
KINGSTON UPON THAMES
LEWISHAM DEPTFORD
LEWISHAM EAST
LEWISHAM WEST
LEYTON
MITCHAM & MORDEN
NEWHAM NORTH EAST
NEWHAM NORTH WEST
NEWHAM SOUTH
NORWOOD
OLD BEXLEY & SIDCUP
ORPINGTON
PECKHAM
PUTNEY
RAVENSBOURNE
RICHMOND & BARNES
ROMFORD
RUISLIP - NORTHWOOD
SOUTHWARK & BERMONDSEY
STREATHAM
SURBITON
SUTTON & CHEAM
CITY OF LONDON & WESTMIN
TOOTING
TOTTENHAM
TWICKENHAM
UPMINSTER
UXBRIDGE
VAUXHALL
WALTHAMSTOW
WANSTEAD & WOODFORD
WESTMINSTER NORTH
WIMBLEDON
WOOLWICH
AYLESBURY
BANBURY
BEACONSFIELD
BILLERICAY
BRAINTREE
BRENTWOOD & ONGAR
BUCKINGHAM
CHELMSFORD
CHESHAM & AMERSHAM
EPPING FOREST
HARLOW
HARWICH
HENLEY
HERTFORD & STORTFORD
HERTSMERE
BEDFORDSHIRE MID
MILTON KEYNES
BEDFORDSHIRE NORTH
COLCHESTER NORTH
HERTFORDSHIRE NORTH
LUTON NORTH
OXFORD WEST & ABINGDON
ROCHFORD
SAFFRON WALDEN
ST ALBANS
COLCHESTER SOUTH & MALDON
BEDFORDSHIRE SOUTH WEST
HERTFORDSHIRE SOUTH WEST
STEVENAGE
15166
18481
20418
21858
22963
18158
16006
25480
12807
13195
11298
7166
9615
21264
19517
24867
13728
18192
18901
10125
36124
21434
25142
18369
7862
6601
9792
23426
7355
17208
19676
17906
27257
7735
7130
5422
16003
24650
24385
8232
23768
22364
20979
25139
24247
4172
19215
17359
23957
19102
16830
10975
28571
19389
20626
21036
9896
9389
23012
21561
14295
16312
20804
20446
14474
16145
20790
15496
11462
19192
15013
12712
12448
30294
17645
17352
17087
13010
12876
13175
13090
14672
18235
18916
23386
22047
11557
17664
18899
16634
21287
4098
17071
25162
9328
12981
27899
11541
13908
13593
10469
11261
10062
19776
17367
16950
0
2937
0
4820
5451
0
3536
7614
0
3319
0
0
0
3550
3185
5440
0
3704
4981
0
6227
3755
5425
3341
0
0
1990
4822
0
0
0
0
0
0
3167
0
2436
4268
23063
0
3887
5982
6934
0
4188
0
2680
4027
6023
2708
0
0
6516
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1572
0
0
1175
0
0
0
0
533
0
793
0
0
0
72
0
372
0
0
0
0
780
0
917
1232
1670
0
0
1277
0
0
0
638
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1706
0
299
0
0
462
55980
63200
57508
53032
55870
57696
60336
72349
43088
56746
64980
44436
41362
71919
50363
57374
49556
51999
55551
41742
99800
64683
69852
58292
45077
43282
43430
59796
42569
51670
56625
66540
72613
36695
55490
49751
53412
66483
65112
50750
76658
54305
57053
79448
54702
34166
53146
47706
60991
58974
58501
51258
74038
16900
15100
17700
17300
28300
15600
19500
18200
17400
11300
7800
6600
11900
19500
18000
24000
14600
17300
17800
8200
15600
18800
19843
16060
6700
6500
18400
23000
11100
18000
18700
12800
18500
10900
6301
4400
13300
20400
26400
8000
20200
20400
19500
17100
22000
6100
18500
17200
22600
17000
12700
7900
27600
21000
20500
7500
11000
23100
16421
26500
9000
25800
25200
26000
25700
20800
20100
20700
10900
22600
27500
12200
21000
20200
17200
18600
19000
17300
13500
10400
24700
14700
11088
12400
21800
16600
19558
17200
13300
15100
13300
11400
22700
24300
21100
22800
21800
24200
18900
24000
17300
10800
6800
26100
21700
6900
8300
14100
10600
24800
14000
9800
6300
8700
18800
17000
15909
20000
18100
16100
19700
10400
17293
14300
21000
14500
18300
11700
8600
8200
7800
13700
13800
11000
11000
11200
6100
7900
5900
6100
7500
8300
8800
12100
0
8600
11198
4600
10400
8700
12100
9700
4500
5400
8300
12700
8200
10500
8000
8700
10500
8500
6350
5400
6900
9800
22800
6400
10600
11500
15700
12200
10300
3800
7500
10700
20800
6000
7100
2500
16092
11600
10200
5100
8200
11200
6441
13500
6300
14600
11900
14800
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
310
1863
0
1352
1192
0
2653
0
966
0
1758
0
0
0
0
0
2721
0
0
0
619
0
0
0
1441
785
0
288
0
472
1000
2097
507
962
0
2511
0
613
0
0
0
786
570
374
1439
1485
982
0
0
213
337
3680
0
0
0
0
0
0
0
240
1257
0
0
0
70300
61000
55800
67300
70400
60100
78300
53500
58000
52400
52600
49200
52100
63800
48900
56100
55500
50100
51500
39000
59900
58000
64700
55800
41200
42000
60800
58800
60700
68900
61900
63900
64900
65500
53200
57300
52600
49100
65000
62900
66000
48100
53500
54800
53600
54900
56200
45800
60100
52000
53500
47300
71700
63700
59200
46300
51900
57900
58300
70200
50500
67100
67000
68000
23414
7477
7870
20065
8590
15285
10259
31084
36712
40039
19768
13046
12472
8522
11645
8554
19423
20441
25166
16465
4265
0
2564
4224
1012
4749
0
6849
6859
11750
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63362
37676
35125
48485
33926
42742
48887
77212
88852
93823
29229
22957
5574
0
0
72364
20800
22500
18800
12400
15200
13500
0
0
0
0
65000
57800
36821
23780
5811
0
350
88312
0
0
61055
8519
8907
0
0
0
960
82117
84618
28600
27000
24300
13016
32500
24400
16100
9700
16100
25800
18700
10500
21900
16600
11500
13280
20000
15500
0
0
0
0
0
0
0
0
0
0
0
0
79300
62800
63500
62100
88200
62000
26404
17972
0
32754
33452
19923
19310
29670
28088
26330
30562
37258
19035
25567
21051
20325
40932
7799
5475
4740
7248
6148
0
0
0
0
0
0
0
0
0
0
72982
72262
67556
74946
108859
21200
29000
27200
26100
29100
27200
15000
18100
17900
24100
19900
22200
23200
20100
13400
17200
15500
15400
15700
10800
12700
0
0
0
0
0
0
0
0
0
0
0
0
467
0
64100
74500
78300
73500
81200
72200
57700
24549
24503
14885
16629
6959
6439
0
0
0
0
60063
62417
22100
23000
26300
13400
13100
14100
14900
15500
17900
0
0
0
0
0
0
61300
61900
69000
38085
32661
33107
24214
6956
7489
0
0
0
542
101058
85654
21400
26600
22200
15800
18500
30300
16600
18000
15400
0
0
0
0
0
0
63700
75300
81300
Page 19
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
Appendix C
WANTAGE
WELWYN HATFIELD
HERTFORDSHIRE WEST
WITNEY
WYCOMBE
BASILDON
BROXBOURNE
CASTLE POINT
LUTON SOUTH
OXFORD EAST
SOUTHEND EAST
SOUTHEND WEST
THURROCK
WATFORD
ALDERSHOT
ARUNDEL
ASHFORD
BASINGSTOKE
BEXHILL & BATTLE
CANTERBURY
CHICHESTER
DARTFORD
DOVER
BERKSHIRE EAST
HAMPSHIRE EAST
SURREY EAST
FAREHAM
FAVERSHAM
FOLKESTONE & HYTHE
GRAVESHAM
GUILDFORD
HASTINGS & RYE
HORSHAM
ISLE OF WIGHT
LEWES
MAIDSTONE
MEDWAY
KENT MID
SUSSEX MID
MOLE VALLEY
NEWBURY
NEW FOREST
THANET NORTH
HAMPSHIRE NORTH WEST
SURREY NORTH WEST
READING EAST
READING WEST
ROMSEY & WATERSIDE
SEVENOAKS
SHOREHAM
THANET SOUTH
SURREY SOUTH WEST
TONBRIDGE & MALLING
TUNBRIDGE WELLS
WEALDEN
WINCHESTER
WINDSOR & MAIDENHEAD
WOKING
WOKINGHAM
BRIGHTON KEMPTOWN
BRIGHTON PAVILION
CHERTSY & WALTON
CRAWLEY
EASTBOURNE
EASTLEIGH
EPSOM & EWELL
ESHER
GILLINGHAM
GOSPORT
HAVANT
HOVE
PORTSMOUTH NORTH
PORTSMOUTH SOUTH
REIGATE
SLOUGH
SOUTHAMPTON ITCHEN
SOUTHAMPTON TEST
SPELTHORNE
WORTHING
BRIDGWATER
CHRISTCHURCH
CIRENCESTER & TEWKESBURY
DEVIZES
FALMOUTH & CAMBORNE
HONITON
NORTHAVON
CORNWALL NORTH
DEVON NORTH
DORSET NORTH
WILTSHIRE NORTH
36209
36494
40417
23136
26924
28067
7198
5994
9274
0
0
0
0
0
0
85728
88233
99254
100742
71677
61068
84046
68154
55297
34800
22581
27572
22100
29500
22800
32000
28600
15700
23967
19600
25000
17700
16100
29400
37700
21800
30900
33600
34300
29100
16100
27000
27200
30700
23600
22300
26300
23400
28000
28200
20100
21000
21166
27385
14200
18800
33500
20700
19400
17100
23146
14600
8700
36200
18900
15500
10600
12100
23100
7000
15800
7900
19800
22200
16300
7700
6900
8200
20900
11400
29600
11200
13000
18500
12923
15700
12200
15500
17800
18500
13900
9700
13100
10000
19900
15500
11000
18700
17700
13300
17600
17500
17300
17700
10300
12800
16800
21200
15500
14400
14900
14900
13100
18300
11700
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
488
0
0
0
0
155
0
0
0
0
651
1148
0
0
0
0
831
0
945
661
0
101
0
1879
151
0
1726
0
0
89500
66500
82900
59200
78200
90400
88000
75400
53100
77700
56900
66900
88600
55600
79800
82900
57900
86000
71800
85100
69200
56600
74100
72800
73600
55100
56900
75400
64300
86500
71700
56600
40151
47719
37668
41589
23308
24873
24025
29304
19486
19622
33447
23341
43765
23601
23684
21959
22989
17065
12419
30874
19698
18916
8297
0
7538
6811
0
5103
0
7077
5024
3778
7551
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
447
0
0
0
0
0
0
95935
123121
93597
100167
62457
71058
57663
68940
84266
57121
84511
26649
35138
32300
33222
38120
27822
30103
43183
30414
14037
25664
9031
15172
12574
27262
28454
22630
10307
7902
8183
8947
11553
10205
5453
0
12704
7783
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1766
66975
91958
68013
80395
87116
81701
72580
108740
69705
39234
29914
27031
29924
27203
20364
21262
26103
13772
28711
13108
13549
11754
0
0
5234
8822
6324
0
0
0
0
0
0
0
0
1219
0
0
0
30437
33592
31316
30263
13111
14904
18473
24922
12883
9083
11167
0
0
0
0
0
1607
0
0
0
80537
79103
84441
77136
27000
30400
31300
24326
7500
10900
16000
23483
34800
16200
23700
14900
0
0
0
0
0
0
0
0
85200
71600
87400
79100
25393
30380
10523
18647
7103
13279
0
0
0
0
58907
85801
27300
24800
24600
8000
9000
10000
15200
14500
23400
0
0
0
0
0
0
60400
58400
71900
16900
8000
9200
0
0
43600
28800
23700
20000
30600
29900
28200
17900
25700
24800
27200
23900
30800
31000
25200
11600
13400
17600
12700
15000
8400
11300
6300
14683
11700
5600
15700
15400
11600
13900
18400
13100
19200
16200
18400
9000
19200
14701
16200
15400
20300
16000
17700
0
0
0
0
0
0
0
0
0
0
0
0
0
0
463
0
0
0
754
0
0
251
0
0
0
0
1041
0
68300
68500
64000
77500
74300
68100
47600
62100
65000
68600
55100
82100
79000
67400
23500
21900
26600
31800
31500
28500
35800
21800
20900
19600
27400
30500
23527
26800
30100
16000
22000
23700
24800
33600
24800
25900
31200
27900
22500
32400
27600
16900
23000
30300
24600
19500
11300
14600
25000
5900
18400
10800
6000
14900
12300
13400
6400
23847
15800
13500
22900
27600
22300
16700
8300
16800
7500
13800
18000
18200
8800
21100
1700
6100
6000
9400
8000
9800
13600
18200
24000
17200
18900
11100
15100
7500
18200
18900
7300
10300
16100
10100
13200
12000
13600
14700
15300
11300
21000
16800
13000
18300
17300
25700
34100
23400
21600
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
170
428
0
0
0
0
0
0
0
0
675
442
0
394
254
1885
0
0
1399
0
0
0
0
0
0
0
0
0
0
0
0
64900
57000
67000
89900
74300
75200
79900
47200
61500
48400
74600
72700
68500
70400
72000
63200
81300
73400
68800
72200
69200
55000
79700
77100
66400
72600
78800
51400
73100
70800
67300
23598
36041
32654
43907
33434
25113
22444
13576
15376
16531
21709
8817
0
10322
12290
11769
7176
10178
0
0
0
0
0
0
867
0
0
0
2136
0
63322
83419
81635
100507
89093
60008
31890
38359
25249
32264
37220
17897
12014
11773
16214
18652
10167
12343
8867
6343
9763
0
0
0
0
0
0
0
0
0
0
83309
86537
61466
77634
93579
24208
24365
27239
41994
30296
30300
35541
37727
25813
21105
13771
15653
27706
8475
22248
12767
14449
18057
3833
0
5239
8574
23308
6825
9563
8845
0
0
0
0
0
0
0
0
0
0
0
1205
0
0
0
0
0
0
0
65414
59086
67724
105794
84208
75710
79874
84668
59742
43733
34287
16214
23962
28462
21436
0
24660
27266
33051
26685
26492
15639
17169
13847
15433
24103
0
22858
18239
8989
18224
10226
0
0
0
8952
3407
0
4349
4792
8336
6066
0
0
0
0
0
0
0
0
0
0
0
0
0
579
0
0
0
43792
0
0
0
0
112725
74855
46972
56126
71535
62941
80963
70732
68599
71935
66067
30217
28475
21477
32885
35045
19233
18524
28471
24371
16131
20442
19954
11072
26067
1741
5268
8626
10807
7593
6210
5843
11330
7680
19863
18893
12095
13833
0
0
0
0
0
0
0
0
0
0
0
960
0
0
0
175
0
0
73124
72396
62001
72118
87500
47905
50504
62614
63305
Page 20
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
Appendix C
ST IVES
SALISBURY
SOMERTON & FROME
DORSET SOUTH
CORNWALL SOUTH EAST
SOUTH HAMS
STROUD
TAUNTON
TEIGNBRIDGE
TIVERTON
DEVON WEST & TORRIDGE
TRURO
WANSDYKE
WELLS
WESTBURY
DORSET WEST
GLOUCESTERSHIRE WEST
WESTON-SUPER-MARE
WOODSPRING
YEOVIL
BATH
BOURNEMOUTH EAST
BOURNEMOUTH WEST
BRISTOL EAST
BRISTOL NORTH WEST
BRISTOL SOUTH
BRISTOL WEST
CHELTENHAM
EXETER
GLOUCESTER
KINGSWOOD
PLYMOUTH DEVONPORT
PLYMOUTH DRAKE
PLYMOUTH SUTTON
POOLE
SWINDON
TORBAY
BROMSGROVE
BURTON
CANNOCK & BURNTWOOD
HEREFORD
LEOMINSTER
LUDLOW
MERIDEN
STAFFORDSHIRE MID
WORCESTERSHIRE MID
SHROPSHIRE NORTH
WARWICKSHIRE NORTH
NUNEATON
RUGBY & KENILWORTH
SHREWSBURY & ATCHAM
STAFFORDSHIRE SOUTH EAST
STAFFORDSHIRE SOUTH
WORCESTERSHIRE SOUTH
STAFFORD
STAFFORDSHIRE MOORLANDS
STRATFORD-ON-AVON
WARWICK & LEAMINGTON
WYRE FOREST
ALDRIDGE-BROWNHILLS
BIRMINGHAM EDGBASTON
BIRMINGHAM ERDINGTON
BIRMINGHAM HALL GREEN
BIRMINGHAM HODGE HILL
BIRMINGHAM LADYWOOD
BIRMINGHAM NORTHFIELD
BIRMINGHAM PERRY BARR
BIRMINGHAM SELLY OAK
BIRMINGHAM SMALL HEATH
BIRMINGHAM SPARKBROOK
BIRMINGHAM YARDLEY
COVENTRY NORTH EAST
COVENTRY NORTH WEST
COVENTRY SOUTH EAST
COVENTRY SOUTH WEST
DUDLEY EAST
DUDLEY WEST
HALESOWEN & STOURBRIDGE
NEWCASTLE UNDER LYME
SOLIHULL
STOKE-ON-TRENT CENTRAL
STOKE-ON-TRENT NORTH
STOKE-ON-TRENT SOUTH
SUTTON COLDFIELD
WREKIN THE
WALSALL NORTH
WALSALL SOUTH
WARLEY EAST
WARLEY WEST
WEST BROMWICH EAST
18581
26549
25106
27580
20187
9913
17493
16335
20716
5350
7981
0
9174
4680
16267
0
0
0
0
0
0
0
0
0
0
48567
61317
65386
66953
51803
18300
22800
25400
26900
20274
9200
10500
14400
18300
5328
12900
16500
17600
12100
20283
0
0
0
0
0
177
0
0
0
0
51100
62200
69000
69900
55106
27089
26158
31519
24689
25846
24894
19158
17823
16429
10823
8982
16684
6799
4871
9515
9229
10397
8923
0
0
0
0
0
0
0
0
0
0
0
0
66081
61809
74944
58095
59941
65963
25600
23800
30600
27200
23500
23500
17100
15400
13200
8300
5100
12900
15500
13600
20900
21600
18300
20900
0
0
0
0
0
0
470
0
394
0
0
850
68800
63000
80100
69300
57000
71500
26524
21081
21530
33816
38975
27689
22344
31104
28714
23488
24124
15254
20110
22823
21680
21838
17413
10526
22637
14473
28121
20621
16493
10594
14099
29176
23075
24682
8175
14213
20409
20777
8781
7314
4932
10120
0
7418
5957
8182
8303
0
3299
0
5108
8431
6072
3935
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
840
0
0
0
227
0
0
0
0
0
67684
50651
63740
79306
84866
70236
59194
70448
73779
73034
64913
61441
50334
60189
59420
61164
21843
28636
20471
29383
0
0
0
0
0
0
59597
80223
31100
20155
33996
37544
27428
32665
22011
17630
22104
40077
17610
25731
15948
26670
23063
31136
14410
6321
12800
35353
9846
0
11163
0
0
0
4953
6462
5444
0
0
0
0
0
0
0
0
0
0
0
0
456
0
0
0
0
0
0
0
0
78927
61305
82703
83932
66790
90061
56389
41755
55066
99794
26200
21600
21100
33800
34600
26000
20900
22300
23500
18600
21600
11700
21100
21700
22800
23100
17000
15382
18400
21600
31200
15400
33200
33100
28300
12800
18700
16200
18700
36100
16500
8300
22800
13500
22400
17400
14300
7400
10100
26500
20900
24900
9500
13000
17700
18200
18600
15819
15800
13500
15400
24100
14400
29500
25000
23900
11300
4200
9000
40500
17800
14200
11900
20200
18000
18500
15700
13000
12700
9900
11300
7500
13100
15800
16300
10200
12500
6300
8800
12700
21100
10600
20800
10700
0
9700
15200
14600
10700
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
521
0
171
0
0
720
322
1709
0
1425
440
1006
0
0
0
366
0
0
0
0
0
240
0
0
0
0
0
0
0
0
73000
53100
66700
84400
88200
74600
61800
56600
60700
68900
65700
60400
60100
62100
66600
61900
55500
49800
55100
60600
82700
62200
85100
87100
67200
56100
57300
43800
48300
96400
20361
10801
8963
0
0
55641
20400
9700
13400
0
0
55900
18767
22086
22619
31274
32877
25041
13413
29298
5602
0
5960
0
0
0
0
0
0
254
0
0
75131
58123
57441
82257
30648
30056
27899
28106
36994
27667
12839
20380
26359
11393
21355
18297
7262
4370
6219
8895
0
7502
0
0
0
0
0
0
0
0
0
0
0
0
70395
74701
90109
65324
80363
72409
23690
11894
27319
15848
2523
32148
18083
18281
6923
11427
21827
24010
18344
13047
15456
17930
22559
5067
33364
16817
16758
13794
14773
21707
36275
24004
0
0
0
0
4087
0
0
0
1754
1813
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
725
704
0
1736
0
605
0
0
117
0
0
841
0
58832
47646
66684
62777
18734
96527
49370
54661
38947
43101
62339
86603
56726
27816
29163
43440
29403
20223
37756
11227
10542
13341
36774
26282
20128
35545
13968
15537
18976
30010
29499
28203
26499
22329
13181
18758
20642
20770
18134
25764
27543
24196
16077
25001
23412
0
0
0
0
1954
7795
0
0
0
9163
0
0
0
747
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1194
0
0
0
364
0
0
597
0
0
0
0
77626
81569
97759
77390
69811
80279
59941
58898
68064
93000
66087
72185
81883
45164
63885
68286
16800
19000
21100
30700
23900
28100
30100
31500
30100
30200
27100
19300
25900
16100
27300
13500
6200
23200
15900
19700
6900
12500
18600
15100
15400
11500
22472
15800
24500
26500
23000
35000
15400
15700
16000
28400
24100
17800
19200
17200
14300
16700
34300
25200
11500
28900
14100
9800
21100
25800
11200
18900
18380
19600
20000
23000
21000
23700
15100
31700
18000
16800
19300
19900
20600
30500
22100
22200
22985
27400
29100
22500
28600
11600
27200
28200
31700
6000
30600
32500
20800
24800
28900
21900
12600
6600
14900
14200
10400
21000
13100
11900
16900
14500
18230
11900
0
7000
0
7200
3800
0
6000
9700
7400
0
6900
0
0
4500
11300
0
0
17500
8900
17700
0
0
7600
14900
11500
0
6000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
106
0
0
482
850
0
0
0
0
0
0
2391
1145
0
280
751
1605
853
0
0
0
0
2170
1542
0
0
0
5971
0
228
0
0
0
481
0
0
819
0
0
0
2907
77200
59100
59600
89000
60600
73000
78100
83200
71300
77900
78400
61100
69700
65300
66500
62000
40200
76900
52100
62400
51100
49400
58600
63300
48900
50500
67500
59900
74200
81600
72100
79200
60700
59400
72000
59900
82000
70800
58800
57100
60800
57800
Page 21
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
Appendix C
WEST BROMWICH WEST
WOLVERHAMPTON NORTH EAST
WOLVERHAMPTON SOUTH EAST
WOLVERHAMPTON SOUTH WEST
WORCESTER
BOLTON WEST
CHORLEY
CHESTER CITY OF
CONGLETON
CREWE & NANTWICH
EDDISBURY
ELLESMERE PORT & NESTON
FYLDE
HALTON
HAZEL GROVE
HEYWOOD & MIDDLETON
KNOWSLEY NORTH
KNOWSLEY SOUTH
LANCASTER
LITTLEBOROUGH & SADDLEWO'
MACCLESFIELD
MAKERFIELD
MORECOMBE & LUNESDALE
RIBBLE VALLEY
SOUTH RIBBLE
STALYBRIDGE & HYDE
TATTON
LANCASHIRE WEST
WIRRAL SOUTH
WIRRAL WEST
WORSLEY
WYRE
ALTRINCHAM & SALE
ASHTON-UNDER-LYNE
BIRKENHEAD
BLACKBURN
BLACKPOOL NORTH
BLACKPOOL SOUTH
BOLTON NORTH EAST
BOLTON SOUTH EAST
BOOTLE
BURNLEY
BURY NORTH
BURY SOUTH
CHEADLE
CROSBY
DAVYHULME
DENTON & REDDISH
ECCLES
HYNDBURN
LEIGH
LIVERPOOL BROADGREEN
LIVERPOOL GARSTON
LIVERPOOL MOSSLEY HILL
LIVERPOOL RIVERSIDE
LIVERPOOL WALTON
LIVERPOOL WEST DERBY
MANCHESTER BLACKLEY
MANCHESTER CENTRAL
MANCHESTER GORTON
MANCHESTER WITHINGTON
MANCHESTER WYTHENSHAWE
OLDHAM CENTRAL & ROYTON
OLDHAM WEST
PENDLE
PRESTON
ROCHDALE
ROSSENDALE & DARWEN
ST HELENS NORTH
ST HELENS SOUTH
SALFORD EAST
SOUTHPORT
STOCKPORT
STRETFORD
WALLASEY
WARRINGTON NORTH
WARRINGTON SOUTH
WIGAN
ABERAVON
ALYN & DEESIDE
BLAENAU GWENT
BRECON & RADNOR
BRIDGEND
CAERNARFON
CAERPHILLY
CARMARTHEN
CEREDIGION & PEMBROKE N.
CLWYD NORTH WEST
CLWYD SOUTH WEST
15358
26240
26220
29717
23847
26577
25877
18678
20397
20366
17251
27240
11753
21275
29674
24900
18872
22160
15124
15746
0
0
2459
0
0
4428
4978
0
8595
3604
0
0
0
0
0
0
0
0
0
0
1592
0
318
0
0
334
0
0
0
0
51305
77060
53592
70010
69583
71230
67948
57515
56880
50608
11700
16300
10800
23100
27400
17900
25035
24500
16100
21500
19800
25100
28900
21700
16200
19900
30600
25440
17800
21300
16300
13500
0
0
5500
9700
12700
10900
12700
13100
8300
11700
10300
0
0
0
0
0
0
0
0
0
0
0
3107
3259
1546
1523
0
0
0
0
0
0
0
59200
68900
55000
63600
74200
71400
75500
69000
58800
60700
52100
39459
20841
39728
13354
28384
8062
9214
0
37974
0
0
0
0
0
0
85295
71633
107330
41000
19200
25000
13500
35700
6300
15600
0
27000
0
0
0
0
0
0
90100
74400
67100
24509
18584
24178
29023
14877
27442
20430
20480
19377
33194
40517
31260
38655
20867
33667
27904
19973
15151
19737
21273
22298
22769
19225
11496
14846
29796
25030
45583
16843
25081
18571
32295
13916
14158
19149
22226
11612
25486
30535
22197
29392
15235
16671
23927
20980
22473
13267
13062
22298
17981
20110
24200
26592
23988
0
2436
5620
8124
0
6792
4965
0
5303
10684
0
0
9276
0
0
7875
0
4926
0
5730
4946
0
0
0
3446
0
8175
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2122
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
351
0
0
0
0
0
0
0
0
0
107301
47596
72084
73000
66699
66305
49858
52253
63840
74231
91053
81913
94520
69554
71558
70755
61468
58250
55812
58882
58835
61243
50254
48467
56036
74534
76539
24042
28629
17594
18458
20234
15314
19127
28381
10804
12820
16124
16619
18838
6110
10726
18854
24505
16260
14387
19881
20102
12978
20448
25863
16509
9583
22958
18132
15546
24283
11647
25272
12882
18350
24614
23679
23913
20828
26625
10253
21456
14752
15276
20530
22324
21437
10912
13728
15365
30260
17020
16062
18471
17140
19247
18568
34873
31587
15853
8950
17261
13833
21172
20970
16204
28102
0
0
3013
0
0
0
11650
0
0
0
0
0
0
0
0
4540
0
0
2944
0
2458
14076
0
5678
0
3000
13809
4022
0
5577
0
5741
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
643
0
3776
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2946
0
0
672
59336
71062
61594
59212
51170
64436
59271
75818
43566
45123
53911
60546
57967
30490
40841
57130
78063
51031
49853
49051
51655
63500
49900
89413
74609
45679
64772
52649
45636
72097
50009
61400
57639
16500
17700
21100
32600
17100
27700
20600
15500
16900
23600
27000
27400
38500
15400
36600
26400
14700
13700
14000
20100
23900
18200
16600
12400
11300
31100
23840
27600
32500
23600
13300
14800
15000
12663
19000
24651
6900
8800
11800
12700
13900
5100
11200
18000
14500
12200
11600
15694
16542
7900
16040
23600
10800
7500
24000
16745
12300
22400
8400
26400
12300
31800
15200
28200
18400
39800
12782
12100
17400
24900
8800
34800
31900
22600
28100
16900
15600
23000
20700
20300
11700
15800
19800
16000
27300
21100
30800
24357
6600
16900
19600
22300
22500
20100
26300
13800
25332
13000
14400
20100
22700
19400
15100
16100
13600
26900
18500
17900
15515
16797
16400
15243
38400
32600
14400
6700
16948
14700
19900
19600
19100
30500
7600
6900
14400
15900
0
12948
10400
8000
0
12500
11900
12400
14100
12900
0
17700
9800
11400
5900
12000
0
8700
8300
6300
9500
0
12900
21300
12800
12600
7900
8000
7200
12594
11500
9800
7200
6700
4800
5700
8200
4300
4600
10900
6900
7700
7500
9166
7100
25300
10487
15900
10900
4500
20100
9283
8000
12700
6200
12000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
234
876
0
0
0
0
0
0
10900
0
0
0
0
0
0
0
0
0
1778
229
3720
1259
0
586
0
0
0
0
0
0
0
404
0
0
0
0
0
263
647
388
0
0
0
0
0
0
0
0
0
1885
0
0
991
0
0
0
206
0
0
0
0
72700
49300
77100
79500
76500
68000
51600
51200
65900
54700
95700
85800
92300
69900
74200
72700
59900
59900
53800
57600
59300
59600
50300
63500
52500
77100
76000
65000
77900
68100
55500
57000
50400
64500
59200
80100
40700
45600
51500
58100
54400
39500
47600
57700
64400
50400
47700
48000
50900
66500
50000
94500
75500
39800
65600
52400
51000
69400
46200
69300
56400
10419
20145
2146
13892
31314
24227
21817
18736
0
5888
4371
8169
3912
2332
1805
2349
1102
0
0
0
62481
64793
38496
52694
11000
18800
2300
15900
31700
27700
20700
18200
0
10700
5000
8700
5900
1100
1800
2100
0
0
0
0
64200
68700
37400
53900
6812
3917
4975
5715
21246
13627
24972
18719
11063
12537
2195
0
10707
9800
8636
11331
11505
14812
6498
5254
0
0
0
0
0
41560
51741
58824
40330
60761
5800
5900
6000
4758
21300
12400
24800
17165
11900
10100
2500
0
9700
14400
15200
14100
12000
17162
4754
4100
0
711
0
0
0
42200
56000
60000
42800
63000
Page 22
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
Appendix C
CONWY
CYNON VALLEY
DELYN
GOWER
ISLWYN
LLANELLI
MEIRIONNYDD NANT CONWY
MERTHYR TYDFIL & RHYMNEY
MONMOUTH
MONTGOMERY
NEATH
NEWPORT EAST
NEWPORT WEST
OGMORE
PEMBROKE
PONTYPRIDD
RHONDDA
TORFAEN
VALE OF GLAMORGAN
WREXHAM
YNYS MON
CARDIFF CENTRAL
CARDIFF NORTH
CARDIFF SOUTH & PENARTH
CARDIFF WEST
SWANSEA EAST
SWANSEA WEST
16927
2484
20999
9435
5799
5777
2965
3169
28312
7891
6765
22005
16024
22817
13655
26485
28078
31398
8861
9234
26957
5335
28378
30132
2626
0
7437
0
0
3834
5034
0
4061
10202
0
0
4311
11431
3108
5869
3780
8387
5425
3076
1501
3145
4012
1997
0
1317
0
0
0
603
0
16701
0
0
579
0
48662
48771
58115
54317
49157
64616
26436
41299
75602
32304
52744
71520
16800
3200
22000
8780
5000
7496
3392
2600
27300
7800
6600
18000
12200
23800
14900
23900
26700
28900
7235
20500
22700
4900
25400
29400
8500
0
12800
8737
5000
7140
4153
1000
11500
12500
0
11900
4200
12000
2300
3700
3000
6020
7823
7300
900
2300
8800
900
0
1038
0
0
0
507
0
529
0
0
0
0
51400
48000
63900
56500
49800
64100
26500
39500
74200
33300
51900
74600
10415
19120
8205
1359
8869
31957
15649
9220
21983
33436
17889
28414
19602
27402
23286
31089
13966
20207
0
2541
6871
0
0
0
5067
2013
2701
5828
3681
5059
6931
2053
4200
2894
7140
1927
0
11824
0
659
435
0
0
0
0
65731
70663
65265
36836
53878
75049
72814
41334
61133
20771
15878
8191
21384
26226
21655
28183
24622
0
1594
0
0
2585
4378
4188
3033
982
0
563
0
69122
61322
58653
64686
9400
22300
11400
4100
7500
25300
14301
8900
14700
16700
13500
13400
8900
18800
28400
20800
28000
36900
25100
19800
27400
14700
10800
10600
20600
16700
28500
22100
10800
12300
9900
3100
7700
10000
14297
3900
7100
7100
3000
5800
0
8200
5100
2800
4600
6700
1300
1900
2600
7600
1600
1200
1300
2100
5100
1900
0
0
0
1374
498
0
0
0
0
0
3800
0
507
0
67400
71500
69700
65200
54700
69400
75500
43700
43500
43400
56800
51600
58300
64800
ANGUS EAST
ARGYLL & BUTE
AYR
BANFF & BUCHAN
CAITHNESS & SUTHERLAND
CARRICK CUMNOCK & DOON VA
FIFE CENTRAL
CLACKMANNAN
CLYDEBANK & MILNGAVIE
CLYDESDALE
CUMBERNAULD & KILSYTH
CUNNINGHAME NORTH
CUNNINGHAME SOUTH
DUMBARTON
DUMFRIES
DUNFERMLINE EAST
DUNFERMLINE WEST
EAST KILBRIDE
EAST LOTHIAN
EASTWOOD
FALKIRK EAST
FALKIRK WEST
GALLOWAY & UPPER NITHSDAL
GORDON
INVERNESS NAIRN & LOCHABE
KILMARNOCK & LOUDOUN
KINCARDINE & DEESIDE
KIRKCALDY
LINLITHGOW
LIVINGSTON
MIDLOTHIAN
MORAY
FIFE NORTH EAST
TAYSIDE NORTH
ORKNEY & SHETLAND
PERTH & KINROSS
RENFREW WEST & INVERCLYDE
ROSS CROMARTY AND SKYE
ROXBURGH & BERWICKSHIRE
STIRLING
STRATHKELVIN & BEARSDEN
TWEEDDALE ETTRICK & LAUDE
WESTERN ISLES
ABERDEEN NORTH
ABERDEEN SOUTH
DUNDEE EAST
DUNDEE WEST
EDINBURGH CENTRAL
EDINBURGH EAST
EDINBURGH LEITH
EDINBURGH PENTLANDS
EDINBURGH SOUTH
EDINBURGH WEST
GLASGOW CATHCART
GLASGOW CENTRAL
GLASGOW GARSCADDEN
GLASGOW GOVAN
GLASGOW HILLHEAD
GLASGOW MARYHILL
GLASGOW POLLOK
GLASGOW PROVAN
GLASGOW RUTHERGLEN
20439
13521
22220
12866
5334
11675
12837
13178
7557
7633
17770
5656
8768
23910
29929
23729
0
0
0
3548
6063
0
0
2640
8409
9039
2186
9377
3690
3103
5386
7243
0
0
0
0
0
0
855
0
49293
40644
51691
45711
28710
50324
66028
61877
20500
12300
21600
12600
5100
10600
9100
10000
9800
14700
5700
4000
16500
2400
8600
23100
24400
18700
16400
16800
0
0
0
2700
6200
0
0
0
2600
0
15200
15600
4700
18300
3800
6600
10300
22300
5906
8800
0
0
0
0
0
0
2019
322
5980
0
51900
41500
51600
47100
28600
50800
57900
62100
49000
48000
18853
19569
16783
24661
12459
24536
23009
15555
0
0
0
0
3852
2383
5414
6211
0
339
0
0
47787
58024
58138
60980
17200
17300
13600
21700
10400
23600
16200
12700
3800
0
0
5600
6100
7300
11100
9200
0
0
0
0
48700
58800
51600
61300
12086
27721
20466
29163
21532
30194
21107
16062
0
0
0
7053
3657
7859
4735
3733
462
1273
0
0
51026
84384
55326
73508
14800
15500
21200
25700
19200
23400
20700
10200
6200
0
0
9600
8700
13800
7000
5300
0
693
0
0
60200
65100
57000
61300
15754
14003
18396
12378
11476
14687
13193
10048
22984
5665
6141
9038
24477
5092
22986
29360
0
2461
12847
15052
2459
3212
0
0
6571
5723
2112
2781
2836
4677
4863
15620
0
0
0
0
0
0
0
459
61943
38661
52108
54298
52091
37066
55098
72894
12200
13300
17300
11700
13800
14300
13100
11800
21700
3100
4700
7300
23500
3700
22500
28100
0
4600
15600
16900
4900
4400
0
0
17900
9300
6800
7800
7600
6800
12300
21700
0
0
0
0
0
0
0
438
63800
39000
54700
56900
59900
37200
60300
76900
18328
13994
21619
14434
5364
21860
20695
6418
30802
6452
9756
3827
3552
9972
22999
5023
0
0
3577
2228
7896
3011
0
5617
9047
7885
4666
4670
0
7112
4195
2268
0
0
0
0
0
0
0
0
76931
39268
53258
33944
25596
56984
60264
26947
20500
14200
21200
14400
4200
21200
19500
7900
32200
2300
6600
2700
2900
6800
22200
4300
0
0
7800
3900
11500
4600
5000
4600
19500
16000
8600
6300
0
12200
8400
5000
0
0
0
0
0
0
0
0
88400
40800
56100
35000
26100
57200
66500
29200
11465
26972
18974
2822
9807
23843
19832
19449
8000
16657
10682
21829
19851
26864
29093
3071
14487
6301
14674
6638
18708
11881
14710
18884
32527
4454
5842
27707
22754
22630
26271
9561
22171
12066
18646
15071
19523
24188
10260
26492
13443
7303
18925
19311
25864
17751
0
3460
19524
0
2835
3135
0
0
1486
0
1490
4055
3469
4467
0
0
0
0
0
0
0
0
0
8279
8257
3147
6568
3756
2777
4181
4441
1666
3502
1827
2814
2861
3711
0
1089
4313
2294
1957
3273
3733
4181
0
0
1656
103
0
521
0
176
809
0
413
0
0
0
0
419
376
846
326
0
0
0
601
1490
48938
93873
57249
23518
63833
68020
61242
66470
31364
57350
35614
61498
55675
72709
72155
24786
65586
35378
34451
45221
57624
65066
42702
12789
19100
16600
1042
8100
21900
13400
15700
10400
14600
11900
18200
18800
18900
18200
7500
9800
3000
14400
6600
17700
6300
14900
17700
15400
3100
2900
23200
18400
17100
22200
11400
20200
12600
13600
12400
10400
16200
15900
21000
10300
8000
20300
21100
23200
19000
0
5900
25700
0
6000
7400
0
0
4200
4000
0
6900
8100
9200
0
0
0
800
6600
0
0
0
0
12886
11600
4000
10100
11300
7600
20100
13000
4100
7100
6600
5500
5800
4200
5400
4400
8800
9800
3700
8900
6600
7400
6100
0
0
0
1031
0
0
220
675
0
274
0
0
0
0
0
462
635
0
0
0
377
749
0
52500
61300
57300
22300
64400
67400
62600
63500
40800
56900
39200
54500
55700
52000
49400
38600
54300
31900
41600
51300
59000
54500
48400
Page 23
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
Appendix C
GLASGOW SHETTLESTON
7969 17840
0
GLASGOW SPRINGBURN
4574 14968
0
GREENOCK & PORT GLASGOW
0 19334 16100
HAMILTON
5455 25431
0
MONKLANDS EAST
15574 26117
0
MONKLANDS WEST
16963 21982
0
MOTHERWELL NORTH
15720 26431
0
MOTHERWELL SOUTH
12509 20683
0
PAISLEY NORTH
PAISLEY SOUTH
15232 25429
2918
The following constituencies did not exist after the 1979 general election
ABERTILLERY
3478 22819
0
BANFF
8457
3795
4589
BATTERSEA,SOUTH
9227 10925
1183
BIRMINGHAM,HANDSWOR
16122 14310
0
BRISTOL,NORTH EAST
23254 22792
0
CONSETT
11914 28985
0
DARWEN
26728 17634
6663
EDINBURGH NORTH
13005
9127
2475
GLASGOW CENTRAL
2394
7936
0
GLASGOW CRAIGTON
13661 20872
0
GLASGOW KELVINGROVE
5274
6106
0
HACKNEY CENTRAL
9339 17380
0
HARROW,CENTRAL
16525 12561
3449
HUDDERSFIELD,WEST
16673 16866
6128
ISLINGTON CENTRAL
8660 13980
0
LAMBETH CENTRAL
9727 13053
0
LEEDS,SOUTH EAST
5182 10930
1135
LIVERPOOL,KIRKDALE
13615 17678
0
LIVERPOOL,SCOTLAND
3740 11074
0
MANCHESTER,OPENSHAW
12296 19397
0
NEWCASTLE/TYNE CENT
4256 13671
1433
RIPON
21211
9147
4583
SALFORD,WEST
14310 16986
0
ST.MARYLEBONE
17639
8325
2443
ST.PANCRAS NORTH
10648 16497
0
STOCKPORT SOUTH
14679 16747
4613
WOOD GREEN
14022 18666
0
The following constituencies did not exist after the 1970 general election
BARONS COURT
12269 13374
2206
BIRMINGHAM,ALL SAIN
7762 12041
2271
BRADFORD,EAST
8208 17346
660
BRISTOL,CENTRAL
9130 12375
2569
CENTRAL NORFOLK
32921 19030
6172
EALING,SOUTH
19326 12042
3784
EAST HAM,SOUTH
8402 13638
0
ENFIELD,EAST
12403 16433
3373
GLASGOW BRIDGETON
3801 11056
0
GLASGOW WOODSIDE
9457 10785
0
HESTON AND ISLEWORT
21580 16981
0
KENSINGTON,SOUTH
21591
6928
0
LEICESTER,SOUTH WES
14611 14505
2124
LEWISHAM,SOUTH
13665 19217
0
LIVERPOOL,EXCHANGE
4638 12995
0
MANCHESTER,EXCHANGE
3341
8234
0
MERTON AND MORDEN
18727 15244
2876
PADDINGTON,SOUTH
10526
7913
1367
POPLAR
4036 16520
0
RHONDDA,WEST
1610 18779
0
SEDGEFIELD
24036 36867
0
SOUTHWARK
7040 16834
0
STOCKTON-ON-TEES
17960 22283
0
TORRINGTON
21328
6695 11455
WALTHAMSTOW,EAST
14260 13732
2547
WANDSWORTH,CLAPHAM
16593 13473
2982
WEDNESBURY
20627 23998
0
WEMBLEY,SOUTH
16578 14336
0
ANTRIM EAST
ANTRIM NORTH
ANTRIM SOUTH
BELFAST EAST
BELFAST NORTH
BELFAST SOUTH
BELFAST WEST
DOWN NORTH
DOWN SOUTH
FERMANAGH AND SOUTH TYRONE
FOYLE
LAGAN VALLEY
LONDONDERRY EAST
NEWRY AND ARMAGH
STRANGFORD
ULTER MID
UPPER BANN
3995
3323
0
16849
2667
3486
6157
3861
0
423
559
295
0
0
0
1829
46899
38061
47441
60182
58157
54589
64135
52888
6500
7500
7900
8000
13200
14700
12700
12000
14200
18100
20600
19100
24900
21400
22300
18300
0
0
8800
0
0
0
5400
0
5800
7700
4900
12700
8000
8200
6700
7900
0
478
483
0
0
0
562
1066
3432
0
65744
14900
23800
0
10500
0
1751
5006
0
0
0
0
0
0
1688
2946
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
716
0
0
0
0
0
0
0
0
252
358
1427
0
0
198
0
0
552
0
0
0
0
670
0
0
37388
31705
34650
46573
63847
58289
66494
35099
20312
50050
18935
53120
45863
53107
43674
43346
29876
49136
29292
50412
31501
47404
47735
47640
50108
49141
53653
2700
8300
12800
12700
12500
9100
25500
16400
3400
10800
10700
6300
15300
16800
7000
8600
8400
9900
3000
9000
4200
21100
10300
15700
7900
12600
11000
20100
1500
14400
14300
18600
27400
16200
9400
9400
18100
13100
18700
12400
17400
15700
16000
17800
16400
15300
16500
12200
4600
16800
7000
14800
15700
18600
2600
3100
5900
5600
8100
8400
15100
5500
1000
0
0
5300
7600
9800
6400
5200
7000
4900
1600
4500
0
16700
5600
5600
4800
9200
7200
3100
11000
0
0
0
0
0
4600
2200
6300
5700
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
787
334
0
0
0
0
0
0
0
0
823
0
0
444
405
0
505
853
0
0
0
470
466
0
0
0
0
0
0
0
0
0
0
1550
1912
0
0
0
0
0
0
0
0
0
3528
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1180
614
0
0
749
821
775
440
0
0
0
1201
0
1128
369
0
0
300
0
0
41375
36287
40407
36090
75786
53763
39964
48303
31262
35711
53902
57130
44811
51183
34421
21074
50504
34472
42115
30811
83776
51845
55494
48903
43043
52999
65453
44797
624
625
626
627
628
629
630
631
632
65500 633
36300
31600
46500
45400
51600
58600
70000
46900
25400
44000
42500
48300
44900
53100
45100
48500
49500
45000
34800
42200
25000
49800
45500
43500
41400
47400
51700
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
21451
59589
26778
28668
27523
27451
55679
34894
31390
39141
6476
19971
18259
18894
11567
0
14246
0
0
0
2269
913
0
0
0
0
1076
7747
0
0
0
0
0
0
0
0
0
0
0
0
28442
16975
0
11614
0
30649
9729
21676
32813
34571
79930
143274
59524
75740
57112
68665
121196
87079
70381
90302
37667
8781
0
0
21696
86847
61760
31810
0
0
0
32779
77143
66721
Page 24
38200
47800
61500
49900
59200
53500
58700
50900
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
64989
71332
57518
49206
51890
47667
62488
59620
61654
62781
689
690
691
692
693
694
695
696
697
698
699
700
701
90255 702
703
80980 704
705
102985
116700
78821
71081
74534
65651
92790
90589
70570
92199
Appendix C
British General election results 1955 to 1987
Political party and electoral roll Conservativ Labour
Parliamentary constituency area
BARROW & FURNESS
BERWICK-UPON-TWEED
BISHOP AUCKLAND
DURHAM CITY OF
COPELAND
EASINGTON
HEXHAM
LANGBAURGH
DURHAM NORTH
DURHAM NORTH WEST
PENRITH & THE BORDER
SEDGEFIELD
WANSBECK
WESTMORLAND & LONSDALE
WORKINGTON
BLAYDON
BLYTH VALLEY
CARLISLE
DARLINGTON
GATESHEAD EAST
HARTLEPOOL
HOUGHTON & WASHINGTON
JARROW
MIDDLESBROUGH
NEWCASTLE UPON TYNE CENTL
NEWCASTLE UPON TYNE EAST
NEWCASTLE UPON TYNE NORTH
REDCAR
SOUTH SHIELDS
STOCKTON NORTH
STOCKTON SOUTH
SUNDERLAND NORTH
SUNDERLAND SOUTH
TYNE BRIDGE
TYNEMOUTH
WALLSEND
BARNSLEY WEST & PENISTONE
BEVERLEY
BOOTHFERRY
BRIDLINGTON
BRIGG & CLEETHORPES
CALDER VALLEY
COLNE VALLEY
DEWSBURY
DONCASTER NORTH
DON VALLEY
ELMET
HARROGATE
HEMSWORTH
KEIGHLEY
NORMANTON
PONTEFRACT & CASTLEFORD
RICHMOND (YORKS)
ROTHER VALLEY
RYEDALE
SCARBOROUGH
SELBY
SHEFFIELD HALLAM
SHEFFIELD HILLSBOROUGH
SHIPLEY
SKIPTON & RIPON
WENTWORTH
BARNSLEY CENTRAL
BARNSLEY EAST
BATLEY & SPEN
BRADFORD NORTH
BRADFORD SOUTH
BRADFORD WEST
DONCASTER CENTRAL
GLANFORD & SCUNTHORPE
GREAT GRIMSBY
HALIFAX
HUDDERSFIELD
HULL EAST
HULL NORTH
HULL WEST
LEEDS CENTRAL
LEEDS EAST
LEEDS NORTH EAST
LEEDS NORTH WEST
LEEDS WEST
LEEDS SOUTH & MORLEY
PUDSEY
ROTHERHAM
SHEFFIELD ATTERCLIFFE
Liberal
Nationalis Other par Registered
October 1974
electors
Conservative
Labour
Liberal
Nationalis Other par Registered
1979
electors
14253
14611
16086
13189
11899
8047
21352
19973
8268
9197
23547
21607
4768
27181
31305
21832
28984
16711
18445
33511
27953
9791
5788
14684
8168
9011
5563
7005
10991
7795
9233
6418
7215
0
0
0
0
0
0
0
0
0
0
0
384
0
0
0
0
0
0
0
0
0
0
54527
41851
72561
74692
50949
63798
65069
60659
68335
61267
55587
14946
13663
21160
19666
17171
11981
25483
26735
16112
14245
26940
22687
2602
27200
30903
22626
29537
16935
19818
38672
29525
9844
4983
19351
7439
8562
2559
6979
10697
5870
9247
4394
7257
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
790
0
0
0
0
0
0
54451
42493
74696
77463
52787
65242
66798
65488
83415
63387
57210
8009
20559
12988
10277
6590
14825
17620
10021
16546
4399
8707
8984
11217
11063
14983
12774
11667
18488
17482
13947
15593
4432
24510
15911
12011
28206
19583
21901
19819
14325
7337
13477
14747
6046
30498
24583
5895
16488
9739
6966
23156
11893
24779
19831
12707
26083
8718
18518
17822
22696
7028
22539
23743
20308
21079
21332
27620
24440
29699
24558
22791
10748
17312
30057
23204
26492
32962
22130
29618
28623
13859
21389
37180
27146
12383
7371
9946
14747
14971
20331
20378
22177
33315
20557
8047
37467
19569
24372
30208
8025
44670
10842
9923
26804
15419
21026
15482
8109
4866
12844
4728
7439
8177
5306
7882
6998
6314
9298
5818
5080
4189
4391
7945
7101
8106
6906
5442
7077
7828
1909
10895
10453
10900
16566
14803
11795
16939
9136
21997
10991
6336
5588
12483
11269
5607
5839
7384
5259
9528
9828
10917
10123
5285
11724
4912
8094
17232
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20230
0
0
0
0
0
0
0
0
0
0
0
711
750
0
0
0
0
0
435
0
0
0
987
0
157
0
0
0
0
0
1749
0
1038
0
457
0
0
0
0
0
0
0
0
0
48506
55867
53101
56896
74449
52305
62940
63478
65325
59889
54721
60235
40232
45640
76945
62345
72565
85490
62312
75560
76464
30762
76431
90275
67043
76236
57498
65745
70483
48735
60761
61493
59448
63249
83782
64743
69788
51727
58922
60271
61986
91938
63841
58540
64614
77385
52022
51993
52548
9913
25274
18767
16178
14194
16777
21513
14078
18877
9105
12529
13463
12721
12087
21591
17417
15551
23790
18073
16311
21002
5221
29941
21695
18309
34525
26550
27988
25701
16797
14450
18448
19208
9048
40381
30551
10466
19620
14398
10665
28958
19984
32520
23669
16439
31436
12206
22641
23177
21744
6497
24523
24687
25047
21343
22565
28776
27039
30181
24057
24872
11010
18257
32827
25470
28675
34917
23597
29213
29403
13533
22377
38214
28010
12743
8827
12693
12316
15617
17799
22829
22184
31783
21670
8221
36509
19698
26591
30566
8173
45986
11924
11344
27690
16502
20556
14281
4632
6972
12867
2819
5364
5176
4829
5054
4201
3193
4479
3907
4023
2983
2818
5801
4225
6003
6074
4255
5238
4984
1185
5736
8514
10772
14637
12006
10390
19026
7369
20151
7580
3646
5352
7909
12021
5474
4062
6134
3616
9964
7937
10533
9025
3976
8982
3088
5673
17484
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17987
0
444
0
0
326
2621
1879
0
0
0
333
0
970
251
0
0
186
0
884
0
0
0
0
261
0
101
0
300
0
1829
585
0
442
0
0
0
0
0
487
0
300
326
486
0
49881
59956
55030
58463
79727
53703
63218
62475
65732
60491
55619
65179
39307
44484
83156
62511
70566
89206
61783
72994
79130
29037
74722
91312
72370
80058
63708
68849
73264
49275
64139
63379
60487
64183
91890
69182
71581
54428
61390
60697
65319
99029
71905
60896
66042
78901
51088
53516
53881
9400
34212
8753
0
0
76551
13654
36276
5751
0
986
77699
19787
14252
16944
16192
13767
22187
14675
16798
11108
10397
12596
10272
6388
12434
18749
19243
7907
12931
20180
8840
8043
21964
22841
25219
21133
41187
28929
21657
20976
19522
34190
22417
20393
21653
24745
13121
15216
20669
21179
15293
25874
29601
8265
9475
10306
5884
10161
12452
9481
8693
7326
10196
7810
6508
5563
6970
6737
8663
13062
8928
15599
5350
5282
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
339
0
0
166
919
764
0
0
0
0
327
0
0
0
0
0
0
0
63631
66117
73252
62669
88753
90137
66284
63548
53502
81598
63262
57577
52689
67718
58954
65047
60388
61882
65338
61197
63903
23448
17548
22005
16554
22243
31130
20041
20182
15945
15719
14725
11592
8058
15810
20297
23837
11626
17632
24591
13145
11599
21714
25069
26323
24309
39603
30644
26282
21416
19040
39411
22318
19750
22388
26346
14913
17623
21290
22984
13727
26580
29702
7278
5819
7127
3668
8238
7764
3837
6853
4890
7543
5069
3656
3568
4622
5329
7899
9734
4943
15852
3686
4017
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
772
422
633
1118
2165
351
455
243
374
696
411
416
754
916
847
466
1308
340
490
457
65415
68530
76353
65407
95287
94822
66644
63768
53983
89023
60801
52489
50119
67048
59113
68702
58798
62871
67853
60871
62984
Page 25
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2
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8
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10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
Appendix C
SHEFFIELD BRIGHTSIDE
SHEFFIELD CENTRAL
SHEFFIELD HEELEY
WAKEFIELD
YORK
AMBER VALLEY
ASHFIELD
BASSETLAW
BLABY
BOLSOVER
BOSWORTH
BROXTOWE
CORBY
DAVENTRY
LINDSEY EAST
EREWASH
GAINSBOROUGH & HORNCASTLE
GEDLING
GRANTHAM
HARBOROUGH
HIGH PEAK
HOLLAND WITH BOSTON
KETTERING
LOUGHBOROUGH
MANSFIELD
NEWARK
DERBYSHIRE NORTH EAST
LEICESTERSHIRE NORTH WEST
RUSHCLIFFE
RUTLAND & MELTON
SHERWOOD
DERBYSHIRE SOUTH
STAMFORD & SPALDING
WELLINGBOROUGH
DERBYSHIRE WEST
CHESTERFIELD
DERBY NORTH
DERBY SOUTH
LEICESTER EAST
LEICESTER SOUTH
LEICESTER WEST
LINCOLN
NORTHAMPTON NORTH
NORTHAMPTON SOUTH
NOTTINGHAM EAST
NOTTINGHAM NORTH
NOTTINGHAM SOUTH
BURY ST. EDMUNDS
SUFFOLK CENTRAL
GREAT YARMOUTH
HUNTINGDON
NORFOLK MID
CAMBRIDGESHIRE NORTH EAST
NORFOLK NORTH
NORFOLK NORTH WEST
CAMBRIDGESHIRE SOUTH EAST
NORFOLK SOUTH
SUFFOLK SOUTH
CAMBRIDGESHIRE SOUTH WEST
NORFOLK SOUTH WEST
SUFFOLK COASTAL
WAVENEY
CAMBRIDGE
IPSWICH
NORWICH NORTH
NORWICH SOUTH
PETERBOROUGH
BARKING
BATTERSEA
BECKENHAM
BETHNAL GREEN & STEPNEY
BEXLEYHEATH
BOW & POPLAR
BRENT EAST
BRENT NORTH
BRENT SOUTH
BRENTFORD & ISLEWORTH
CARSHALTON & WALLINGTON
CHELSEA
CHINGFORD
CHIPPING BARNET
CHISLEHURST
CROYDON CENTRAL
CROYDON NORTH EAST
CROYDON NORTH WEST
CROYDON SOUTH
DAGENHAM
DULWICH
EALING ACTON
EALING NORTH
4905
5539
15322
12810
23294
15295
12452
16494
25405
6209
28490
25095
18108
30057
24728
25616
26983
31153
35367
28663
13244
27275
28188
24974
3271
6093
7151
8304
7370
9671
7959
7821
12290
5176
12082
9658
0
0
0
0
0
0
0
0
0
0
0
0
10182
403
723
0
475
0
0
408
0
0
0
0
54084
67412
65231
66518
77153
74962
74683
71707
65057
51866
83800
74156
7979
7159
19845
19571
25453
21160
25319
22247
33221
10116
37030
33273
25672
27483
24618
27124
26703
29760
33116
29426
12581
27495
28595
23077
3482
4737
4708
6059
6752
7879
3914
6913
9277
3688
10032
6935
0
0
0
0
0
0
0
0
0
0
0
0
354
692
274
530
790
0
397
0
2056
0
682
0
54459
62174
63996
70509
76832
75305
77878
73763
68576
52740
90290
77802
29801
16750
18856
19163
24638
27738
25776
19043
28145
19800
20739
6849
17851
11797
20019
19708
11934
17041
19461
30970
13640
11506
6404
15195
9859
10752
12567
9875
10476
12038
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1273
0
0
0
0
0
83233
49616
53727
61732
71763
78386
65837
57081
80435
85779
41422
21362
24004
24040
31762
36697
33328
22532
35440
30101
19939
6240
16617
10335
18989
18547
11350
17777
17908
31579
11286
10833
5518
16885
9077
10852
9529
8200
10480
8424
0
0
0
0
0
0
0
0
0
0
522
319
498
570
606
0
1002
0
0
0
90892
51531
57516
65654
76593
84479
68989
59323
85578
88396
11685
20827
14997
20521
27074
30943
28964
26598
25238
22869
12131
16747
9358
8116
10336
10409
10300
15567
0
0
0
0
0
0
448
0
0
1340
0
0
69536
71329
66852
70226
63961
82117
17720
27711
21889
29788
34196
40242
29051
25960
27218
24589
11712
15882
8536
6773
7436
6650
9060
12596
0
0
0
0
0
0
259
0
0
1079
0
0
71851
75612
70767
76455
67286
87319
21681
19101
29078
18468
13393
22767
17010
16877
20455
13446
11223
14776
14393
10574
17853
18108
32179
22387
22573
26989
27365
12111
27320
9456
30953
26960
26342
20688
21588
23406
14698
16314
14252
16530
24694
27373
21097
13948
20313
17745
9017
10113
11500
10622
7349
10595
7520
5668
5709
5135
0
6160
4842
4442
7470
9598
10631
14530
9250
15152
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
242
793
2967
2208
2253
13714
0
0
736
1317
0
0
0
0
0
71181
54642
85268
49130
71194
82674
74325
63881
72539
64632
53008
49017
44333
53777
76472
77656
87300
65694
70785
79705
27193
26198
37812
21478
17445
28583
20853
20988
22550
17194
17777
18597
19125
12199
21956
23801
41426
28707
28066
40193
26311
11383
25278
8134
31049
28797
26945
23844
24548
26032
17175
13934
15491
15433
25028
26301
21167
13686
20838
18630
7331
8801
8506
11261
5617
6093
5196
4623
4856
4032
5638
5659
3478
2270
4900
5497
10386
12259
6112
12812
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
460
0
738
0
0
708
855
1385
940
1308
1927
373
407
678
1525
910
0
592
640
983
73341
59404
89022
50655
73738
83580
75422
67269
70714
67209
55194
50739
51151
47838
78996
77155
95621
69843
72154
93862
19355
33312
27513
30508
31478
30049
11420
22191
26170
17853
22713
17986
22040
13776
8862
15841
14687
15206
0
0
0
0
0
0
0
0
0
0
317
0
68473
90507
79725
84416
90789
84267
23067
43952
33796
41218
42792
39544
7067
22126
25868
17929
23755
18972
26397
10643
6588
13780
11990
13435
0
0
0
0
0
0
0
548
0
0
0
0
69954
98135
83702
93179
100243
91441
19778
14850
6658
0
0
53706
24767
14063
6363
0
0
57901
25510
21790
29833
8754
13185
19972
5256
6019
19798
2995
17399
3183
11554
24853
10558
22527
22538
19674
19022
19661
18926
20390
17938
16035
25703
7684
14331
17669
21652
23448
19017
31566
17958
16590
21820
21546
17161
11140
19649
15412
24159
20481
17541
21611
22295
18840
6507
14377
11795
15032
20226
15787
14556
7203
29678
21790
16861
24574
11165
11129
8295
5378
5429
7302
5245
3048
10578
3700
6882
3181
4416
8158
3929
6019
8272
5758
8438
8884
6902
7834
7228
6563
11514
7564
7866
4569
8351
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
885
0
0
0
0
1661
1352
0
2172
0
617
1478
1297
1388
1362
0
321
0
1207
0
0
451
1049
0
569
0
0
0
76919
75932
87651
45068
44850
63028
50027
44791
59497
53753
51009
60446
63157
72104
61230
71186
66838
64548
56972