The cartographic method
• Cartography
• Context
The cartographic method
• Understanding
• Data analysis and design
• Maps at work
Menno-Jan Kraak
• Beware!
Faculty of Geoinformation Science and Earth Observation
Cartography
Why maps?
French troops
visualization process
cartographic method
How do I say What to Whom,
and is it effective?
November 26
Geographic
Data
Map
dorp
Cartography is the art,
art, science
scienceand
andtechnology
technology
of of
making
making
and
and
using
using
maps
maps
Why maps?
Why maps?
• Maps are abstractions or models of reality, in which geographic
space is represented by map space
• Make invisible things visible
• The spatial layout of maps enable users to see:
• patterns
• relationships
• trends
1985
1965
weather: wind speed
http://www.knmi.nl/actueel/
personal landscape preferences
http://www.daarmoetikzijn.nl/
Maps, constraints and variables
goal / purpose
of map
user
charactersitics
use environment
data
characterisitcs
Context
contents
scale
projection
map design
accuracy
interface
Context: maps to present
Presentation
• start: facts to be presented
are fixed
• For presentation, communication of the map contents can be
optimized by applying cartographic design rules for the translation of
data into graphics
• process: choice of
appropriate visualization
technique
• Guided by: ‘How do I say what to whom, and is it effective?’
• result: high quality
visualization presenting
facts, the single best map
What
Cartographic
Information
Analysis
• emphasize: on map design
I
Information
Effective
Info retrieved
Evaluation
Cartographer
User
How
Cartographic
Sign
System
Whom
Map
Say
Context: maps to explore
Exploration
• start: data without
hypothesis about the data
• Map as interface to the data
• process: interactive,
undirected search for
structures and trends
• result: visualization that
provides hypothesis,
different alternative views
• emphasize: enabling
‘discoveries’
• Map in context
Exploration
What
Cartographic
Information
Analysis
I
Information
Effective
Info retrieved
Evaluation
I
Cartographer
Cartographer
User
Understanding
Whom
User
How
Cartographic
Sign
System
Whom
Map
Say
Understanding maps
Understanding which map to use
40 minutes by
underground
3 minutes
walking
Understanding what to map?
Understanding
what has
been mapped?
• day
• night
Understand need for design
Understanding your limitations
Understanding your possibilities
Understanding your data
[source: New York Times]
Method: data analysis and design
determine
Data analysis
and design
geographical
data
link
measurement
scales
select
perceptual
properties
choice of representation
method
( type of map )
visual
variables
MEASUREMENT
LEVEL
interval
implicit
ordinal
distinguishing
Population of Estonia’s provinces in 2012
Numberof
ofinhabitants
inhabitants
Number
a)
nominal
<<10,000
10,000
ºC
-5
proportion
order
10,000
10,000-<80,000
-<80,000
distance
C
5º
-1
a) nominal
hamlet and road
Importance of correct design
Which province has most inhabitants?
ratio
>
differentiation
Data and perception
80,000-<
80,000-<500,000
500,000
PERCEPTUAL
PROPERTIES
<<500,000
500,000
2 dimensions / plane
c) interval
temperature
size
high
value
>
grain / texture
low
Number of inhabitants
colour
color
b) ordinal
high and low
d) ratio
number of students
0
0
> 500,000
50 km
50 km
b)
c)
orientation
shape / form
positive
perception
VISUAL
VARIABLES
< 10,000
Example data
Harjumaa
data
components
map title
Hiiumaa
MEASUREMENT LEVEL
LENGTH
legend
nominal
(qualitative)
ordered
interval
ratio
(quantitative)
50 km
Population of
Estonia’s provinces in 2012
10,000 -<80,000
Data analysis and map symbols
invariant
0
80,000-< 500,000
PERCEPTUAL
PROPERTIES
VISUAL
VARIABLES
Läänemaa
Raplamaa
Jõgevamaa
Pärnumaa
size
value
texture
color
orientation
shape
Järvamaa
BASIC
SYMBOLS
RANGE
differentiation
order
distance
proportion
point
line
area
text
Ida-Virumaa
LääneVirumaa
Saaremaa
Viljandimaa
Tartumaa
MAP
Põlvamaa
Valgamaa
Võrumaa
geo
a)
b)
Design: visual hierarchy
Population of the province of Tartu
in comparison with other Estonian provinces
data
invariant
Harjumaa
Läänemaa
Raplamaa
Harjumaa
LääneVirumaa
Järvamaa
components
Hiiumaa
Läänemaa
Jõgevamaa
MEASUREMENT LEVEL
LENGTH RANGE
nominal
(qualitative)
ordered
AMOUNT
IdaVirumaa
LääneVirumaa
Population by Province
Hiiumaa
Population of the province of Saaremaa
in comparison with other Estonian provinces
interval
ratio
(quantitative)
geo
8482-552972
PERCEPTUAL
PROPERTIES
VISUAL
VARIABLES
Jõgevamaa
BASIC
SYMBOLS
Pärnumaa
Saaremaa
differentiation
order
distance
proportion
size
value
texture
color
orientation
shape
IdaVirumaa
Järvamaa
Raplamaa
Pärnumaa
Viljandimaa
Tartumaa
point
Viljandimaa
Saaremaa
Tartumaa
Põlvamaa
line
area
text
Põlvamaa
Valgamaa
Valgamaa
Võrumaa
550000
550000
number on inhabitants
15
Võrumaa
number on inhabitants
150000
150000
50000
10000
0
50000
10000
50 km
0
a)
Correct design?
50 km
symbol Tartu
map title / legend
other provincial symbols
provincial names
base map
all information
b)
Important map types
• Choropleth
inhabitants per km2
Estonia’s population density in 2012
8 - 12
13 - 24
25 - 73
74 - 128
0
• Qualitative map
a)
b)
• Proportional point symbol map
(diagram map)
c)
d)
50 km
a)
Thematic map types
Convert
Common mistakes
inhabitants per km2
Estonia’s population density in 2012
number of inhabitants
8 - 12
< 10,000
13 - 24
10,000 -<80,000
25 - 73
80,000-< 500,000
74 - 128
> 500,000
Estonia’s population in 2012
Maps at work
0
50 km
b)
0
50 km
c)
Backgrounds
On-line tools
Scale - zoom levels
Compare location
2
4
6
8
10
12
14
16
Compare attribute
Compare time
Compare and understand
Beware!
http://www.zorgatlas.nl
Qualitative maps - caution
Choices to be made: absolute or relative quantities
• Impression of non-area related phenomena may be wrong because
it is influenced by the size of the areas (e.g. dominant language
spoken: related to people, not to area)
• Use a cartogram
Scale & placename patterns
Geographical units
http://bl.ocks.org/pramsey/922f6170dec2a3cd5da6
Interpreting maps
Borders
Order
Errors
‘Errors’
due to (misuse of) mapping method: design error?
unemployment
More than 8%
Less than 8%
unemployment
More than 8%
Less than 8%