Map Design
Map Layout and Design
• Key components to consider when
designing a map
1.
2.
3.
4.
5.
Legibility
Visual Contrast
Visual Balance
Figure-Ground Relationship
Hierarchical Organization
Map Layout and Design
• Legibility
– Make sure that graphic symbols are easy to
read and understand
– Size, color, pattern must be easily
distinguishable
Map Layout and Design
• Visual Contrast
– Uniformity produces monotony
– Strive for contrast/variation (but don’t overdo
it)
– Variation can be expressed with
• Size
• Intensity
• Shape
• Color
Visual Contrast
Simultaneous Color Contrast
Map Layout and Design
• Visual Balance
– Keep things in balance
– Think about the graphic weight, visual weight
– Graphic weight is affected by
darkness/lightness, intensity and density of
map elements
– Visual center is slightly above the actual
center (standard is 5%)
Visual Balance
Visual center
5% of height
5% of height
Landscape
Portrait
Map Layout and Design
• Figure-Ground Relationship
– Complex, automatic reaction of eye and brain
to a graphic display
– Figure: stands out
Ground: recedes
Map Layout and Design
• Figure-Ground Relationship
– All other things being equal, there are factors that
are likely to cause an object to be perceived as figure
(i.e. stand out from background)
• Articulation & detail
• Objects that are complete (e.g. land areas
contained within a map border)
• Smaller areas (relative to large background areas)
• Darker areas
Map Layout and Design
• Color Conventions
– “Normal” colors that we’ve become accustomed to seeing (these are
somewhat standard worldwide, but can be culturally specific)
– Part of the figure – ground relationship
• Common Examples
– Water = blue
– Forests = green
– Elevation:
• low = dark
• high = light (because mountains can have snow on top)
– Roads in a road atlas:
• Interstate = blue
• Highway = red
• Small road = gray
Where is this?
Map Layout and Design
• Figure-Ground Relationship
– Very difficult to develop a hard and fast rule
with figure ground, relies on a mix of factors
Map Layout and Design
• Hierarchical Organization
– Use of graphical organization schemes to
focus reader’s attention
• Types
– Extensional
– Stereogrammatic
– Subdivisional
Hierarchical Organization
• Extensional
– “Ranks Features on the Map”
• Use of different sized line symbols for roads
Heywood County
#
#
#
æ
,.-
40
Madison
J
Æ
æ
æ
J
Æ
#
Great Smoky
Mountains
National Park
#
æ
æ
Ê
#
æ
#
æ æ#
Æ
J
Q
Æ
Q
Æ
Ã
J
Æ
J
J Æ
Æ
J Æ
Æ
J
#
/(19
20 9
æ
æ
å
å
æ
#
#
#
æ
ææ
Swain
Q
Æ
å#æ
æ
/(2 7 6
#
#
Buncombe
æ
ææ å
æ
æ
æ
æ
æ
åæ
å# # #æ
åå #
å
ææ ## æ #
æ æ
æ
æ
#
,.-
40
#å# #
å
æ
æ
æ
æ
/(
æ
#
æ
ææ
#
å
æ
æ
#
#æ
# å 19
æ
æ
#
æ
å
åæ ææ
å#
#
æ
!"
#
#æ
å
æ
#
æ
Æ
J
JÆ
Æ
J Æ
J
J
Æ
JÆ
Æ
J
JÆ
#
#
#æ
Q
Æ
å#
æ æ
æ
æ
æ
æ
å
#
æ
#
æ
Q
Æ
#
Q
Æ
Jackson
Q
Æ
Q
Æ
Henderson
J
Æ
#
Macon
Roads
Primary road with limited access
Primary road
Secondary and connecting road
Local road
Road, major and minor categories unknown
Ferry crossing
Heywood Landmarks
Campground
Q
Æ
Overlook
J
Æ
Retail
Misc.
Monument
Ã
Ê
#
Fish Hatchery
Golf
Community Ctr
Places
å
æ
Schools
Churches
Hierarchical Organization
• Subdivisional
– Portrays the internal divisions of a hierarchy
– Example: Regions of North Carolina
Western
Mountains
Piedmont
Coastal Plain
Hierarchical Organization
• Stereogrammatic
– Gives the impression that classes of
features lie at different levels on the map
– Those on top are most important
Western
Moun i s
Piedmont
Coastal Plain
Text: Selection and
Placement
POINT
AREA
LINE
Example Map Types
• We will consider five thematic map types
•
•
•
•
•
Choropleth
Proportional symbol
Dot density
Isoline Maps
Cartograms
Choropleth Maps
• Greek: choros (place) + plethos (filled)
Choropleth Maps
Source: http://www.gis.psu.edu/geog121/pop.html
Choropleth Maps
•These use polygonal enumeration units
•E.g. census tract, counties, watersheds, etc.
•Data values are generally classified into ranges
•Polygons can produce misleading impressions
•Area/size of polygon vs. quantity of thematic data value
Thematic Mapping Issue:
Modifiable Area Unit Problem
• Assumption:
– Mapped phenomena are uniformly spatially distributed within
each polygon unit
– This is usually not true!
• Boundaries of enumeration units are frequently
unrelated to the spatial distribution of the phenomena
being mapped
• This issue is always present when dealing with data
collected or aggregated by polygon units
MAUP
Modifiable Areal Unit Problem: (numbers represents the polygon mean)
Scale Effects (a,b)
Zoning Effects (c,d)
The following numbers refer to quantities per unit area
a)
c)
b)
d)
Summary: As you “scale up” or choose different zoning boundaries, results change.
Review: Generalizing Spatial Objects
• Representing an object as a point, a
line, or a polygon?
– Depends on
• Scale (small or large area)
• Data
• Purpose of your research
– Example: House
• Point (small scale mapping)
• Polygon
• 3D object (modeling a city block)
Review: Generalizing Spatial Objects
•Scale effects how an object is generalized
•Left  houses appear to have length & width (polygons)
•Right  houses appear as points
Generalizing Data by Attribute
• So generalization can mean abstracting a realworld geographic feature to a data (GIS) or map
object
• But generalization can also refer to how we
convey attribute information on a map through
the use of symbols, colors, etc.
• This process is generally referred to as
classifying
Classifying Thematic Data
• Data values are classified into ranges for many thematic
maps (especially choropleth)
– This aids the reader’s interpretation of map
• Trade-off:
– Presenting the underlying data accurately
VS.
– Generalizing data using classes
• Goal is to meaningfully classify the data
– Group features with similar values
– Assign them the same symbol/color
• But how to meaningfully classify the data?
Creating Classes
• How many classes should we use?
– Too few - obscures patterns
– Too many - confuses map reader
• Difficult to recognize more than 4-5 classes
Creating Classes
• Methods to create classes
– Assign classes manually
– Equal intervals
• This ignores the data distribution
– Natural breaks
– Quantiles quartiles
• E.g., quartiles - top 25%, 25% above middle, 25% below middle,
bottom 25% (quintiles uses 20%)
– standard deviation
• Mean +/- 1 standard deviation, mean +/- 2 standard deviations …
The Effect of Classification
• Equal Interval
– Splits data into user-specified number of classes
of equal width
– Each class has a different number of
observations
The Effect of Classification
• Quantiles
– Data divided so that there are an equal number
of observations are in each class
– Some classes can have quite narrow intervals
The Effect of Classification
• Natural Breaks
– Splits data into classes based on natural breaks
represented in the data histogram
The Effect of Classification
• Standard Deviation
– Mean + or – Std. Deviation(s)
Natural Breaks
Quantiles
Equal Interval
Standard Deviation
Thematic Mapping Issue:
Counts Vs. Ratios
• When mapping count data, a problem frequently occurs
where smaller enumeration units have lower counts than
larger enumeration units simply because of their size.
This masks the actual spatial distribution of the
phenomena.
• Solution: map densities by area
– E.g., population density, per capita income, automobile
accidents per road mile, etc.
Thematic Mapping Issue:
Counts Vs. Ratios
• Raw count (absolute)
values may present a
misleading picture
• Solution:
• Normalize the data
• E.g., ratio values
Proportional Symbol Maps
• Size of symbol is proportional to size of
data value
– Also called graduated symbol maps
• Frequently used for mapping points’
attributes
– Easily avoids distortions due to area size as seen in
choropleth maps by using both
size and color
Proportional Symbol Maps
Dot Density Maps
• Dot density maps provide an immediate picture of density
over area
• 1 dot = some quantity of data value
– E.g. 1 dot = 500 persons
– The quantity is generally associated with polygon enumeration
unit
– MAUP still exists
• Placement of dots within polygon enumeration units can
be an issue, especially with sparse data
Dot Density Maps
• Population by county
#
#
# #
#
# #
#
#
#
#
# #
#
#
# # #
#
# #
#
#
#
#
#
#
# ##
#
# ## #
#
##
# #
# ## # # #
#
#
#
#
#
#
#
## # #
#
#
# ## #
#
#
## #
#
# #
#
#
#
#
#
#
# #
#
#
#
#
#
#
#
#
#
#
#
#
#
# #
#
# #
##
#
##
# #
#
#
#
#
#
#
#
#
#
#
# #
#
# #
###
##
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
##
#
#
#
#
#
#
##
# # # # #
#
#
#
# ##
# # ## # ### # # #
## #
#
#
# #
#
##
# #
#
# # #
#
# # ##
#
# # ## #
##
##
# # ##
###
#
#
#
#
#
## #
#
#
# #
#
#
#
## #
#
#
# #### # ### # # # # ### #
#
##
#
# #
#
# ### # #
# ##
## # ####
##
#
# ##### # # # ### # # # # #
# # #
#
#
#
#
# # ### ##
## ##
#
# ##
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
# #
# #
#
### #
## #
#
# ### # # #
#
## # # # # #
# #
#
# #
# # ##
####
#
#
# ##
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
# ##
# ### # #
## ## ### # # # # # #
## # # #
##
# ##
#
#
### ## #
# ##
# # ##
#
#
# ##
##
## # # ## #
# #### #
#
#
#
# ##
###
# # ## # # ## ####
##### #
## ## # #
# #
#
# # ##
#
##
# # ## # ## # #
#
#
### #
# # # # # ## #
## # ##
##
##
##
#
# ### ###
# # ## #
#
# ## # #
#
# ##
## ## ### ## ##
#
##
#
#
#
#
##
#
#
#
#
#
#
# # # # # ### #
## ## ## # ### # #
### #
####
# ### ###### ##
#
##
#
#
#
## # # ##
##
##
# # # # #
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
##
#
# # # # # ## #
##
#
# # #
##
## #
## # ## # # # #
##
#
# #
##
#
#
# #
# # #
#
##### # #
#
#
#### #
## ### # ### ## #
#
#
# #
##
# ## #
# ## #
##
#
## # #
##
# #
### # #
##
# # #
### #
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
##
# #
## #
#
#
#
## # #
# # #
# #
#
#
##
## # ## # ### ## # # # #
#
#
# # #
# #
##
#
# #
#
##
#
# ### ###
#
# # ##
# ## #
##
# #
##
#
###
# # #
#
#
# ##
# #
#
##
#
## # # # # # #
##
# ## ##
## #
##
# ## # ##
#
#
#
#
#
#
#
#
#
#
#
##
#
#
# # ### #
#
# # # # # ## # # ## ###
#
#
#
#
##
#
#
#
#
#
#
##
#
### ##### #
#
##
#
#
# # ## ## # ### #
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
##
# #
# # # # # ### #
#
#
# # #
# # # ### ## ##
#
# #
##
#
# # #
# ## #
#
# # # ## #
# #
# #
# ##
#
## # # # #
##
#
# # ## # ## #
# # # #
# ## # # ## ## # #
##
#
# #
# # #
## #
## #
#
# # #
## #
##
#
## # #
# # ### #
### # # ##
###
#
# # #
# ####
#
# #
#
# #
#
#
#
#
##
#
# ##
#
###
#
## #
#
#
## ##
#
## # #
# ## ##
## # #
#
# # ## # # # #
##
# #
#
### # # ##### # ## #
#
# # ## # #
#
# ## #
# # #
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
##
#
# # ##
# # ##
## # #
#
#
## # # #
#
#
#
#
# ##
#
#
#
#
#
#
#
#
#
#
#
#
#
##
## # #
##### # # #
#
# # # ##
# #
##
# #####
# # ###
#
## #
# # ##
#
# # #
#
#
#
# #
#
## #
## # ##
### ##
##
### ## ## # # ## #
#
# #
#
##
# #
# #
#
# # #
## # # #
##
##
# # # ###
##
#
# #
#
# # # ##
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
# # # ## # #
#### # # ##### # ### # #
#
#
#
#
## #
#
#
#
##
##
## #
# # #
#
#
##
# #
#
# #
# # #
#
# #
# #
##
## # # # # # #
## # ##
######
# ###### #
#
# # # ##
#
#
#
#
# #
#
## #
# # ##
# #
#
#
###
###
## #
#
#
## #
# # # # # ## #
#
# #
# #
## ##
# ##### #
# ##
## # #
#
#
#
# ## #
# # ####
# #
#
#
#
#
#
#
## ##
# ## ##
###
##
##
#### ##
# ###
# # # # ##
#
#
#
# ##
#
## ###
##
#
#
# #
# # #
# ##
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
## #
### ## # # #
# #
#
# ## ##
#
#
# # # ##
##
##### #
#
# ## #
# ##
## ##
#
#
#
#
#
#
## #
### #
## #
# #
#
#
# ###
#
#
#
# ###
##
###
## ##
#
## #
# #
# # #
#
#
#
#
#
# # ##
#
# ## #
# #
# ###
# #
### ## #
###
##
# #
# #
## #
# #
#
#
#
#
# # ##
# ##
###
## # ### ##
## #
# #
### ##
#
#
# ## ##
# # # ##
#
#
#
#
#
#
## #
# ##
## ###
#
# ##
### #
#
#
#
#
#
#
#
#
#
#
# # ## ###
#
##
#
#
#
#
#
# # ####
# #
##
#
#
#### # # # # #
#
## #
#
#
##
# # #
#
#
# # # ##
# ## # # ## # #
# #
###
#
##
#
#
##
## #
# # ##
#
#
#
# ##
#
#
#
#
##
#
# # # ## #
#
#
#
#
#
### ## #
#
#
#
#
#
#
#
#
##
#
#
## # #
## #
#
#
#
#
#
## #
#
#
#
#
#
#
# #
# ## # # # #
# #
#
#
# #
#
# ## ## #
# ###
## #
# #
#
#
#
# #
#
#
#
##
#
## #
## ###
# # #
# #
#
#
#
#
#
#
## #
# ## #
#
#
#
#
#
#
#
#
# # # ##
# # #
#
#
#
#
#
## #
#
#
#
# # ##
#
#
# #
# ## #####
#
#
## #
##
#
## ##
## #
#
# #
##
#
##
#
###
#
#
#
#
#
#
#
## #
#
# ##
#
#
#
#
# #
#
#
#
#
#
# #
#
#
#
#
#
#
#
#
#
#
#
# #
#
#
###
#
#
#
#
#
##
#
#
##
#
# #
##
##
#
#
#
#
#
#
# #
##
#
Dot Density Maps
Map credits/source: Division of HIV/AIDS Prevention, National Center for
HIV, STD, and TB Prevention (NCHSTP), Centers for Disease Control.
Dot Density Maps
Isoline Maps
• Lines on the map that are used to visualize a surface
• Isolines are best for continuous data (raster), but
frequently applied to discrete data (vector) too
• Drawing the lines (or data) in-between the data points
utilizes the processes of interpolation
– Interpolation: “The action of introducing or inserting
among other things or between the members of any series”
Making Isolines
Making Isolines
Can you draw
Isolines with
an interval of 5
units?
Making Isolines
I’ll start with
the15 unit
isoline
Begin by
putting dots
where the line
should pass
Making Isolines
Now just
connect the
dots
Who can draw
the 20 unit
interval?
Making Isolines
Who can draw
the 25 unit
interval?
Making Isolines
Who can draw
the 30 unit
interval?
Making Isolines
Final Map
Isoline Maps
• Types
– Isometric lines – based on control points that have
observed values
– Isopleths – based on point values that are areal
averages (e.g., the population density calculated
for each county, with the county center point used
as the locational information)
Isometric Lines
Isopleths
Cartograms
•Instead of normalizing data within polygons:
•We can change the polygons themselves!
•Maps that do this are known as cartograms
•Cartograms distort the size and shape of polygons to
portray sizes proportional to some quantity other than
physical area
Conventional Map of 2004
Election Results by State
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan
http://www-personal.umich.edu/~mejn/election/
Population Cartogram of 2004
Election Results by State
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan
http://www-personal.umich.edu/~mejn/election/
Electoral College Cartogram
of 2004 Election Results by State
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan
http://www-personal.umich.edu/~mejn/election/
Conventional Map of 2004
Election Results by County
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan
http://www-personal.umich.edu/~mejn/election/
Population Cartogram of 2004
Election Results by County
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan
http://www-personal.umich.edu/~mejn/election/
Graduated Color Map of 2004
Election Results by County
Robert J. Vanderbei – Princeton University
http://www.princeton.edu/~rvdb/JAVA/election2004/
Graduated Color Population Cartogram
of 2004 Election Results by County
Robert J. Vanderbei – Princeton University
http://www.princeton.edu/~rvdb/JAVA/election2004/
• Lab #2 due TODAY
• See you Friday for case study #6