1. Map Design

2. Map Layout and Design Key components to consider when designing a map
Legibility
Visual Contrast
Visual Balance
Figure-Ground Relationship
Hierarchical Organization

3. Map Layout and Design Legibility
Make sure that graphic symbols are easy to read and understand
Size, color, pattern must be easily distinguishable

4. 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

5. Visual Contrast

6. Simultaneous Color Contrast

7. 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%)

8. Visual Balance

9. Visual center 5% of height 5% of height Portrait Landscape
The idea is to place your map features so that the center of the mapped area is ~5% of the height above the actual center. This is the part of the map that the eye is drawn to first.
Landscape
Portrait

10. Map Layout and Design Figure-Ground Relationship
Complex, automatic reaction of eye and brain to a graphic display
Figure: stands out Ground: recedes
Figure is the object(s) that you are interested in.
Ground is the rest of the mapped area that is less important to the map.

11. 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

12. Map Layout and Design Color Conventions Common Examples
“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

13. Where is this?

14. 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

15. Map Layout and Design Hierarchical Organization Types
Use of graphical organization schemes to focus reader’s attention
Types
Extensional
Stereogrammatic
Subdivisional
There are three types of Hierarchical Organization.

16. Hierarchical Organization
Extensional
“Ranks Features on the Map”
Use of different sized line symbols for roads

18. Hierarchical Organization
Subdivisional
Portrays the internal divisions of a hierarchy
Example: Regions of North Carolina

19. Hierarchical Organization
Stereogrammatic
Gives the impression that classes of features lie at different levels on the map
Those on top are most important

20. W e s t r n M o u i P d m C a l

21. Text: Selection and Placement
POINT
AREA
LINE

22. Example Map Types We will consider five thematic map types Choropleth
Proportional symbol
Dot density
Isoline Maps
Cartograms
Need to talk about lying with maps

23. Choropleth Maps
Greek: choros (place) + plethos (filled)

24. Choropleth Maps This potential exists for nearly all maps.
Source:

25. 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

26. 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

27. 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)  b)
                                                            
                   
Does the top example remind you of anything? In many ways it is the opposite of ecological fallacy. c) d)
Summary: As you “scale up” or choose different zoning boundaries, results change.

28. 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)

29. Review: Generalizing Spatial Objects
Scale effects how an object is generalized
Left  houses appear to have length & width (polygons)
Right  houses appear as points

30. Generalizing Data by Attribute
So generalization can mean abstracting a real-world 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

31. 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?

32. 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

33. 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 …

34. 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

35. 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

36. The Effect of Classification
Natural Breaks
Splits data into classes based on natural breaks represented in the data histogram

37. The Effect of Classification
Standard Deviation
Mean + or – Std. Deviation(s)

38. Natural Breaks
Quantiles
Equal Interval
Standard Deviation

39. 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.

40. Thematic Mapping Issue: Counts Vs. Ratios
Raw count (absolute) values may present a misleading picture
Solution:
Normalize the data
E.g., ratio values

41. 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

42. Proportional Symbol Maps

44. 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

45. Dot Density Maps
Population by county

46. Dot Density Maps
Map credits/source: Division of HIV/AIDS Prevention, National Center for HIV, STD, and TB Prevention (NCHSTP), Centers for Disease Control.

47. Dot Density Maps

48. 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”

49. Making Isolines

50. Making Isolines
Can you draw Isolines with an interval of 5 units?

51. Making Isolines I’ll start with the15 unit isoline
Begin by putting dots where the line should pass

52. Making Isolines Now just connect the dots
Who can draw the 20 unit interval?

53. Making Isolines
Who can draw the 25 unit interval?

54. Making Isolines
Who can draw the 30 unit interval?

55. Making Isolines
Final Map

56. 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)

57. Isometric Lines

58. Isopleths

59. 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

60. Conventional Map of 2004 Election Results by State
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan

61. Population Cartogram of 2004 Election Results by State
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan

62. Electoral College Cartogram of 2004 Election Results by State
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan

63. Conventional Map of 2004 Election Results by County
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan

64. Population Cartogram of 2004 Election Results by County
Michael Gastner, Cosma Shalizi, and Mark Newman- University of Michigan

65. Graduated Color Map of 2004 Election Results by County
Robert J. Vanderbei – Princeton University

66. Robert J. Vanderbei – Princeton University
Graduated Color Population Cartogram of 2004 Election Results by County
Robert J. Vanderbei – Princeton University

67. Lab #2 due TODAY
See you Friday for case study #6