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MAPS & JUDGMENT
Steps toward
cartographic literacy
Projections
Every map employs some kind of projection to transform spherical surface to flat surface
Choice of projection reflects priorities of cartographer in regard to preserving one of the following (or compromising between all) Area Distance Direction Shape
Read the Introductory section in your atlas (this is required reading!)
Mercator projection
Why did my plane from Paris go so far out of the way to get to Chicago?
Mollweide Projection
Maybe it didn’t go too far out of the way…
Azimuthal projection (north pole)
That explains it!
Remember that every projection is a distortion.
How is this projection distorted?
Judgment is inevitable when making maps
All maps involve decision-making process There is no “natural” way to draw a map Maps are not reflections of reality but
selections of reality Maps can be analyzed and critiqued just like
literature to determine what the cartographer believed and thought about the world, and his/her values, beliefs, objectives, etc. (J.B. Harley, John Pickles, etc.)
Who might have made this map and why?
Medieval “T&O” map
What common words in the English language reflect this tradition of mapping?
• Orientation
• Oriented
Thematic Maps
Thematic maps represent one or two variables (population, ethnicity, income, language, religion, etc.) in map form
The “language” of thematic maps is quite varied, and involves the use of color, shape, pattern, light and dark, etc.
Thematic maps serve two main purposes: Analysis of data Presentation of data
Map with pie chart callouts
What are the strengths and weaknesses of this cartographic language?
Choropleth map
What are the strengths and weaknesses of a choropleth map?
Cartogram and choropleth
A cartogram is a boundary map in which the areas are distorted systematically: every partition’s area shows its value for a particular variable
Isoline map of population
Why use an isoline map for population rather than a choropleth map?
Choropleth maps
From A to Z
Choropleth maps
Tremendously common and useful Use some existing system of boundaries
(countries, states, counties, voting districts, etc.)
Group data into 2 or more levels or classes using slicing values
Show spatial variation of one or two variables at a time by using color, shades of grey and/or patterns
Percent of Population White (not Hispanic)
Percent of Population White (not Hispanic)
How can such different looking maps show the same variable?
Cartographic reasons Different slicing values Different levels of spatial aggregation
Geographical reasons Uneven distribution of minorities at the state scale
as well as at the national scale Concentration of minorities in cities, particularly in
northern states
Different ways of “slicing” data The Data {42, 50, 55, 57, 61, 77, 79, 97}
Equal interval Three classes based on range 40 to 100 {42, 50, 55, 57} {61, 77, 79} {97}
Quantile Quartiles (lowest 1/4 of observations, next 1/4, …) {42, 50} {55, 57} {61, 77} {79, 97}
Different ways of “slicing” data
Natural breaks {428505552574616772791897} {42} {50, 55, 57, 61} {77, 79} {97}
Standard deviations Mean = 64.75, Std. Dev. = 15.977 {42} {50, 55, 57, 61} {77, 79} {97}
-2 32.79517-1 48.77259
mean 64.751 80.727412 96.70483
Natural Breaks
Equal Interval
Quintiles (quantiles based on division into 5 classes)
Which map would be preferred by each of the following users?
• The ACLU
• The KKK
• A geographer studying the relationship between ethnicity and poverty
• A spokesman for the Georgia branch of a charitable assistance association targeting minorities
Alabama 70.3
Alaska 67.6
Arizona 63.8
Arkansas 78.6
California 46.7
Colorado 74.5
Connecticut 77.5
Delaware 72.5
District of Columbia 27.8
Florida 65.4
Georgia 62.6
Hawaii 22.9
Idaho 88
Illinois 67.8
Indiana 85.8
Iowa 92.6
Kansas 83.1
Kentucky 89.3
Louisiana 62.5
Maine 96.5
Maryland 62.1
Massachusetts 81.9
Michigan 78.6
Minnesota 88.2
Mississippi 60.7
Missouri 83.8
Montana 89.5
Nebraska 87.3
Nevada 65.2
New Hampshire 95.1
New Jersey 66
Percent of total population; White
alone, not Hispanic or Latino
GeographyNew Hampshire 95.1
New Jersey 66
New Mexico 44.7
New York 62
North Carolina 70.2
North Dakota 91.7
Ohio 84
Oklahoma 74.1
Oregon 83.5
Pennsylvania 84.1
Rhode Island 81.9
South Carolina 66.1
South Dakota 88
Tennessee 79.2
Texas 52.4
Utah 85.3
Vermont 96.2
Virginia 70.2
Washington 78.9
West Virginia 94.6
Wisconsin 87.3
Wyoming 88.9Puerto Rico 0.9
• Not very useful this way
• Generally it helps to re-order the data
Puerto Rico 0.9
Hawaii 22.9
District of Columbia 27.8
New Mexico 44.7
California 46.7
Texas 52.4
Mississippi 60.7
New York 62
Maryland 62.1
Louisiana 62.5
Georgia 62.6
Arizona 63.8
Nevada 65.2
Florida 65.4
New Jersey 66
South Carolina 66.1
Alaska 67.6
Illinois 67.8
North Carolina 70.2
Virginia 70.2
Alabama 70.3
Delaware 72.5
Oklahoma 74.1
Colorado 74.5
Connecticut 77.5
Arkansas 78.6
Michigan 78.6
Washington 78.9
Tennessee 79.2
Massachusetts 81.9
Rhode Island 81.9
Percent of total population; White
alone, not Hispanic or Latino
Geography
Massachusetts 81.9
Rhode Island 81.9
Kansas 83.1
Oregon 83.5
Missouri 83.8
Ohio 84
Pennsylvania 84.1
Utah 85.3
Indiana 85.8
Nebraska 87.3
Wisconsin 87.3
Idaho 88
South Dakota 88
Minnesota 88.2
Wyoming 88.9
Kentucky 89.3
Montana 89.5
North Dakota 91.7
Iowa 92.6
West Virginia 94.6
New Hampshire 95.1
Vermont 96.2Maine 96.5
1 Puerto Rico 0.9
2 Hawaii 22.9
3 District of Columbia 27.8
4 New Mexico 44.7
5 California 46.7
6 Texas 52.4
7 Mississippi 60.7
8 New York 62
9 Maryland 62.1
10 Louisiana 62.5
11 Georgia 62.6
12 Arizona 63.8
13 Nevada 65.2
14 Florida 65.4
15 New Jersey 66
16 South Carolina 66.1
17 Alaska 67.6
18 Illinois 67.8
19 North Carolina 70.2
20 Virginia 70.2
21 Alabama 70.3
22 Delaware 72.5
23 Oklahoma 74.1
24 Colorado 74.5
25 Connecticut 77.5
26 Arkansas 78.6
27 Michigan 78.6
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
5152
Percent of total population; White
alone, not Hispanic or Latino
Geography
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 Arkansas 78.6
27 Michigan 78.6
28 Washington 78.9
29 Tennessee 79.2
30 Massachusetts 81.9
31 Rhode Island 81.9
32 Kansas 83.1
33 Oregon 83.5
34 Missouri 83.8
35 Ohio 84
36 Pennsylvania 84.1
37 Utah 85.3
38 Indiana 85.8
39 Nebraska 87.3
40 Wisconsin 87.3
41 Idaho 88
42 South Dakota 88
43 Minnesota 88.2
44 Wyoming 88.9
45 Kentucky 89.3
46 Montana 89.5
47 North Dakota 91.7
48 Iowa 92.6
49 West Virginia 94.6
50 New Hampshire 95.1
51 Vermont 96.252 Maine 96.5
quartiles: what is 52 ÷ 4 ?
What if you wanted to use quintiles?
difference
Puerto Rico 0.9
Hawaii 22.9 22
District of Columbia 27.8 4.9
New Mexico 44.7 16.9
California 46.7 2
Texas 52.4 5.7
Mississippi 60.7 8.3
New York 62 1.3
Maryland 62.1 0.1
Louisiana 62.5 0.4
Georgia 62.6 0.1
Arizona 63.8 1.2
Nevada 65.2 1.4
Florida 65.4 0.2
New Jersey 66 0.6
South Carolina 66.1 0.1
Alaska 67.6 1.5
Illinois 67.8 0.2
North Carolina 70.2 2.4
Virginia 70.2 0
Alabama 70.3 0.1
Delaware 72.5 2.2
Oklahoma 74.1 1.6
Colorado 74.5 0.4
Percent of total population; White
alone, not Hispanic or Latino
Geography
difference
Oklahoma 74.1 1.6
Colorado 74.5 0.4
Connecticut 77.5 3
Arkansas 78.6 1.1
Michigan 78.6 0
Washington 78.9 0.3
Tennessee 79.2 0.3
Massachusetts 81.9 2.7
Rhode Island 81.9 0
Kansas 83.1 1.2
Oregon 83.5 0.4
Missouri 83.8 0.3
Ohio 84 0.2
Pennsylvania 84.1 0.1
Utah 85.3 1.2
Indiana 85.8 0.5
Nebraska 87.3 1.5
Wisconsin 87.3 0
Idaho 88 0.7
South Dakota 88 0
Minnesota 88.2 0.2
Wyoming 88.9 0.7
Kentucky 89.3 0.4
Montana 89.5 0.2
North Dakota 91.7 2.2
Iowa 92.6 0.9
West Virginia 94.6 2
New Hampshire 95.1 0.5
Vermont 96.2 1.1Maine 96.5 0.3
How informative would “natural breaks” be in this case?
Problems with choropleth maps
Make it easy to slant data to suit the cartographer’s purpose (by adjusting the slicing values)
Create the illusion of rapid breaks whereas data varies continuously and gradually in the real world
Allow small areas (like major cities) to overwhelm the data of large regions (like states)
Mapping inequality in Africa
Mapping economic inequality Variable used: GINI index
No. Country Gini(%)
1 Algeria 35.33
2 Botswana 66.7
3 Burkina Faso 46.85
4 Burundi 42.39
5 Cameroon 46.82
6 Central african republic 61.33
7 Côte d'Ivoire 36.68
8 Egypt 30.33
9 Ethiopia 39.96
10 Gambia 50.23
11 Ghana 39.55
12 Kenya 44.93
13 Lesotho 63.13
14 Madagascar 38.11
15 Malawi 50.31
16 Mali 50.5
17 Mauritania 37.71
18 Morocco 39.46
19 Mozambique 39.61
20 Namibia 74.33
21 Niger 50.61
22 Nigeria 50.56
23 Rwanda 28.9
24 Senegal 41.28
25 Sierra leone 62.87
26 South africa 56.59
27 Swaziland 60.65
28 Tanzania 59.01
29 Tunisia 41.66
30 Uganda 37.4
31 Zambia 53.8
32 Zimbabwe 50.12
GINI index measures economic inequality in a society
100% = completely unequal
0% = completely equal
Gini(%) Gini(%)28.9 28.9
30.33 30.3335.33 35.3336.68 36.6837.4 37.4
37.71 37.7138.11
38.11 39.4639.4639.55 39.5539.61 39.6139.96 39.9641.28 41.2841.66 41.6642.39 42.3944.93 44.9346.82 46.8246.85
46.8550.12 50.1250.23 50.2350.31 50.3150.5 50.5
50.56 50.5650.61 50.6153.8 53.8
56.5956.59
59.01 59.0160.65 60.6561.33 61.3362.87 62.8763.13 63.1366.7 66.7
74.3374.33
Equal Interval
28-37.99
38-47.99
48-57.99
58-67.99
68-77.99
Quantiles (Quartiles)
32 entries ÷ 4 = 8
4 groups of 8
Can you identify the classification schemes?
Gini(%) Gini(%)28.9 28.9
30.33 30.3335.33 35.3336.68 36.6837.4 37.4
37.71 37.7138.11
38.11 39.4639.4639.55 39.5539.61 39.6139.96 39.9641.28 41.2841.66 41.6642.39 42.3944.93 44.9346.82 46.8246.85
46.8550.12 50.1250.23 50.2350.31 50.3150.5 50.5
50.56 50.5650.61 50.6153.8 53.8
56.5956.59
59.01 59.0160.65 60.6561.33 61.3362.87 62.8763.13 63.1366.7 66.7
74.3374.33
Can you identify an observation that “jumps” two classifications?
Gini(%) Gini(%)28.9 28.9
30.33 30.3335.33 35.3336.68 36.6837.4 37.4
37.71 37.7138.11
38.11 39.4639.4639.55 39.5539.61 39.6139.96 39.9641.28 41.2841.66 41.6642.39 42.3944.93 44.9346.82 46.8246.85
46.8550.12 50.1250.23 50.2350.31 50.3150.5 50.5
50.56 50.5650.61 50.6153.8 53.8
56.5956.59
59.01 59.0160.65 60.6561.33 61.3362.87 62.8763.13 63.1366.7 66.7
74.3374.33
South AfricaSouth Africa
Can you think of a scenario with:
• Two different map users (think in terms of organizations and professionals)
• Opposite judgments of what is the “best” map for their purposes?
What would you guess…
Source: Wikimedia commons
Process Maps
How can you map a process?
Map with pie chart callouts
To see the process (growing American dominance in the movie theater) you have to visually compare each pair of pies
Expansion of the EU
To see the process (expansion of the EU) you have to know what color comes first, second, third, etc.
Religious diffusion
To see the process (religious diffusion) you must follow the arrows
Mei-Po Kwan
To see the process (human movement) you must follow the time-space path from the bottom of the lower blue line to the top of the upper blue line
Mei-Po Kwan
Paul Adams
Charles Joseph Minard’s map of the disastrous Napoleonic campaign in Russia (1812)
Questions Why use an isoline map instead of a choropleth
map? Why use a choropleth map instead of an isoline
map? What kind of map is unbiased? What kind of map reflects the interests, intent, and
biases of the mapmaker? What are some problems with choropleth maps? What are some processes that people have
managed to map? What are some cartographic ways of showing
change through time?