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?