How not to lie with maps: Design choices for quantitative data Catherine Riihimaki, Drew University.

How not to lie with maps: Design choices for quantitative data

Catherine Riihimaki, Drew University

GIS at Drew

• No geoscience program (except me)• One GIS course (so far) offered once per year• GIS is one of 4 required courses for

Environmental Studies• GIS now satisfies a few Gen. Ed. categories:

Natural Sciences, Interdisciplinary, Quantitative

Map Design Challenges

• Nuts-and-bolts of making a map

• Good vs. bad design choices– Aesthetic choices– Misleading choices

Assignment

• Create maps showing population, racial composition, and age composition of New Jersey counties (data provided)

• Describe one conclusion each about how population, race/ethnicity, and age are distributed across NJ

• Describe and justify your design choices– Color/symbol choices– Normalization choices– One alternative map design that you rejected

Key conclusions:1.All maps provide selective truth

2.Cut-points can be manipulated to change meaning of choropleth maps

3.Count data should be shown with varying symbol sizes or should be normalized to show density (e.g., population density instead of population)

Ways students can go wrong…Inappropriate use of pie charts Confusion about normalization

Ways students can go wrong…Confusion about normalizationInappropriate use of pie charts

Outcomes

• Reinforcing concept that map design must be done carefully and deliberately– Synthesis of article through designing maps

• Practicing quantitative concepts– Normalization– Chart usage– Unequal group sizes (and other data limitations)

• Learning about New Jersey characteristics– Must have some map interpretation to make

them look at the maps carefully

How not to lie with maps: Design choices for quantitative data Catherine Riihimaki, Drew University.

Documents

Transcript of How not to lie with maps: Design choices for quantitative data Catherine Riihimaki, Drew University.