Maps and the Geospatial Revolution

Lesson 4 – Lecture 2

Anthony C. Robinson, Ph.D Lead Faculty for Online Geospatial Education John A. Dutton e-Education Institute Assistant Director, GeoVISTA Center Department of Geography The Pennsylvania State University

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Analysis Pitfalls

• Good spatial analysis can tell you a lot

• Bad spatial analysis can be very misleading (and hard to spot by the untrained eye)

• Maps tend to come across as objective and factual to most normal people

Correlation (is not Causation)

• Just because two things co-occur, it doesn’t mean that they are causally related

• Examples

– Centre County: 18.9% living in poverty, therefore Universities are terrible for the economy

– Lung cancer mortality and rainfall

Lung Cancer & Rainfall

Lung Cancer & Rainfall

Scale Matters

• Depending on the scale at which you analyze things, you may be able to derive very different results

• This is called the Modifiable Areal Unit Problem (MAUP) – Also the toughest Pictionary prompt ever and the

sound a cat makes before launching a hairball

Scale Matters

Scale Matters

Scale Matters

Map Rates, Not Totals

• Almost anything you can imagine measuring about people and society will be population dependent

• This means a map that isn’t normalized will just highlight populated places

• Normalization calculates rates of occurence as a proportion of overall population

– If you have data for every US county with the # of smelly dogs

you’d need to divide those # by the total population of dogs in each county

– Then you’d have a stink-rate for each place

Normalization

Normalization

Maps and the Geospatial Revolution www.coursera.org/course/maps Twitter @MapRevolution Online Geospatial Education @ Penn State www.pennstategis.com

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