Post on 25-Jun-2015
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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
This content is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License