These are the slides to accompany the second lecture from Lesson 4 of Maps and the Geospatial Revolution on Coursera. www.coursera.org/course/maps/

1. Maps and the Geospatial Revolution
Lesson 4 – Lecture 2
Anthony C. Robinson, Ph.D
Lead Faculty for Online Geospatial Education
JohnA. Dutton e-Education Institute
Assistant Director, GeoVISTA Center
Department of Geography
The Pennsylvania State University
This content is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License

2. 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

3. 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

4. Lung Cancer & Rainfall

5. Lung Cancer & Rainfall

6. 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

7. Scale Matters

8. Scale Matters

9. Scale Matters

10. Map Rates, NotTotals
• 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

11. Normalization

12. Normalization

13. 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