Visualizing Geographic Data With Python
summary
Explains why projections matter (Greenland vs Africa, Mercator incorrect and unfair but useful), shows concrete code paths: build a Robinson world map and a Lambert conformal regional map, plot unemployment maps for 2006/2009/2014 and a San Francisco dot‑density crime map, demonstrate data classification methods (equal intervals, quantiles, Jenks) and color selection with ColorBrewer, and walks through making data‑rich maps with matplotlib & basemap toolkit and interactive web maps using Folium.
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chapters (10)
- 0:00Introduction
- 0:49Reasons for this talk
- 1:52Overview
- 3:07The power of maps
- 5:35The problem with maps
- 10:05How are maps made
- 14:55Good map design
- 22:07Examples
- 23:45Extras
- 28:10Questions
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