So many great data analysis exercises that could be run with such a set. Effectively, you could create scalar variables representing lots of different populati

So many great data analysis exercises that could be run with such a set. Effectively, you could create scalar variables representing lots of different population statistics on a country, state, city, ect level. Density: http://i.imgur.com/bfVdrbK.png http://i.imgur.com/bfVdrbK.png Racial Density / Clustering: http://i.imgur.com/pQDJLtX.png http://i.imgur.com/pQDJLtX.png Terrain / Feature Clustering: http://i.imgur.com/AH9f2KW.png http://i.imgur.com/AH9f2KW.png City Homogeneity: http://i.imgur.com/Lo3X6KI.png http://i.imgur.com/Lo3X6KI.png City Racial Mixing: http://i.imgur.com/VBzLPI6.png http://i.imgur.com/VBzLPI6.png or http://i.imgur.com/ldIgYaw.png http://i.imgur.com/ldIgYaw.png City Cluster Homogeneity: http://i.imgur.com/2TLabMj.png http://i.imgur.com/2TLabMj.png vs http://i.imgur.com/LlsAMx4.png http://i.imgur.com/LlsAMx4.png Cluster to nearby dot gravity (how much do dots spread from a nearby loci): http://i.imgur.com/LlsAMx4.png http://i.imgur.com/LlsAMx4.png vs http://i.imgur.com/qNkMCcu.png http://i.imgur.com/qNkMCcu.png And that doesn't even cover stuff that's harder to show like gradients, edge analysis (who's likely driving between two towns?), or migration patterns if multiple census years are available.