Regionalization via network-constrained clustering
I was interested in applications for a clustering algorithm that works along a network, identifying contiguous partitions, and thought that a good place to start would be identifying regional patterns in electoral preferences.
This project represents the early products of this inquiry. I chose county-level data, as counties are small enough to make “interesting” regions, and the presidential vote data was available back to 1920.
The poster linked below was given at the 2010 Political Networks Conference at Duke University, and describes the project in somewhat greater detail.
I am particularly enamored of the Obama/McCain color-coded network graph, as an abstracted version of the red/blue/purple cartograms produced in the wake of recent U.S. national elections. I also like the 12-cluster solution (middle left), as the regions produced are large enough to be considered general, but appear to cluster around recognizable politico-geographic features. In general, I have been very pleased with the results produced by this network-constrained clustering algorithm.