Using self organizing maps to analyze demographics and swing state voting in the 2008 US presidential election
Abstract
Emergent self-organizing maps (ESOMs) and k-means clustering are used to cluster counties in each of the states of Florida, Pennsylvania, and Ohio by demographic data from the 2010 United States census. The counties in these clusters are then analyzed for how they voted in the 2008 U.S. Presidential election, and political strategies are discussed that target demographically similar geographical regions based on ESOM results. The ESOM and k-means clusterings are compared and found to be dissimilar by the variation of information distance function.
Type
Publication
Pearson, P. T., & Cooper, C. I. (2012, September). Using self organizing maps to analyze demographics and swing state voting in the 2008 US presidential election. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 201-212). Springer, Berlin, Heidelberg.
Paul Pearson
Associate Professor of Mathematics
My research interests include algebraic topology, applied mathematics, and machine learning.