Using self organizing maps to analyze demographics and swing state voting in the 2008 u.s. presidential election

Authors:
Paul T. Pearson;Cameron I. Cooper
Affiliations:
Hope College, Holland, MI;Fort Lewis College, Durango, CO
Venue:
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Year:
2012

Citing 4
Cited 0

Clustering Algorithms

Clustering Algorithms
Socio-economic Data Analysis with Scan Statistics and Self-organizing Maps

ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Analysis of Parliamentary Election Results and Socio-Economic Situation Using Self-Organizing Map

WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Self organizing maps as models of social processes: the case of electoral preferences

WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps

Quantified Score

Hi-index	0.00

Visualization

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.