Discovering spatio-social motifs of electoral support using discriminative pattern mining

Association analysis provides a natural, data-centric framework for the discovery of patterns of explanatory variables that are linked to a certain outcome. In this paper we demonstrate how such a framework can be applied for political analysis, using an expository example of discovering different spatio-social motifs of support for Barack Obama in the 2008 presidential election. Election results and thirteen different socio-economic explanatory variables, tabulated at the county level, are used as an input for calculating a collection of discriminative patterns having disproportionately large support within the counties won by Obama. These patterns are synthesized into a small number of larger socio-economics motifs using a novel pattern similarity measure that outputs a concise summary readily interpretable in terms of political analysis. The method discovers two major Obama constituencies that differ in their socio-economic makeup and in their geographical distributions. The larger constituency can be further divided into more narrowly-defined motifs.