  Due to its wide reaching implications for everything from identifying
hotspots of income inequality to political redistricting, there is a rich body
of literature across the sciences quantifying spatial patterns in socioeconomic
data. In particular, the variability of indicators relevant to social and
economic well-being between localized populations is of great interest, as it
pertains to the spatial manifestations of inequality and segregation. However,
heterogeneity in population density, sensitivity of statistical analyses to
spatial aggregation, and the importance of pre-drawn political boundaries for
policy intervention may decrease the efficacy and relevance of existing methods
for analyzing spatial socioeconomic data. Additionally, these measures commonly
lack either a framework for comparing results for qualitative and quantitative
data on the same scale, or a mechanism for generalization to multi-region
correlations. To mitigate these issues associated with traditional spatial
measures, here we view local deviations in socioeconomic variables from a
topological lens rather than a spatial one, and use a novel information
theoretic network approach based on the Generalized Jensen Shannon Divergence
to distinguish distributional quantities across adjacent regions. We apply our
methodology in a series of experiments to study the network of neighboring
census tracts in the continental US, quantifying the decay in two-point
distributional correlations across the network, examining the county-level
socioeconomic disparities induced from the aggregation of tracts, and
constructing an algorithm for the division of a city into homogeneous clusters.
These results provide a new framework for analyzing the variation of attributes
across regional populations, and shed light on new, universal patterns in
socioeconomic attributes.
