Information theoretic network approach to socioeconomic correlations
Visiting Speaker
Past Talk
Alec Kirkley
PhD candidate, University of Michigan
Dec 8, 2020
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Due to its wide reaching implications for everything from identifying hot spots 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 predrawn 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 multiregion 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 an 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 framework for analyzing the variation of attributes across regional populations and shed light on universal patterns in socioeconomic attributes.

About the speaker
Alec Kirkley is a PhD candidate in the University of Michigan Physics Department, working with Mark Newman on complex networks and statistical physics. He did his undergraduate studies at the University of Rochester and began his studies in complex systems working with Gourab Ghoshal. He is interested in the theory of complex networks and statistical physics, as well as their applications in urban and social systems. Topics of research include statistical inference on network data, human mobility and the structure and function of urban systems, social network dynamics, and the spatial manifestation of socioeconomic inequality.