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Demarcating geographic regions using community detection in commuting networks with significant self-loops
Mark He1, Joseph Glasser1, Nathaniel Pritchard2
1Statistics & Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Abstract:
We develop a method to identify statistically significant communities in a weighted network with a high proportion of self-looping weights. We use this method to find overlapping agglomerations of U.S. counties by representing inter-county commuting as a weighted network. We identify three types of communities; non-nodal, nodal and monads, which correspond to different types of regions. The results suggest that traditional regional delineations that rely on ad hoc thresholds do not account for important and pervasive connections that extend far beyond expected metropolitan boundaries or megaregions.
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