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.
Highlights
Geographic regions map to social, cultural, and economic structures that enable us to make sense of the world
We present a method for inferring geographic regions systematically from the underlying data using community detection methods in network science
We examine the results imputed by modularity maximization (Louvain) and the degree-corrected stochastic blockmodel (DC-SBM)
Summary
Geographic regions map to social, cultural, and economic structures that enable us to make sense of the world. Demarcation of these regions allows institutional responses to shared problems by creating territorial administrations. These regions are useful at different scales and are created for varying purposes (e.g. cities, places, watersheds, economic regions) [1, 2, 3]. In the United States, metropolitan regions are conceived as collections of counties or equivalent areas (sub-state political units) and are used for different statistical, governance and planning purposes. Many urbanized areas are often sub-county regions [5]
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