Demarcating geographic regions using community detection in commuting networks with significant self-loops

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...

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Published inPloS one Vol. 15; no. 4; p. e0230941
Main Authors He, Mark, Glasser, Joseph, Pritchard, Nathaniel, Bhamidi, Shankar, Kaza, Nikhil
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 29.04.2020
Public Library of Science (PLoS)
Subjects
Commuting
Computer and Information Sciences
Earth Sciences
Labor market
Methods
Metropolitan areas
Operations research
People and places
Physical Sciences
Planning
Research and Analysis Methods
Social networks
Social Sciences
Statistical analysis
Texas
California
United States > US
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Summary: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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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0230941