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

Author

Listed:
  • Mark He
  • Joseph Glasser
  • Nathaniel Pritchard
  • Shankar Bhamidi
  • Nikhil Kaza

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.

Suggested Citation

  • Mark He & Joseph Glasser & Nathaniel Pritchard & Shankar Bhamidi & Nikhil Kaza, 2020. "Demarcating geographic regions using community detection in commuting networks with significant self-loops," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-31, April.
    • Handle: RePEc:plo:pone00:0230941
    DOI: 10.1371/journal.pone.0230941
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    References listed on IDEAS

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    Cited by:

    1. J. J. Merelo & M. Cristina Molinari, 2024. "Intra-family links in the analysis of marital networks," Journal of Computational Social Science, Springer, vol. 7(1), pages 425-449, April.

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