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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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.
AbstractList 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.
Audience Academic
Author He, Mark
Kaza, Nikhil
Glasser, Joseph
Pritchard, Nathaniel
Bhamidi, Shankar
AuthorAffiliation 3 City & Regional Planning, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
University of Hail, SAUDI ARABIA
1 Statistics & Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
2 Statistics, University of Wisconsin at Madison, Madison, WI, United States of America
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– name: 3 City & Regional Planning, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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– name: 1 Statistics & Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/32348311$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
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2020 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2020 He et al 2020 He et al
Copyright_xml – notice: COPYRIGHT 2020 Public Library of Science
– notice: 2020 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SSID ssj0053866
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Snippet 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...
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StartPage e0230941
SubjectTerms 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
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Title Demarcating geographic regions using community detection in commuting networks with significant self-loops
URI https://www.ncbi.nlm.nih.gov/pubmed/32348311
https://www.proquest.com/docview/2396303618
https://search.proquest.com/docview/2396855996
https://pubmed.ncbi.nlm.nih.gov/PMC7190107
https://doaj.org/article/6cd2237349654ff2b4f836e508200d8a
http://dx.doi.org/10.1371/journal.pone.0230941
Volume 15
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