Demarcating geographic regions using community detection in commuting networks with significant self-loops.
PLoS One
; 15(4): e0230941, 2020.
Article
em En
| MEDLINE
| ID: mdl-32348311
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Meios de Transporte
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article