Detect Megaregional Communities Using Network Science Analytics

Bibliographic Data

ID13113236
AuthorsMing Zhang (0000-0003-3023-1419, The University of Texas at Austin, corresponding author), Bolin Lan
Year2022
Volume6
Issue1
Pages12-12
Publication date2022-02-16
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueUrban Science (JOURNAL)
Journal identifiersISSN: 2413-8851 • E-ISSN: 2413-8851
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/urbansci6010012
OpenAlexW4212963042
LanguageEN
Citations received1
References cited30

Urban science research and the research on megaregions share a common interest in the system of cities and its implications for world urbanization and sustainability. The two lines of inquiry currently remain largely separate efforts. This study aims to bridge urban science and megaregion research by applying network science’s community detection algorithm to explore the spatial pattern of megaregions in the contiguous United States. A network file was constructed consisting of county centroids as nodes, the direct links between each pair of counties as edges, and inter-county commuting flows as the weight to capture spatial interactions. Analyses were carried out at two levels, one at the national level using Gephi and the other for the State of Texas involving NetworkX, an open-source Python programming package to implement a weighted community detection algorithm. Results show the detected communities largely conforming to the qualitative knowledge on megaregions. Despite a number of limitations, the study indicates the great potential of applying network science analytics to improve understanding of the spatial process of megaregions

Analytics · Complex network · Data mining · Data science · Geography · Network analysis · Network science · Python (programming language · Urbanization · World Wide Web · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Land Use and Ecosystem Services · Urban Design and Spatial Analysis

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