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Urban attractors: Discovering patterns in regions of attraction in cities

PloS one, 2021-04, Vol.16 (4), p.e0250204-e0250204 [Peer Reviewed Journal]

COPYRIGHT 2021 Public Library of Science ;COPYRIGHT 2021 Public Library of Science ;2021 Alhazzani et al 2021 Alhazzani et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0250204 ;PMID: 33901224

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  • Title:
    Urban attractors: Discovering patterns in regions of attraction in cities
  • Author: Alhazzani, May ; Alhasoun, Fahad ; Alawwad, Zeyad ; González, Marta C
  • Chiabaut, Nicolas
  • Subjects: Analysis ; Central business districts ; Computational linguistics ; Earth Sciences ; Engineering and Technology ; Evaluation ; Language processing ; Natural language interfaces ; Physical Sciences ; Research and Analysis Methods ; Social Sciences ; Urban population ; Urbanization
  • Is Part Of: PloS one, 2021-04, Vol.16 (4), p.e0250204-e0250204
  • Description: Understanding the dynamics by which urban areas attract visitors is important in today's cities that are continuously increasing in population towards higher densities. Identifying services that relate to highly attractive districts is useful to make policies regarding the placement of such places. Thus, we present a framework for classifying districts in cities by their attractiveness to daily commuters and relating Points of Interests (POIs) types to districts' attraction patterns. We used Origin-Destination matrices (ODs) mined from cell phone data that capture the flow of trips between each pair of places in Riyadh, Saudi Arabia. We define the attraction profile for a place based on three main statistical features: The number of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of locations from where trips started. We used a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We discovered three main types of Urban Attractors in Riyadh during the morning period: Global, which are significant places in the city, Downtown, which contains the central business district, and Residential attractors. In addition, we uncovered what makes districts possess certain attraction patterns. We used a statistical significance testing approach to quantify the relationship between Points of Interests (POIs) types (services) and the patterns of Urban Attractors detected.
  • Publisher: United States: Public Library of Science
  • Language: English
  • Identifier: ISSN: 1932-6203
    EISSN: 1932-6203
    DOI: 10.1371/journal.pone.0250204
    PMID: 33901224
  • Source: GFMER Free Medical Journals
    PubMed Central
    Public Library of Science (PLoS)
    ProQuest Central
    DOAJ Directory of Open Access Journals
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