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    Network-based space-time scan statistics for detecting micro-scale hotspots

    Shiode, Shino and Shiode, Narushige (2022) Network-based space-time scan statistics for detecting micro-scale hotspots. Sustainability 14 (24), p. 16902. ISSN 2071-1050.

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    Events recorded in urban areas are often confined by the micro-scale geography of street networks, yet existing spatial–analytical methods do not usually account for the shortest-path distance of street networks. We propose space–time NetScan, a new spatial–temporal analytical method with improved accuracy for detecting patterns of concentrations across space and time. It extends the notion of a scan-statistic-type search window by measuring space-time patterns along street networks in order to detect micro-scale concentrations of events at the street-address level with high accuracy. Performance tests with synthetic data demonstrate that space-time NetScan outperforms existing methods in detecting the location, shape, size and duration of hotspots. An empirical study with drug-related incidents shows how space-time NetScan can improve our understanding of the micro-scale geography of crime. Aside from some abrupt one-off incidents, many hotspots form recurrent hotbeds, implying that drug-related crimes tend to persist in specific problem places.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Humanities and Social Sciences > School of Social Sciences
    Depositing User: Shino Shiode
    Date Deposited: 07 Feb 2023 06:40
    Last Modified: 02 Aug 2023 18:20


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