Predicting the geo-temporal variations of crime and disorder

Jonathan J. Corcoran, Ian D. Wilson, Andrew Ware

    Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

    49 Dyfyniadau (Scopus)

    Crynodeb

    Traditional police boundaries-precincts, patrol districts, etc.-often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).

    Iaith wreiddiolSaesneg
    Tudalennau (o-i)623-634
    Nifer y tudalennau12
    CyfnodolynInternational Journal of Forecasting
    Cyfrol19
    Rhif cyhoeddi4
    Dynodwyr Gwrthrych Digidol (DOIs)
    StatwsCyhoeddwyd - 1 Hyd 2003

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