Predicting the geo-temporal variations of crime and disorder

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

    Research output: Contribution to journalArticlepeer-review

    49 Citations (Scopus)


    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).

    Original languageEnglish
    Pages (from-to)623-634
    Number of pages12
    JournalInternational Journal of Forecasting
    Issue number4
    Publication statusPublished - 1 Oct 2003


    • Artificial neural networks
    • Autoregressive model
    • Cluster analysis
    • Crime forecasting
    • Gamma test
    • Geographic information system


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