Predicting the geo-temporal variations of crime and disorder

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

doi:10.1016/S0169-2070(03)00095-5

Predicting the geo-temporal variations of crime and disorder

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

Research output: Contribution to journal › Article › peer-review

49 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	623-634
Number of pages	12
Journal	International Journal of Forecasting
Volume	19
Issue number	4
DOIs	https://doi.org/10.1016/S0169-2070(03)00095-5
Publication status	Published - 1 Oct 2003

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/S0169-2070(03)00095-5

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title = "Predicting the geo-temporal variations of crime and disorder",

abstract = "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).",

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AU - Wilson, Ian D.

AU - Ware, Andrew

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AB - 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).

KW - Artificial neural networks

KW - Autoregressive model

KW - Cluster analysis

KW - Crime forecasting

KW - Gamma test

KW - Geographic information system

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JF - International Journal of Forecasting

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ER -

Predicting the geo-temporal variations of crime and disorder

Abstract

Keywords

UN SDGs

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