TY - JOUR
T1 - Predicting the geo-temporal variations of crime and disorder
AU - Corcoran, Jonathan J.
AU - Wilson, Ian D.
AU - Ware, Andrew
PY - 2003/10/1
Y1 - 2003/10/1
N2 - 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).
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
U2 - 10.1016/S0169-2070(03)00095-5
DO - 10.1016/S0169-2070(03)00095-5
M3 - Article
AN - SCOPUS:0142118660
VL - 19
SP - 623
EP - 634
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 4
ER -