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Crimes analysis software: ‘pins in maps’, clustering and Bayes net prediction

Oatley, G.C. and Ewart, B.W. (2003) Crimes analysis software: ‘pins in maps’, clustering and Bayes net prediction. Expert Systems with Applications, 25 (4). pp. 569-588.

Link to Published Version: http://dx.doi.org/10.1016/S0957-4174(03)00097-6
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Abstract

The OVER Project was a collaboration between West Midlands Police, UK, the Centre for Adaptive Systems, and Psychology Division, from the University of Sunderland. The Project was developed primarily to assist the Police with the high volume crime, burglary from dwelling houses. A developed software system enables the trending of historical data, the testing of ‘short term’ hunches, and the development of ‘medium’ and long term’ strategies to burglary and crime reduction, based upon victim, offender, location and details of victimisations. The software utilises mapping and visualisation tools and is capable of a range of sophisticated predictions, tying together statistical techniques with theories from forensic psychology and criminology.

The statistical methods employed (including multi-dimensional scaling, binary logistic regression) and ‘data-mining’ technologies (including neural networks) are used to investigate the impact of the types of evidence available and to determine the causality in this domain. The final predictions on the likelihood of burglary are calculated by combining all of the varying sources of evidence into a Bayesian belief network. This network is embedded in the developed software system, which also performs data cleansing and data transformation for presentation to the developed algorithms.

It is important that derived statistics from the software and predictions are interpretable by the intended users of the decision support system, namely Police sector managers, and this paper includes some of the design decisions based upon the forensic psychology and criminology literature, including the graphical representation of geographic data and presentation of results of analyses.

Publication Type: Journal Article
Publisher: Elsevier BV
Copyright: © 2003 Elsevier Ltd
URI: http://researchrepository.murdoch.edu.au/id/eprint/36007
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