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Decision Support Systems for Police: Lessons from the Application of Data Mining Techniques to 'Soft' Forensic Evidence

Oatley, Giles and Ewart, Brian and Zeleznikow, John (2006) Decision Support Systems for Police: Lessons from the Application of Data Mining Techniques to 'Soft' Forensic Evidence. Artificial Intelligence and Law, 14 (1-2). pp. 35-100. ISSN 0924-8463

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Abstract

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors' work with three Police Services. The focus is upon the use of \soft" forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than \hard" evidence such as DNA or ¯ngerprint evidence. Three objectives underpin this paper. Firstly, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Secondly, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by ¯rst considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both bene¯cial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for pro¯ling crimes or o®enders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems.

Item Type: Article
Uncontrolled Keywords: ResPubID11170, data mining, decision support systems, matching, prediction
Subjects: Faculty/School/Research Centre/Department > School of Management and Information Systems
FOR Classification > 0806 Information Systems
FOR Classification > 1899 Other Law and Legal Studies
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Depositing User: VUIR
Date Deposited: 19 Aug 2011 04:21
Last Modified: 24 Mar 2013 23:40
URI: http://vuir.vu.edu.au/id/eprint/3059
DOI: 10.1007/s10506-006-9023-z
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Citations in Scopus: 8 - View on Scopus

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