Decision support systems for police: Lessons from the application of data mining techniques to “soft” forensic evidence [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Artificial Intelligence and Law 14 (1-2):35-100 (2006)
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 fingerprint evidence. Three objectives underpin this paper. First, 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. Second, 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 first 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 beneficial 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 profiling crimes or offenders 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.
|Keywords||data mining decision support systems matching prediction|
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References found in this work BETA
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Judea Pearl (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
Douglas Walton (2002). Legal Argumentation and Evidence. Penn State University Press.
David A. Schum (1994). The Evidential Foundations of Probabilistic Reasoning. Wiley-Interscience.
Citations of this work BETA
Xingan Li & Martti Juhola (2014). Country Crime Analysis Using the Self-Organizing Map, with Special Regard to Demographic Factors. AI and Society 29 (1):53-68.
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