Data mining and crime analysis

Abstract

AbstractAn essential component of criminal investigation involves the interrogation of large databases of information held by police and other criminal justice agencies. Data mining and decision support systems have an important role to play in assisting human inference in this forensic domain that creates one of the most challenging decision‐making environments. Technologies range widely and include social network analysis, geographical information systems, and data mining technologies for clustering crimes, finding links between crime and profiling offenders, identifying criminal networks, matching crimes, generating suspects, and predicting criminal activity. This paper does not intend to cover the gamut of techniques available to the investigator of crime as this has been presented elsewhere (Oatley GC, Ewart BW, Zeleznikow J. Decision support systems for police: lessons from the application of data mining techniques to ‘soft’ forensic evidence. Artif Intell Law 2006, 14:35–100). Rather, the objective is to highlight issues of implementation and interpretation of the techniques available to the crime analyst. To this end, the authors draw from their experiences of working with real‐world crime databases (Oatley GC, Belem B, Fernandes K, Hoggarth E, Holland B, Lewis C, Meier P, Morgan K, Santhanam J, et al. The gang gun‐crime problem—solutions from social network theory, epidemiology, cellular automata, Bayesian networks and spatial statistics. Accepted: book chapter for IEEE publication Computational Forensics; 2008; Oatley GC, McGarry K, Ewart BW. Offender network metrics. WSEAS Trans Inf Sci Appl 2006, 3:2440–2448; Oatley GC, Ewart BW. Crimes analysis software: pins in maps, clustering and Bayes net prediction. Expert Syst Appl 2003, 25:569–588), involving gun and gang crime, fraud, terrorism, burglary, and retail crime. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 147‐153 DOI: 10.1002/widm.6This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Science and Technology Application Areas > Society and Culture Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction