An Early Warning System for the Prediction of Criminal Careers
Dismantling networks of career criminals is one of the focus points of modern police forces. A key factor within this area of law enforcement is the accumulation of delinquents at the bottom of the criminal hierarchy. A deployed early warning system could benefit the cause by supplying an automated alarm after every apprehension, sounding when this perpetrator is likely to become a career criminal. Such a system can easily be built upon existing, strategic, analysis already performed at headquarters. We propose a tool that superimposes a 2-dimensional extrapolation on a static clustering, that describes the movement in time of an offender through the criminal spectrum. Using this extrapolation, possible future attributes are calculated and the criminal is classified accordingly. If the predicted class falls within the danger category, the system notifies police officials. We outline the implementation of such a tool and highlight test results on the Dutch National Criminal Record Database. Certain problematic situations, like time constraints, privacy concerns and reliability issues, are also discussed.
KeywordsEarly Warning System Criminal Career Crime Syndicate Extrapolation Scheme Cluster Reduction
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- 1.Abdi, H.: Signal detection theory. In: Salkind, N.J. (ed.) Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks (2007)Google Scholar
- 2.Adderley, R., Musgrove, P.B.: Data mining case study: Modeling the behavior of offenders who commit serious sexual assaults. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), New York, pp. 215–220 (2001)Google Scholar
- 3.Blumstein, A., Cohen, J., Roth, J.A., Visher, C.A.: Criminal Careers and “Career Criminals”. National Academies Press, Washington (1986)Google Scholar
- 4.Chau, M., Atabakhsh, H., Zeng, D., Chen, H.: Building an infrastructure for law enforcement information sharing and collaboration: Design issues and challenges. In: Proceedings of The National Conference on Digital Government Research (2001)Google Scholar
- 5.Chau, M., Xu, J., Chen, H.: Extracting meaningful entities from police narrative reports. In: Proceedings of The National Conference on Digital Government Research, pp. 1–5 (2002)Google Scholar
- 6.Chen, H., Atabakhsh, H., Petersen, T., Schroeder, J., Buetow, T., Chaboya, L., O’Toole, C., Chau, M., Cushna, T., Casey, D., Huang, Z.: COPLINK: Visualization for crime analysis. In: Proceedings of The National Conference on Digital Government Research, pp. 1–6 (2003)Google Scholar
- 8.Cocx, T.K., Kosters, W.A., Laros, J.F.J.: Enhancing the automated analysis of criminal careers. In: SIAM Workshop on Link Analysis, Counterterrorism, and Security 2008 (LACTS 2008) (2008)Google Scholar
- 11.The FBI strategic plan (2004–2009), http://www.fbi.gov/
- 12.Goldberg, H.G., Wong, R.W.H.: Restructuring transactional data for link analysis in the FinCEN AI system. In: Papers from the AAAI Fall Symposium, pp. 38–46 (1998)Google Scholar
- 13.Kosters, W.A., Laros, J.F.J.: Metrics for mining multisets. In: Proceedings of the Twenty-seventh SGAI International Conference on Artificial Intelligence SGAI 2007, pp. 293–303 (2007)Google Scholar
- 14.Oatley, G.C., Zeleznikow, J., Ewart, B.W.: Matching and predicting crimes. In: Proceedings of the Twenty-fourth SGAI International Conference on Knowledge Based Systems and Applications of Artificial Intelligence (SGAI 2004), pp. 19–32 (2004)Google Scholar
- 15.Schermer, B.: Software Agents, Surveillance, and the Right to Privacy: A Legislative Framework for Agent-enabled Surveillance. PhD thesis, Leiden University (2007)Google Scholar