Abstract
Our central aim is the development of decision support systems for purposes such as profiling single and series of crimes or offenders, and matching and predicting crimes. This paper presents research in this area for the high-volume crime of Burglary Dwelling House, examining the operational use of networks and the metric of brokerage from the social network analysis literature. Our work builds upon several years of experimentation using forensic psychology guided exploratory techniques from artificial intelligence, statistics and spatial statistics.
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References
AIT, 2005. Homepage of Advanced Information Technologies. Online, accessed 5 January 2005, Available at: http://www.alphatech.com/primary/technologies/knowledge_discovery_data_mining/link_analysis_and_discovery.htm
Batagelj, V., Mrvar, A.: Pajek – Analysis and Visualization of Large Networks. In: Junger, M., Mutzel, P. (eds.) Graph Drawing Software. Mathematics and Visualization, pp. 77–103. Springer, Heidelberg (2003) ISBN 3-540-00881-0
Ewart, B.W., Oatley, G.C., Burn, K.: Matching Crimes Using Burglars. Modus Operandi: A Test of Three Models (2004) (forthcoming)
Getoor, L.: Link mining: a new data mining challenge. In: Dzeroski, S., De Raedt, L. (eds.) SIGKDD Explorations Special Issue on Multi-Relational Data Mining, July 2003, vol. 5(1), pp. 84–90 (2003)
Goldberg, H.G., Wong, R.W.H.: Restructuring Transactional Data for Link Analysis in the FinCEN AI System. In: Jensen, D., Goldberg, H. (eds.) Artificial Intelligence and Link analysis. Papers from the AAAI Fall Symposium, Orlando, Florida, Tech report FS-98-01 (1998)
Hauck, R., Atabakhsh, H., Onguasith, P., Gupta, H., Chen, H.: Using Coplink to Analyse Criminal-Justice Data. IEEE Computer 35, 30–37 (2002)
Horn, R.D., Birdwell, J.D., Leedy, L.W.: Link discovery tool. In: Proceedings of the Counterdrug Technology Assessment Center’s ONDCP/CTAC International Symposium, Chicago, IL, August 18-22 (1997)
I2 (2004). Investigative Analysis Software home page. [Online, accessed 2004], Available: http://www.i2.co.uk/Products/AnalystsWorkstation/default.asp
Johnson, S.D., Bowers, K.J.: The burglary as a clue to the future: the beginnings of prospective hot-spotting. The European Journal of Criminology 1(2), 237–255 (2004)
Klerks, P.: The Network Paradigm Applied to Criminal Organisations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands. Connections 24(3), 53–65 (2001)
Leary, R.M.: Home Office Policing & Reducing Crime Unit. (2001) Evaluation of the Impact of the FLINTS Software System in West Midlands and Elsewhere. Home Office, London (2001)
Lyons, D., Tseytin, G.S.: Phenomenal data mining and link analysis. In: Jensen, D., Henry Goldberg, C. (eds.) Artificial Intelligence and Link Analysis, 1998 Fall Symposium, pp. 68–75. AAAI Press, Menlo Park (1998)
Merry, S.: Crime Analysis: Principles for Analysing Everyday Serial Crime. In: Canter, D., Alison, L. (eds.) Profiling Property Crimes, pp. 297–315. Ashgate Publishing Co. (2000)
Oatley, G.C., Ewart, B.W.: Crimes Analysis Software: ‘Pins in Maps’, Clustering and Bayes Net Prediction. Expert Systems with Applications 25(4), 569–588 (2003)
Oatley, G.C., Zeleznikow, J., Ewart, B.W.: Matching and Predicting Crimes. In: Macintosh, A., Ellis, R., Allen, T. (eds.) Applications and Innovations in Intelligent Systems XII. Proceedings of AI2004, The Twenty-fourth SGAI International Conference on Knowledge Based Systems and Applications of Artificial Intelligence, pp. 19–32. Springer, Heidelberg (2004) ISBN 1-85233-908-X
Rossmo, K.: Geographic Profiling. CRC Press, Boca Raton (1999) ISBN: 0849381290
Washio, T., Motoda, H.: State of the Art of Graph-based Data Mining. In: Dzeroski, S., De Raedt, L. (eds.) SIGKDD Explorations Special Issue on Multi-Relational Data Mining, July 2003, vol. 5(1), pp. 59–69 (2003)
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Oatley, G., Zeleznikow, J., Leary, R., Ewart, B. (2005). From Links to Meaning: A Burglary Data Case Study. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_114
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DOI: https://doi.org/10.1007/11554028_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28897-8
Online ISBN: 978-3-540-31997-9
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