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Application of the self-organising map to visualisation of and exploration into historical development of criminal phenomena in the USA, 1960–2007

Published Online:pp 120-142https://doi.org/10.1504/IJSSS.2014.062435

Underneath the prevalence of criminal phenomena, many variables can be used to describe the background data such as the historical development of crime against socio-economic development. With large amount of data and evolution of data processing, multi-dimensional analysis becomes possible. Based on longitudinal (1960–2007), crime and socio-economic data (22 variables), we used the self-organising map (SOM) for development of criminal phenomena in the USA. Classification power of variables was evaluated and, e.g., k-means clustering was used for obtaining comparable results. After initial processing of the data with the SOM, six clusters of years were identified. We show how the SOM is applied to analysing criminal phenomena over a span of several decades. Results proved that, after the evaluation of variables for classification, and validation with k-means clustering, nearest neighbour searching, decision trees, and logistic discriminant analysis, SOM can be a new tool for mapping criminal phenomena processing multivariate data.

Keywords

development of criminal phenomena, data mining, self-organising map, SOM, variable evaluation, k-means clustering, decision trees, logistic discriminant analysis, nearest neighbour searching, the USA

References

  • 1. Abidogun, O.A. (2005). Data Mining, Fraud Detection and Mobile Telecommunications: Call Pattern Analysis with Unsupervised Neural Networks. South Africa, Dissertation of University of the Western Cape Google Scholar
  • 2. Adderley, R. (2004). ‘The use of data mining techniques in operational crime fighting’. in Proceedings of Symposium on Intelligence and Security Informatics. 3073, (10/06/2004), ETATS-UNIS, Tucson AZ, 2, 418-425 Google Scholar
  • 3. Adderley, R. , Musgrave, P. (2003). ‘Modus operandi modelling of group offending: a data-mining case study’. International Journal of Police Science and Management. 5, 4, 265-276 Google Scholar
  • 4. Adderley, R. , Townsley, M. , Bond, J. (2007). ‘Use of data mining techniques to model crime scene investigator performance’. in Proceedings of the 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. 170-176 Google Scholar
  • 5. Axelsson, S. (2005). Understanding Intrusion Detection through Visualization. Goteborg, Sweden:Chalmers University of Technology , PhD thesis Google Scholar
  • 6. Blumstein, A. , Wallman, J. (2000). The Crime Drop in America. New York:Cambridge University Press Google Scholar
  • 7. Brockett, P.L. , Xia, X. , Derrig, R.A. (1998). ‘Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud’. The Journal of Risk and Insurance. 65, 2, 245-274 Google Scholar
  • 8. Bureau of Justice Statistics (2012). ‘Reported crime in the United States 1960–2007’. (accessed 10 August 2012), [online] http://bjsdata.ojp.usdoj.gov/dataonline/Search/Crime/State/StatebyState.cfm?NoVariables=Y&CFID = 350216&CFTOKEN = 91023531 Google Scholar
  • 9. Chung, W. , Chen, H. , Chaboya, L.G. , O’Toole, C. D. , Atabakhsh, H. (2005). ‘Evaluating event visualization: a usability study of COPLINK spatio-temporal visualizer’. International Journal of Human-Computer Studies. 62, 1, 127-157 Google Scholar
  • 10. Conklin, J.E. (2003). Why Crime Rates Fell. Boston, MA:Pearson Education Google Scholar
  • 11. Dahmane, M. , Meunier, J. (2005). ‘Real-time video surveillance with self-organizing maps’. in Proceedings of the Second Canadian Conference on Computer and Robot Vision (CRV ‘05). Washington, DC Google Scholar
  • 12. Deboeck, G. (2000). ‘Self-organizing patterns in world poverty using multiple indicators of poverty repression and corruption’. Neural Network World. 10, 1–2, 239-254 Google Scholar
  • 13. Fei, B.K. , Eloff, J. , Venter, H. , Olivier, M. (2005). ‘Exploring data generated by computer forensic tools with self-organising maps’. in Proceedings of the IFIP Working Group 11.9 on Digital Forensics. Google Scholar
  • 14. Fei, B.K. , Eloff, J.H. , Olivier, M.S. , Venter, H.S. (2006). ‘The use of self-organising maps for anomalous behaviour detection in a digital investigation’. Forensic Science International. 162, 1–3, 33-37 Google Scholar
  • 15. Grosser, H. , Britos, P. , García-Martínez, R. , Ali, M. Esposito, F. (2005). ‘Detecting fraud in mobile telephony using neural networks’. IEA/AIE 2005. Springer-Verlag Berlin Heidelberg, 613-615, LNAI 3533 Google Scholar
  • 16. Hollmén, J. (1996). Process Modeling Using the Self-Organizing Map. (accessed 15 July 2013), [online] http://users.ics.tkk.fi/jhollmen/dippa/node26.html#SECTION00524300000000000000 Google Scholar
  • 17. Hollmén, J. (2000). User Profiling and Classification for Fraud Detection in Mobile Communications Networks. Finland:Helsinki University of Technology , PhD thesis Google Scholar
  • 18. Hollmén, J. , Tresp, V. , Simula, O. (1999). ‘A self-organizing map for clustering probabilistic models’. in Proceedings of Conference on Artificial Neural Networks. 946-951, Conference Publication No. 470, IEE 1999 Google Scholar
  • 19. Huysmans, J. , Chen, H. et al. (2006). ‘Country corruption analysis with self-organizing maps and support vector machines’. WISI 2006, 104-114, LNCS 3917 Google Scholar
  • 20. Juhola, M. , Siermala, M. (2012a). ‘A scatter method for data and variable importance evaluation’. Integrated Computer-Aided Engineering. 19, 2, 137-149 Google Scholar
  • 21. Juhola, M. , Siermala, M. (2012b). ScatterCounter Software. (accessed 15 July 2013), [online] http://www.uta.fi/sis/cis/research_groups/darg/publications.html Google Scholar
  • 22. Kangas, L.J. (2001). Artificial Neural Network System for Classification of Offenders in Murder and Rape Cases. Finland:The National Institute of Justice Google Scholar
  • 23. Kohonen, T. (1990). ‘The self organizing map’. in Proceedings of the IEEE. 78, 9, 1464-1480 Google Scholar
  • 24. Kohonen, T. (1997). Self-organizing Maps. New York, USA:Springer-Verlag Google Scholar
  • 25. Lampinen, T. , Koivisto, H. , Honkanen, T. , Halgamuge, Saman K. Wang, Lipo (2005). ‘Profiling network applications with fuzzy C-means and self-organizing maps’. Classification and Clustering for Knowledge Discovery. 4, Berlin:Springer , 15-27 Google Scholar
  • 26. Lee, S-C. , Huang, M-J. (2002). ‘Applying AI technology and rough set theory for mining association rules to support crime management and fire-fighting resources allocation’. Journal of Information, Technology and Society. 2, 65 Google Scholar
  • 27. Lemaire, V. , Clérot, F. , Halgamuge, Saman K. Wang, Lipo (2005). ‘The many faces of a Kohonen map – a case study: SOM-based clustering for on-line fraud behaviour classification’. Classification and Clustering for Knowledge Discovery. 4, Berlin:Springer , 1-13 Google Scholar
  • 28. Leufven, C. (2006). Detecting SSH Identity Theft in HPC Cluster Environments using Self-organizing Maps. Sweden:Linköping University , Master’s thesis Google Scholar
  • 29. Li, S-T. , Tsai, F-C. , Kuo, S-C. , Cheng, Y-C.A. (2006). ‘Knowledge discovery approach to supporting crime prevention’. in Proceedings of the Joint Conference on Information Sciences. Taiwan Google Scholar
  • 30. Lozano, S. , Gutierrez, E. (2008). ‘Data envelopment analysis of the human development index’. International Journal of Society Systems Science. 1, 2, 132-150 AbstractGoogle Scholar
  • 31. Martin, J.A. , Hamilton, B.E. , Sutton, P.D. , Ventura, S.J. , Menacker, F. , Kirmeyer, S. , Mathews, T.J. (2006). Births: Final Data for 2006. 57, 7, 29, National Vital Statistics Reports Google Scholar
  • 32. Memon, Q.A. , Mehboob, S. (2006). ‘Crime investigation and analysis using neural nets’. in Proceedings of International Joint Conference on Neural Networks. Washington, DC, 346-350 Google Scholar
  • 33. Oatley, G.C. , Ewart, B.W. , Zeleznikow, J. (2006). ‘Decision support systems for police: lessons from the application of data mining techniques to “soft” forensic evidence’. Artificial Intelligence and Law. 14, 1, 35-100 Google Scholar
  • 34. Priya, R.V. , Vadivel, A. , Thakur, R.S. (2012). ‘Maximal pattern mining using fast CP-tree for knowledge discovery’. International Journal of Information Systems and Social Change. 3, 1, 56-74 Google Scholar
  • 35. Rhodes, B. , Mahaffey, J. , Cannady, J. (2000). ‘Multiple self-organizing maps for intrusion detection’. in Proceedings of the 23rd National Information Systems Security Conference. October 16–19, Baltimore, Maryland, USA Google Scholar
  • 36. Rock, R. (1994). History of Criminology. Aldershot, UK:Dartmouth Publishing Google Scholar
  • 37. Situngkir, H. (2003). ‘Emerging the emergence sociology: the philosophical framework of agent-based social studies’. Journal of Social Complexity. 1, 2, 3-15 Google Scholar
  • 38. The Community Safety and Crime Prevention Council (1996). The Root Causes of Crime – CS&CPC Statement on the Root Causes of Crime. (accessed 15 July 2013)., [online] http://preventingcrime.ca/ documents/Causes_of_Crime.pdf Google Scholar
  • 39. US Census Bureau (2012). The 2012 Statistical Abstract: Historical Statistics. (accessed 15 July 2013), [online] http://www.census.gov/compendia/statab/hist_stats.html Google Scholar
  • 40. Viscovery (2012). Viscovery SOMine 5.2. (accessed 10 August 2012), [online] http://www.viscovery.net/somine/ Google Scholar
  • 41. Wadsworth, T. (2010). ‘Is immigration responsible for the crime drop? An assessment of the influence of immigration on changes in violent crime between 1990 and 2000’. Social Science Quarterly. 91, 2, 531-553 Google Scholar
  • 42. Zaslavsky, V. , Strizhak, A. (2006). ‘Credit card fraud detection using self-organizing maps’. Information and Security: An International Journal. 18, 48-63 Google Scholar
  • 43. Zimring, F.E. (2007). The Great American Crime Decline. New York:Oxford University Press Google Scholar