Building Multi-modal Crime Profiles with Growing Self Organising Maps

Part of the Studies in Computational Intelligence book series (SCI, volume 555)

Abstract

Profiling is important in law enforcement, especially in understanding the behaviours of criminals as well as the characteristics and similarities in crimes. It could provide insights to law enforcement officers when solving similar crimes and more importantly for pre-crime action, which is to act before crimes happen. Usually a single case captures data from the crime scene, offenders, etc. and therefore could be termed as multi-modality in data sources and subsequently has resulted a complex data fusion problem. Traditional criminal profiling requires experienced and skilful crime analysts or psychologists to laboriously associate and fuse multi-modal crime data. With the ubiquitous usage of digital data in crime and forensic records, law enforcement has also encountered the issue of big data. In addition, law enforcement professionals are always competing against time in solving crimes and facing constant pressures. Therefore, it is necessary to have a computational approach that could assist in reducing the time and efforts spent for the laborious fusion process in profiling multi-modal crime data. Besides obtaining the demographics, physical characteristics and the behaviours of criminals, a crime profile should also comprise of crime statistics and trends. In fact, crime and criminal profiles are highly interrelated and both are required in order to provide a holistic analysis. In this chapter, our approach proposes the fusion of multiple sources of crime data to populate a holistic crime profile through the use of Growing Self Organising Maps (GSOM).

Keywords

crime profiling multi-modal data mining data fusion artificial neural networks growing self organising maps 

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References

  1. 1.
    FBI Uniform Crime Reporting Systems, http://www.fbi.gov/ucr/ucr.htm
  2. 2.
    Los Angeles County Murder Cases, http://www.lacountymurders.com/caseinfo2.cfm
  3. 3.
    Public Practice Local Crime Profile, http://www.publicpratice.net/crime_potrait.htm
  4. 4.
    Adderley, R., Musgrove, P.: Police crime recording and investigation systems a user’s view. International Journal of Police Strategies & Management 24(1), 100–114 (2001)CrossRefGoogle Scholar
  5. 5.
    Adderley, R., Musgrove, P.B.: Data mining case study: Modeling the behavior of offenders who commit serious sexual assaults. In: KDD, pp. 215–220 (2001)Google Scholar
  6. 6.
    Adderley, R., Musgrove, P.B.: Modus operandi modelling of group offending: A data mining case study. International Journal of Police Science and Management 5(4), 265–276 (2003)CrossRefGoogle Scholar
  7. 7.
    Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: A self growing cluster development approach to data mining. In: IEEE Conference Systems, Man and Cybernetics, pp. 2901–2906 (1998)Google Scholar
  8. 8.
    Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks 11(3), 601–614 (2000)CrossRefGoogle Scholar
  9. 9.
    Baumgartner, K.C., Ferrari, S., Salfati, C.G.: Bayesian network modeling of offender behavior for criminal profiling. In: IEEE Conference on Decision and Control, pp. 2702–2709 (2005)Google Scholar
  10. 10.
    Bekerian, D.A., Jackson, J.L.: Chapter12 - critical issues in offender profiling. In: Jackson, J.L., Bekerian, D.A. (eds.) Offender Profiling: Theory, Research and Practice, pp. 209–220. John Wiley & Sons (1997)Google Scholar
  11. 11.
    Bloch, I., Hunter, A., Appriou, A., Ayoun, A., Benferhat, S., Besnard, P., Cholvy, L., Cooke, R., Cuppens, F., Dubois, D., Fargier, H., Grabisch, M., Kruse, R., Lang, J., Moral, S., Prade, H., Saffiotti, A., Smets, P., Sossai, C.: Fusion: General concepts and characteristics. International Journal of Intelligent Systems 16(10), 1107–1134 (2001)CrossRefGoogle Scholar
  12. 12.
    Brown, D.E.: Data mining, data fusion, and the future of systems engineering. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 26–30 (2002)Google Scholar
  13. 13.
    de Bruin, J.S., et al.: Data mining approaches to criminal career analysis. In: International Conference on Data Mining (ICDM), pp. 171–177 (2006)Google Scholar
  14. 14.
    Charles, J.: Ai and law enforcement. IEEE Intelligent Systems 13(1), 77–80 (1998)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Chen, H., Chung, W., Qin, Y., Chau, M., Xu, J.J., Wang, G., Zheng, R., Atabakhsh, H.: Crime data mining: An overview and case studies. In: National Conference on Digital Government Research (dg.o), pp. 1–5 (2003)Google Scholar
  16. 16.
    Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., Chau, M.: Crime data mining: A general framework and some examples. IEEE Computer 37(4), 50–56 (2004)CrossRefGoogle Scholar
  17. 17.
    Chen, M., Han, J., Yu, P.S.: Data mining: An overview from a database perspective. IEEE Transaction on Knowledge and Data Engineering 8(6), 866–883 (1996)CrossRefGoogle Scholar
  18. 18.
    Chu, H.C., Deng, D.J., Park, J.H.: Live data mining concerning social networking forensics based on a facebook session through aggregation of social data. IEEE Journal on Selected Areas in Communications 29(7), 1368–1376 (2011)CrossRefGoogle Scholar
  19. 19.
    Dasarathy, B.V.: Information fusion, data mining, and knowledge discovery. Information Fusion 4(1), 1 (2003)CrossRefGoogle Scholar
  20. 20.
    Elmaghraby, A.S., Kantardzic, M.M., Wachowiak, M.P.: Data mining from multimedia patient records. In: Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. Massive Computing, vol. 6, ch. 16, pp. 551–595. Springer, US (2006)CrossRefGoogle Scholar
  21. 21.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. Communications of The ACM 39(11), 27–34 (1996)CrossRefGoogle Scholar
  22. 22.
    Geradts, Z., Keijzer, J.: The image-database rebezo for shoeprints with developments on automatic classification of shoe outsole designs. Forensic Science International 82(1), 21–31 (1996)CrossRefGoogle Scholar
  23. 23.
    Helbicha, M., Hagenauera, J., Leitnerb, M., Edwardsc, R.: Exploration of unstructured narrative crime reports: an unsupervised neural network and point pattern analysis approach. Journal of Cartography and Geographic Information Science 40(4), 1–11 (2013)Google Scholar
  24. 24.
    Howitt, D.: Forensic and Criminal Psychology. Pearson Education (2002)Google Scholar
  25. 25.
    Kasabov, N.: Evolving systems for integrated multi-modal information processing. In: Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machine, ch. 13, pp. 257–271. Springer, London (2003)Google Scholar
  26. 26.
    Kocsis, R.N.: An empirical assessment of content in criminal psychological profiles. International Journal of Offender Therapy and Comparative Criminology 47(1), 37–46 (2003)CrossRefGoogle Scholar
  27. 27.
    Kohonen, T.: Self Organizing Maps. Springer (2001)Google Scholar
  28. 28.
    Lin, S., Brown, D.E.: Criminal incident data association using the OLAP technology. In: Chen, H., Miranda, R., Zeng, D.D., Demchak, C.C., Schroeder, J., Madhusudan, T. (eds.) ISI 2003. LNCS, vol. 2665, pp. 13–26. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  29. 29.
    Martin, C., grosse Deters, H., Nattkemper, T.W.: Fusion biomedical multi-modal data for exploratory data analysis. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 798–807. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  30. 30.
    McCue, C.: Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis. Butterworth-Heinemann (2007)Google Scholar
  31. 31.
    Memon, Q.A., Mehboob, S.: Crime investigation and analysis using neural nets. In: IEEE 7th International Multi Topic Conference (INMIC), pp. 346–350 (2003)Google Scholar
  32. 32.
    Mena, J.: Investigative Data Mining for Security and Criminal Detection. Butterworth-Heinemann (2003)Google Scholar
  33. 33.
    Oatley, G., Ewart, B., Zeleznikow, J.: Decision support systems for police: Lessons from the application of data mining techniques to soft forensic evidence. Artificial Intelligence and Law 14(1-2), 35–100 (2006)CrossRefGoogle Scholar
  34. 34.
    Pastra, K., Saggion, H., Wilks, Y.: Extracting relational facts for indexing and retrieval of crime-scene photographs. Knowledge-Based Systems 16, 313–320 (2003)CrossRefGoogle Scholar
  35. 35.
    Pinizzotto, A., Finkel, N.: Criminal personality profiling: An outcome and process study. Law and Human Behaviour 14(3), 215–233 (1990)CrossRefGoogle Scholar
  36. 36.
    van der Putten, P., Kok, J.N., Gupta, A.: Why the information explosion can be bad for data mining, and how data fusion provides a way out. In: 2nd SIAM International Conference on Data Mining (SDM) (2002)Google Scholar
  37. 37.
    Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann (1999)Google Scholar
  38. 38.
    Ross, A., Jain, A.K., Qian, J.-Z.: Information fusion in biometrics. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 354–359. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  39. 39.
    Siegel, L.J.: Criminology: Theories, Patterns, and Typologies. Thompson Wadsworth (2007)Google Scholar
  40. 40.
    Strano, M.: A neural network applied to criminal psychological profiling: An italian initiative. International Journal of Offender Therapy and Comparative Criminology 48(4), 495–503 (2004)CrossRefMathSciNetGoogle Scholar
  41. 41.
    Torra, V.: Trends in information fusion in data mining. In: Torra, V. (ed.) Information Fusion in Data Mining. STUDFUZZ, vol. 123, pp. 1–6. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  42. 42.
    Torra, V., Narukawa, Y.: Modelling Decisions: Information Fusion and Aggregation Operators. Springer, Berlin (2007)Google Scholar
  43. 43.
    Turvey, B.: Criminal Profiling: An Introduction to Behavioral Evidence Analysis. Academic Press (1999)Google Scholar
  44. 44.
    Fayyad, U., Piatetsky-Shapir, G., Smyth, P.: From data mining to knowledge discovery in databases. In: American Association for Artificial Intelligence (AAAI), pp. 37–54 (1996)Google Scholar
  45. 45.
    Waltz, E.L.: Information understanding: Integrating data fusion and data mining processes. In: IEEE International Symposium on Circuits and Systems (ISCAS 1998), pp. 553–556 (1998)Google Scholar
  46. 46.
    Wang, G., Chen, H., Xu, J., Atabakhsh, H.: Automatically detecting criminal identity deception: An adaptive detection algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 36(5), 988–999 (2006)CrossRefGoogle Scholar
  47. 47.
    Westphal, C., Blaxton, T.: Data Mining Solutions: Methods and Tools for Solving Real-World Problems. John Wiley & Sons (1998)Google Scholar
  48. 48.
    Wickramasinghe, L.K., Alahakoon, L.D.: Dynamic self organizing maps for discovery and sharing of knowledge in multi agent systems. International Journal on Web Intelligence and Agent Systems 3(1), 31–47 (2005)Google Scholar
  49. 49.
    Xu, J., Chen, H.: Criminal network analysis and visualization. Communications of the ACM 48(6), 101–107 (2005)CrossRefGoogle Scholar
  50. 50.
    Xue, Y., Brown, D.E.: A decision model for spatial site selection by criminals: A foundation for law enforcement decision support. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 33(1), 78–85 (2003)CrossRefGoogle Scholar
  51. 51.
    Yager, R.R.: A framework for multi-source data fusion. Information Sciences 163, 175–200 (2004)CrossRefMathSciNetGoogle Scholar
  52. 52.
    Zhang, S., Zhang, C., Wu, X.: Knowledge Discovery in Multiple Databases. Springer (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Information and Business Analytics, Faculty of Business and LawDeakin UniversityVictoriaAustralia

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