, Volume 15, Issue 1, pp 49–74 | Cite as

Crime analysis through spatial areal aggregated density patterns

  • Peter PhillipsEmail author
  • Ickjai Lee


Intelligent crime analysis allows for a greater understanding of the dynamics of unlawful activities, providing possible answers to where, when and why certain crimes are likely to happen. We propose to model density change among spatial regions using a density tracing based approach that enables reasoning about large areal aggregated crime datasets. We discover patterns among datasets by finding those crime and spatial features that exhibit similar spatial distributions by measuring the dissimilarity of their density traces. The proposed system incorporates both localized clusters (through the use of context sensitive weighting and clustering) and the global distribution trend. Experimental results validate and demonstrate the robustness of our approach.


Crime analysis Spatial distribution Density tracing Areal aggregated data 


  1. 1.
    Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Buneman P, Jajodia S (eds) Proceedings of the ACM SIGMOD’93 international conference on management of data. ACM Press, Washington, DC, pp 207–216Google Scholar
  2. 2.
    Bailey TC, Gatrell AC (1995) Interactive spatial analysis. Longman Scientific & Technical, Harlow, UKGoogle Scholar
  3. 3.
    Boba R (2005) Crime analysis and crime mapping. Sage Publications, Thousand Oaks, CaliforniaGoogle Scholar
  4. 4.
    Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M (2004) Crime data mining: a general framework and some examples. Computer 37(4):50–56CrossRefGoogle Scholar
  5. 5.
    Craglia M, Haining R, Wiles P (2000) A comparative evaluation of approaches to urban crime pattern analysis. Urban Stud 37(4):711–729CrossRefGoogle Scholar
  6. 6.
    Cressie NAC (1991) Statistics for spatial data. Wiley Series in Probability and Statistics, New YorkGoogle Scholar
  7. 7.
    Cristofor L (2002) ARtool: association rule mining algorithms and tools.
  8. 8.
    Dent BD (1999) Cartography: thematic map design. WCB McGraw Hill, BostonGoogle Scholar
  9. 9.
    Estivill-Castro V, Lee I (2001) Data mining techniques for autonomous exploration of large volumes of geo-referenced crime data. In: Pullar DV (ed) Proceedings of the 6th international conference on geocomputation, Brisbane, Australia. GeoComputation CD-ROMGoogle Scholar
  10. 10.
    Estivill-Castro V, Lee I (2002) Argument free clustering via boundary extraction for massive point-data sets. Comput Environ Urban Syst 26(4):315–334CrossRefGoogle Scholar
  11. 11.
    Han J, Kamber M, Tung KH (2001) Spatial clustering methods in data mining. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery. Cambridge University Press, Cambridge, UK, pp 188–217CrossRefGoogle Scholar
  12. 12.
    Hirschfield A, Brown P, Todd P (1995) Gis and the analysis of spatially-referenced crime data: experiences in Merseyside UK. J Geogr Inf Syst 9(2):191–210CrossRefGoogle Scholar
  13. 13.
    Huang Y, Pei J, Xiong H (2006) Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3):239–260. doi: 10.1007/s10707-006-9827-8 CrossRefGoogle Scholar
  14. 14.
    Huang Y, Shekhar S, Xiong H (2004) Discovering co-location patterns from spatial datasets: a general approach. IEEE Trans Knowl Data Eng 16(12):1472–1485CrossRefGoogle Scholar
  15. 15.
    Koperski K, Han J (1995) Discovery of spatial association rules in geographic information databases. In: Proceedings of the 4th international symposium on large spatial databases. LNCS. Springer, Portland, Maine, pp 47–66Google Scholar
  16. 16.
    Lee I, Phillips P (2008) Urban crime analysis through areal categorized multivariate associations mining. Appl Artif Intell 22(5):483–499CrossRefGoogle Scholar
  17. 17.
    Lee S (2001) Developing a bivariate spatial association measure: an integration of Pearson’s r and Moran’s I. J Geogr Syst 3(4):369–385CrossRefGoogle Scholar
  18. 18.
    Mennis J, Liu JW (2005) Mining association rules in spatio-temporal data: an analysis of urban socioeconomic and land cover change. Trans GIS 9(1):5–17. doi: 10.1111/j.1467-9671.2005.00202.x. URL: CrossRefGoogle Scholar
  19. 19.
    Miller HJ, Han J (2001) Geographic data mining and knowledge discovery. Taylor and Francis, LondonCrossRefGoogle Scholar
  20. 20.
    Murray AT, McGuffog I, Western JS, Mullins, P (2001) Exploratory spatial data analysis techniques for examining urban crime. Br J Criminol 41:309–329CrossRefGoogle Scholar
  21. 21.
    Oatley G, Ewart B, Zeleznikow J (2006) Decision support systems for police: lessons from the application of data mining techniques to soft forensic evidence. Artif Intell Law 14(1):35–100. doi: 10.1007/s10506-006-9023-z CrossRefGoogle Scholar
  22. 22.
    Okabe A, Boots BN, Sugihara K, Chiu SN (2000) Spatial tessellations: concepts and applications of voronoi diagrams, 2nd edn. Wiley, West SussexGoogle Scholar
  23. 23.
    Pelekis N, Kopanakis I, Marketos G, Ntoutsi I, Andrienko G, Theodoridis Y (2007) Similarity search in trajectory databases. In: TIME ’07: proceedings of the 14th international symposium on temporal representation and reasoning. IEEE Computer Society, Washington, DC, USA, pp 129–140. doi: 10.1109/TIME.2007.59 CrossRefGoogle Scholar
  24. 24.
    Ratcliffe J (2004) The hotspot matrix: a framework for the spatio-temporal targeting of crime reduction. In: Police practice and research, vol 5, pp 5–23Google Scholar
  25. 25.
    Ratcliffe J, McCullagh M (1998) Identifying repeat victimization with Gis. Br J Criminol 38(4):651–662CrossRefGoogle Scholar
  26. 26.
    Rigaux P, Scholl M, Voisard A (2001) Spatial databases: with application to GIS. Morgan Kaufmann, San Francisco, CAGoogle Scholar
  27. 27.
    Samet H (2005) Foundations of multidimensional and metric data structures (the Morgan Kaufmann series in computer graphics and geometric modeling). Morgan Kaufmann, San Francisco, CA, USAGoogle Scholar
  28. 28.
    Shalabi LA, Shaaban Z, Kasasbeh B (2006) Data mining: a preprocessing engine. J Comput Sci 2:735–739CrossRefGoogle Scholar
  29. 29.
    Shekhar S, Huang Y (2001) Discovering spatial co-location patterns: a summary of results. In: Jensen CS, Schneider M, Seeger VJ, Tsotras B (eds) Proceedings of the 7th international symposium on the advances in spatial and temporal databases. Lecture notes in computer science, vol 2121. Springer, Redondo Beach, CA, pp 236–256CrossRefGoogle Scholar
  30. 30.
    Tobler W (1979) Cellular geography. Philos Geogr, pp 379–386Google Scholar
  31. 31.
    Voudouris C (1997) Guided local search for combinatorial optimisation problems. PhD thesis, Department of Computer Science, University of Essex, Colchester, UKGoogle Scholar
  32. 32.
    Voudouris C, Tsang E (2003) Handbook of metaheuristics, chap Guided Local Search. Springer, pp 185–218Google Scholar
  33. 33.
    Wortley R, Mazerolle L (2008) Environmental criminology and crime analysis. Willan PublishingGoogle Scholar
  34. 34.
    Yoo JS, Shekhar S (2006) A joinless approach for mining spatial colocation patterns. IEEE Trans Knowl Data Eng 18(10):1323–1337. doi: 10.1109/TKDE.2006.150 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Business, Discipline of ITJames Cook UniversityTownsvilleAustralia

Personalised recommendations