Advertisement

The Spatial Structure of Crime in Urban Environments

  • Sarah White
  • Tobin Yehle
  • Hugo Serrano
  • Marcos Oliveira
  • Ronaldo MenezesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8852)

Abstract

It is undoubtedly cliché to say that we are in the Age of Big Data Analytics or Data Science; every computing and IT publication you find talks about Big Data and companies no longer are interested in software engineers and analysts but instead they are looking for Data Scientists! In spite of the excessive use of the term, the truth of the matter is that data has never been more available and the increase in computation power allows for more sophisticated tools to identify patterns in the data and on the networks that governs these systems (complex networks). Crime is not different, the open data phenomena has spread to thousand of cities in the world, which are making data about crime activity available for any citizen to look at. Furthermore, new criminology studies argue that criminals typically commit crimes in areas in which they are familiar, usually close to home. Using this information we propose a new model based on networks to build links between crimes in close physical proximity. We show that the structure of the criminal activity can be partially represented by this spatial network of sites. In this paper we describe this process and the analysis of the networks we have constructed to find patterns in the underlying structure of criminal activity.

Keywords

Network science Crime mapping Social disorganization Routine activity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shaw, C.R., McKay, H.D.: Juvenile delinquency and urban areas (Revision of 1942 ed.). University of Chicago Press (1969)Google Scholar
  2. 2.
    Shaw, C.R., Zorbaugh, F.M., McKay, H.D., Cottrell, L.S.: Delinquency areas. University of Chicago Press (1929)Google Scholar
  3. 3.
    Taylor, R.B., Gottfredson, S.D., Brower, S.: Block crime and fear: Defensible space, local social ties, and territorial functioning. Journal of Research in crime and delinquency 21(4), 303–331 (1984)CrossRefGoogle Scholar
  4. 4.
    Sampson, R.J., Raudenbush, S.W., Earls, F.: Neighborhoods and violent crime: A multilevel study of collective efficacy. Science 277(5328), 918–924 (1997)CrossRefGoogle Scholar
  5. 5.
    Bursik Jr., R.J., Grasmick, H.G., et al.: Neighborhoods & Crime. Lexington Books (1999)Google Scholar
  6. 6.
    Morenoff, J.D., Sampson, R.J., Raudenbush, S.W.: Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence*. Criminology 39(3), 517–558 (2001)CrossRefGoogle Scholar
  7. 7.
    Jobes, P.C., Barclay, E., Weinand, H., Donnermeyer, J.F.: A structural analysis of social disorganisation and crime in rural communities in australia. Australian & New Zealand Journal of Criminology 37(1), 114–140 (2004)CrossRefGoogle Scholar
  8. 8.
    Eck, J.E., Chainey, S., Cameron, J.G., Leitner, M., Wilson, R.E.: Mapping Crime: Understanding Hot Spots. National Institute of Justice (2005)Google Scholar
  9. 9.
    Furtado, V., Melo, A., Coelho, A.L.V., Menezes, R.: Simulating crime against properties using swarm intelligence and social networks. In: Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, pp. 300–318 (2008)Google Scholar
  10. 10.
    Kubrin, C.E., Weitzer, R.: New directions in social disorganization theory. Journal of Research in Crime and Delinquency 40(4), 374–402 (2003)CrossRefGoogle Scholar
  11. 11.
    Levine, N., Lee, P.: Journey-to-crime by gender and age group in manchester, england. In: Crime modeling and mapping using geospatial technologies, pp. 145–178. Springer (2013)Google Scholar
  12. 12.
    Jefferis, E.: A multi-method exploration of crime hot spots: a summary of findings. US Department of Justice, National Institute of Justice, Crime Mapping Research Center, Washington, DC (1999)Google Scholar
  13. 13.
    Sparrow, M.K.: The application of network analysis to criminal intelligence: An assessment of the prospects. Social networks 13(3), 251–274 (1991)CrossRefMathSciNetGoogle Scholar
  14. 14.
    McIllwain, J.S.: Organized crime: A social network approach. Crime, Law and Social Change 32(4), 301–323 (1999)CrossRefGoogle Scholar
  15. 15.
    Klerks, P.: The network paradigm applied to criminal organisations. Transnational organised crime: perspectives on global security, p. 97 (2003)Google Scholar
  16. 16.
    Thiemann, C., Theis, F., Grady, D., Brune, R., Brockmann, D.: The structure of borders in a small world, November 2010Google Scholar
  17. 17.
    Beavon, D.J.K., Brantingham, P.L., Brantingham, P.J.: The Influence of Street Networks on the Patterning of Property Offenses, Crime prevention studies (1994)Google Scholar
  18. 18.
    Willits, D., Broidy, L., Gonzales, A., Denman, K.: Place and Neighborhood Crime: Examining the Relationship between Schools, Churches, and Alcohol Related Establishments and Crime. Institute for Social Research, March 2011Google Scholar
  19. 19.
    Foster, S., Wood, L., Christian, H., Knuiman, M., Giles-Corti, B.: Planning safer suburbs: Do changes in the built environment influence residents’ perceptions of crime risk? Social Science & Medicine 97, 87–94 (2013)CrossRefGoogle Scholar
  20. 20.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sarah White
    • 1
  • Tobin Yehle
    • 2
  • Hugo Serrano
    • 3
  • Marcos Oliveira
    • 3
  • Ronaldo Menezes
    • 3
    Email author
  1. 1.College of Arts and SciencesUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.School of ComputingUniversity of UtahSalt Lake CityUSA
  3. 3.BioComplex LaboratoryFlorida Institute of TechnologyMelbourneUSA

Personalised recommendations