Towards Simulating Criminal Offender Movement Based on Insights from Human Dynamics and Location-Based Social Networks

  • Raquel Rosés BrünggerEmail author
  • Robin Bader
  • Cristina Kadar
  • Irena Pletikosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate offender behavior in a geographical environment, relying exclusively on a small sample of offender homes and crime locations. The complex dynamics of crime and the lack of information on criminal offender’s movement patterns challenge the design of offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate offender movement. We study 9 offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Lévy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.


Crime Simulation ABM LSBN Offender mobility Human mobility patterns 


  1. 1.
    Gerritsen, C., Elffers, H.: Investigating prevention by simulation methods. In: LeClerc, B., Savona, E.U. (eds.) Crime Prevention in the 21st Century. Insightful Approaches for Crime Prevention Initiatives. Springer, Cham, Switzerland (2017)Google Scholar
  2. 2.
    Brantingham, P.J., Tita, G.: Offender mobility and crime pattern formation from first principles. In: Liu, L., Eck, J. (eds.) Artificial Crime Analysis Systems. Using Computer Simulations and Geographic Information Systems, Information Science Reference, Hershey, N.Y., London (2008)Google Scholar
  3. 3.
    Birks, D.J., Townsley, M., Stewart, A.: Emergent regularities of interpersonal victimization. an agent-based investigation. J. Res. Crime Delinquency 51(1), 119–140 (2014)CrossRefGoogle Scholar
  4. 4.
    Dray, A., Mazerolle, L., Perez, P., Ritter, A.: Policing Australia’s ‘heroin drought’. using an agent-based model to simulate alternative outcomes. J. Experimental Criminol. 4(3), 267–287 (2008)CrossRefGoogle Scholar
  5. 5.
    Devia, N., Weber, R.: Generating crime data using agent-based simulation. Comput. Environ. Urban Syst. 42, 26–41 (2013)CrossRefGoogle Scholar
  6. 6.
    Hayslett-McCall, K.L., Qiu, F., Curtin, K.M., Chastain, B., Schubert, J., Carver, V.: The simulation of the journey to residential burglary. In: Liu, L., Eck, J. (eds.) Artificial Crime Analysis Systems. Using Computer Simulations and Geographic Information Systems. Information Science Reference, Hershey, N.Y., London, pp. 281–299 (2008)Google Scholar
  7. 7.
    Gunderson, L., Brown, D.: Using a multi-agent model to predict both physical and cyber criminal activity. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions, vol. 4, Piscataway, pp. 2338–2343 (2000)Google Scholar
  8. 8.
    Peng, C., Kurland, J.: The agent-based spatial simulation to the burglary in Beijing. In: Hutchison, D. et al. (eds.) Computational Science and Its Applications – ICCSA 2014. Lecture Notes in Computer Science, LNCS. Springer, Cham, pp. 31–43 (2014)Google Scholar
  9. 9.
    Malleson, N., Evans, A., Jenkins, T.: An agent-based model of burglary. Environ. Planning B Planning Des. 36(6), 1103–1123 (2009)CrossRefGoogle Scholar
  10. 10.
    Malleson, N., See, L., Evans, A., Heppenstall, A.: Implementing comprehensive offender behaviour in a realistic agent-based model of burglary. Simulation 88(1), 50–71 (2012)CrossRefGoogle Scholar
  11. 11.
    Cohen, L.E., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44(4), 588–608 (1979)CrossRefGoogle Scholar
  12. 12.
    Reid, A.A., Frank, R., Iwanski, N., Dabbaghian, V., Brantingham, P.: Uncovering the spatial patterning of crimes. a criminal movement model (CriMM). J. Res. Crime Delinquency 51(2), 230–255 (2014)CrossRefGoogle Scholar
  13. 13.
    Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439(7075), 462–465 (2006)CrossRefGoogle Scholar
  14. 14.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  15. 15.
    Song, C., Qu, Z., Blumm, N., Barabasi, A.-L.: Limits of predictability in human mobility. Science (New York, N.Y.) 327(5968), 1018–1021 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., Newth, D.: Understanding human mobility from Twitter. PLoS ONE 10(7), e0131469 (2015)CrossRefGoogle Scholar
  17. 17.
    Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., Mascolo, C.: A tale of many cities: universal patterns in human urban mobility. PLoS ONE 7(5), e37027 (2012)CrossRefGoogle Scholar
  18. 18.
    Tappan, P.W.: Who is the Criminal? Am. Sociol. Rev. 12(1), 96–102 (1947)CrossRefGoogle Scholar
  19. 19.
    Calabrese, F., Di Lorenzo, G., Ratti, C.: Human mobility prediction based on individual and collective geographical preferences. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2010), pp. 312–317 (2010)Google Scholar
  20. 20.
    Kadar, C., Iria, J., Pletikosa Cvijikj, I.: Exploring Foursquare-derived features for crime prediction in New York City (2016)Google Scholar
  21. 21.
    Chainey, S., Tompson, L., Uhlig, S.: The utility of hotspot mapping for predicting spatial patterns of crime. Secur. J. 21(1–2), 4–28 (2008)CrossRefGoogle Scholar
  22. 22.
    New York State Department of Transportation. A Transportation Profile Of New York State (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Raquel Rosés Brüngger
    • 1
    Email author
  • Robin Bader
    • 1
  • Cristina Kadar
    • 1
  • Irena Pletikosa
    • 1
  1. 1.Information Management Chair, D-MTECETH ZurichZürichSwitzerland

Personalised recommendations