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

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Book cover Social Informatics (SocInfo 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10540))

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Abstract

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.

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Notes

  1. 1.

    Area between usual activity nodes.

  2. 2.

    Foursquare is a mobile application offering location-search-and-discovery for its users: https://foursquare.com/.

  3. 3.

    https://opendata.cityofnewyork.us/.

References

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  5. Devia, N., Weber, R.: Generating crime data using agent-based simulation. Comput. Environ. Urban Syst. 42, 26–41 (2013)

    Article  Google Scholar 

  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. 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. 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. Malleson, N., Evans, A., Jenkins, T.: An agent-based model of burglary. Environ. Planning B Planning Des. 36(6), 1103–1123 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  11. Cohen, L.E., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44(4), 588–608 (1979)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  13. Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439(7075), 462–465 (2006)

    Article  Google Scholar 

  14. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  16. Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., Newth, D.: Understanding human mobility from Twitter. PLoS ONE 10(7), e0131469 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Tappan, P.W.: Who is the Criminal? Am. Sociol. Rev. 12(1), 96–102 (1947)

    Article  Google Scholar 

  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. Kadar, C., Iria, J., Pletikosa Cvijikj, I.: Exploring Foursquare-derived features for crime prediction in New York City (2016)

    Google Scholar 

  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)

    Article  Google Scholar 

  22. New York State Department of Transportation. A Transportation Profile Of New York State (2012)

    Google Scholar 

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Authors and Affiliations

  1. Information Management Chair, D-MTEC, ETH Zurich, Zürich, Switzerland

    Raquel Rosés Brüngger, Robin Bader, Cristina Kadar & Irena Pletikosa

Authors
  1. Raquel Rosés Brüngger
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  2. Robin Bader
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  3. Cristina Kadar
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  4. Irena Pletikosa
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Correspondence to Raquel Rosés Brüngger .

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

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Rosés Brüngger, R., Bader, R., Kadar, C., Pletikosa, I. (2017). Towards Simulating Criminal Offender Movement Based on Insights from Human Dynamics and Location-Based Social Networks. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10540. Springer, Cham. https://doi.org/10.1007/978-3-319-67256-4_36

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  • DOI: https://doi.org/10.1007/978-3-319-67256-4_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67255-7

  • Online ISBN: 978-3-319-67256-4

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