Towards Understanding the Impact of Crime on the Choice of Route by a Bus Passenger

  • Daniel Sullivan
  • Carlos Caminha
  • Hygor P. M. Melo
  • Vasco Furtado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)


In this paper we describe a simulation platform that supports studies on the impact of crime on urban mobility. We present an example of how this can be achieved by seeking to understand the effect, on the transport system, if users of this system decide to choose optimal routes of time between origins and destinations that they normally follow. Based on real data from a large Brazilian metropolis, we found that the percentage of users who follow this policy is small. Most prefer to follow less efficient routes by making bus exchanges at terminals. This can be understood as an indication that the users of the transport system favor the security factor.


Crime behavior Human mobility 


  1. 1.
    Axtell, R., Epstein, J.M., Dean, J.S., Gumerman, G.J., Swedlund, A.C., Harburger, J., et al.: Population growth and collapse in a multiagent model of the Kayenta Anasazi in long house valley. Proc. Natl. Acad. Sci. U.S.A. (PNAS) 99(3), 7275–7279 (2002)CrossRefGoogle Scholar
  2. 2.
    Kohler, T.A., Kresl, J., Van Wes, Q., Carr, E., Wilshusen, R.H.: Be there then: a modeling approach to settlement determinants and spatial efficiency among late ancestral Pueblo populations of the Mesa Verde Region, U.S. Southwest. In: Kohler, T.A., Gumerman, G.J. (eds.) Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes, pp. 145–178. Oxford University Press, Oxford (2000)Google Scholar
  3. 3.
    Yang, Y., Atkinson, P.M.: An integrated ABM and GIS model of infectious disease transmission. In: Batty, S. (ed.) Computers in Urban Planning and Urban Management (CUPUM), London (2005)Google Scholar
  4. 4.
    Kreft, J.U., Booth, G., Wimpenny, W.T.: BacSim, a simulator for individual based modelling of bacterial colony growth. Microbiology 144(12), 3275–3287 (1998)CrossRefGoogle Scholar
  5. 5.
    Axelrod, R., Bennett, S.D.: A landscape theory of aggregation. Br. J. Polit. Sci. 23(2), 211–233 (1993)CrossRefGoogle Scholar
  6. 6.
    Tesfatsion, L.: Agent-based computational economics: a constructive approach to economic theory. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics: Agent-Based Computational Economics, vol. 2, pp. 831–880. North-Holland, Amsterdam (2006)Google Scholar
  7. 7.
    Nagel, K., Rasmussen, S.: Traffic at the edge of chaos. In: Brooks, R. (ed.) Artificial Life IV, pp. 222–236. MIT Press, Cambridge (1994)Google Scholar
  8. 8.
    Cohen, L.E., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44(4), 588–608 (1979). doi: 10.2307/2094589 CrossRefGoogle Scholar
  9. 9.
    Clarke, R.V.G., Felson, M.: Routine Activity and Rational Choice. Advances in Criminological Theory. Transaction Publishers, New Brunswick (1993)Google Scholar
  10. 10.
    Michael, J., Adler, M.J.: Crime, Law and Social Science. International Library of Psychology, Philosophy, and Scientific Method. K. Paul, Trench, Trubner and Co., Ltd. (1933)Google Scholar
  11. 11.
    Felson, M.: Crime and Everyday Life. SAGE Publications, Thousand Oaks (2002)Google Scholar
  12. 12.
    Guedes, R., Furtado, V., Pequeno, T.: Multiagent models for police resource allocation and dispatch. In: 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC), pp. 288–291. IEEE (2014)Google Scholar
  13. 13.
    Wang, T., Rudin, C., Wagner, D., Sevieri, R.: Learning to detect patterns of crime. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS, vol. 8190, pp. 515–530. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40994-3_33 CrossRefGoogle Scholar
  14. 14.
    Kiani, R., Mahdavi, S., Keshavarzi, A.: Analysis and prediction of crimes by clustering and classification 4(8) (2015)Google Scholar
  15. 15.
    Caminha, C., Furtado, V.: Modeling user reports in crowdmaps as a complex network. In: Proceedings of 21st International World Wide Web Conference. Citeseer (2012)Google Scholar
  16. 16.
    Furtado, V., Caminha, C., Ayres, L., Santos, H.: Open government and citizen participation in law enforcement via crowd mapping. IEEE Intell. Syst. 27(4), 63–69 (2012). doi: 10.1109/MIS.2012.80 CrossRefGoogle Scholar
  17. 17.
    Caminha, C., Furtado, V., Pequeno, T.H., Ponte, C., Melo, H.P., Oliveira, E.A., Andrade, J.S.: Human mobility in large cities as a proxy for crime. PLoS ONE 2(12), e0171609 (2017)CrossRefGoogle Scholar
  18. 18.
    Wang, F.: Geographic Information Systems and Crime Analysis. IGI Global, Hershey (2005)CrossRefGoogle Scholar
  19. 19.
    Melo, H.P.M., Moreira, A.A., Batista, E., Makse, H.A., Andrade, J.S.: Statistical signs of social influence on suicides. Sci. Rep. 4 (2014). doi: 10.1038/srep06239 PMID: 25174706
  20. 20.
    Alves, L.G., Ribeiro, H.V., Lenzi, E.K., Mendes, R.S.: Distance to the scaling law: a useful approach for unveiling relationships between crime and urban metrics. PLoS ONE 8(8), e69580 (2013). doi: 10.1371/journal.pone.0069580. PMID: 23940525CrossRefGoogle Scholar
  21. 21.
    Alves, L.G., Ribeiro, H.V., Mendes, R.S.: Scaling laws in the dynamics of crime growth rate. Phys. A: Stat. Mech. Appl. 392(11), 2672–2679 (2013). doi: 10.1016/j.physa.2013.02.002 CrossRefGoogle Scholar
  22. 22.
    D’Orsogna, M.R., Perc, M.: Statistical physics of crime: a review. Phys. Life Rev. 12, 1–21 (2015). doi: 10.1016/j.plrev.2014.11.001. PMID: 25468514CrossRefGoogle Scholar
  23. 23.
    Alves, L., Lenzi, E., Mendes, R., Ribeiro, H.: Spatial correlations, clustering and percolation-like transitions in homicide crimes. EPL (Europhys. Lett.) 111(1), 18002 (2015). doi: 10.1209/0295-5075/111/18002 CrossRefGoogle Scholar
  24. 24.
    Short, M.B., D’Orsogna, M.R., Pasour, V.B., Tita, G.E., Brantingham, P.J., Bertozzi, A.L., et al.: A statistical model of criminal behavior. Math. Models Methods Appl. Sci. 18(Supp. 1), 1249–1267 (2008). doi: 10.1142/S0218202508003029 MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Short, M.B., Brantingham, P.J., Bertozzi, A.L., Tita, G.E.: Dissipation and displacement of hotspots in reaction diffusion models of crime. Proc. Natl. Acad. Sci. 107(9), 3961–3965 (2010). doi: 10.1073/pnas.0910921107 CrossRefGoogle Scholar
  26. 26.
    Birks, D., Townsley, M., Stewart, A.: Generative explanations of crime: using simulation to test criminological theory. Criminology 50(1), 221–254 (2012)CrossRefGoogle Scholar
  27. 27.
    Peng, C., Kurland, J.: The agent-based spatial simulation to the Burglary in Beijing. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8582, pp. 31–43. Springer, Cham (2014). doi: 10.1007/978-3-319-09147-1_3 Google Scholar
  28. 28.
    Melo, A., Belchior, M., Furtado, V.: Analyzing police patrol routes by simulating the physical reorganization of agents. In: Sichman, J.S., Antunes, L. (eds.) MABS 2005. LNCS, vol. 3891, pp. 99–114. Springer, Heidelberg (2006). doi: 10.1007/11734680_8 CrossRefGoogle Scholar
  29. 29.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  30. 30.
    Wang, D., Pedreschi, D., Song, C., Giannotti, F., Barabasi, A.-L.: Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1100–1108. ACM (2011)Google Scholar
  31. 31.
    Caminha, C., Furtado, V., Pinheiro, V., Silva, C.: Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network. In: 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–6. IEEE (2016)Google Scholar
  32. 32.
    Andrade, J.S., Oliveira, E., Moreira, A., Herrmann, H.: Fracturing the optimal paths. Phys. Rev. Lett. 103(22), 225503 (2009)CrossRefGoogle Scholar
  33. 33.
    Ponte, C., Caminha, C., Furtado, V.: Busca de melhor caminho entre dois pontos quando múltiplas origens e múltiplos destinos são possíveis. In: ENIAC (2016)Google Scholar
  34. 34.
    Frith, M., Johnson, S., Fry, H.: The role of the street network in offender spatial decision making. Criminology 55, 344–376 (2017)CrossRefGoogle Scholar
  35. 35.
    Brantingham, P.L., Brantingham, P.J.: Notes on the geometry of crime. In: Environmental Criminology, pp. 27–54 (1981)Google Scholar
  36. 36.
    Caminha, C., Furtado, V.: Towards understanding the impact of human mobility on police allocation (2017). arXiv:1704.07823v1
  37. 37.
    Furtado, V., Caminha, C., Furtado, E., Lopes, A., Dantas, V., Ponte, C., Cavalcante, S.: Increasing the likelihood of finding public transport riders that face problems through a data-driven approach (2017). arXiv:1704.07823v1
  38. 38.
    Cançado, T.M.L.: Alocação e despacho de recursos para combate à criminalidade. Master dissertation, UFMG, Belo Horizonte (2005). (in Portuguese)Google Scholar
  39. 39.
    Zipf, G.K.: Human Behaviour and the Principle of Least-Effort. Addison-Wesley, Cambridge (1949)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel Sullivan
    • 1
  • Carlos Caminha
    • 1
  • Hygor P. M. Melo
    • 2
  • Vasco Furtado
    • 1
  1. 1.UNIFOR – Universidade de FortalezaFortalezaBrazil
  2. 2.IFCE – Instituto Federal de EducaçãoCiência e Tecnologia do CearáAcaraúBrazil

Personalised recommendations