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Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars

  • Nick Malleson
  • Linda See
  • Andrew Evans
  • Alison Heppenstall
Part of the Intelligent Systems Reference Library book series (ISRL, volume 52)

Abstract

Agent-based methods are one approach for modelling complex social systems but one issue with these models is the large number of parameters that require estimation. This chapter examines the effect of using a genetic algorithm (GA) for the parameter estimation of an agent-based model (ABM) of burglary. One of the main issues encountered in the implementation was the computation time required to run the algorithm. Nevertheless a set of preliminary results were obtained, which indicated that visibility is the most important parameter in the decision of whether to burgle a house while accessibility was the least important. Such tools may eventually provide the means to gain a greater understanding of the factors that determine criminological behaviour.

Keywords

Genetic Algorithm Collective Efficacy Output Area Crime Data Routine Activity Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Andresen, M., Malleson, N.: Testing the stability of crime patterns: implications for theory and policy. J. Res. Crime Delinquency 48(1), 58–82 (2011)CrossRefGoogle Scholar
  2. 2.
    Bäck, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993)CrossRefGoogle Scholar
  3. 3.
    Beasley, D., Bull, D.R., Martin, R.R.: An overview of genetic algortihms: Part 1. Fundamentals. Univ. Comput. 15(2), 58–69 (1993)Google Scholar
  4. 4.
    Bhavnani, R., Miodownik, D., Nart, J.: REsCape: an agent-based framework for modeling resources, ethnicity, and conflict. J. Artif. Soc. Soc. Simul. 11(2), 7 (2008) [Online]. http://jasss.soc.surrey.ac.uk/11/2/7.html. Accessed 16 June 2011
  5. 5.
    Birks, D.: Computational criminology: a multi-agent simulation of volume crime activity. Presentation to the British Society of Criminology Conference, University of Leeds, UK (2005)Google Scholar
  6. 6.
    Birks, D.: Synthesis over analysis: using agent-based models to examine the interactions of crime. Presentation at the Fifth National Crime Mapping Conference, London (2007)Google Scholar
  7. 7.
    Birks, D.J., Donkin, S., Wellsmith, M.: Synthesis over analysis: towards an ontology for volume crime simulation. In: Liu, L., Eck, J. (eds.) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, pp. 160–192. Information Science Reference, Hershey (2008)CrossRefGoogle Scholar
  8. 8.
    Birks, D., Townsley, M., Stewart, A.: Generative explanations of crime: using simulation to test criminological theory. Criminology 50(1), 221–254 (2012)CrossRefGoogle Scholar
  9. 9.
    Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Nat. Acad. Sci. 99, 7280–7287 (2002)CrossRefGoogle Scholar
  10. 10.
    Brantingham, P.J., Brantingham, P.L.: Notes on the geometry of crime. In: Brantingham, P.J., Brantingham, P.L. (eds.) Environmental Criminology (pp. 27–54). Waveland Press, Prospect Heights (1981)Google Scholar
  11. 11.
    Brantingham, P.L., Brantingham, P.J.: Nodes, paths and edges: considerations on the complexity of crime and the physical environment. J. Environm. Psychol. 13(1), 3–28 (1993)CrossRefGoogle Scholar
  12. 12.
    Brantingham, P.L., Brantingham, P.J.: Mapping crime for analytic purposes: location quotients, counts, and rates. In: Weisburd, D., McEwen, T. (eds.) Crime Mapping and Crime Prevention, Volume 8 of Crime Prevention Studies, pp. 263–288. Criminal Justice Press, Monsey (1998)Google Scholar
  13. 13.
    Brantingham, P.L., Brantingham, P.J.: Computer simulation as a tool for environmental criminologists. Secur. J. 17(1), 21–30 (2004)CrossRefGoogle Scholar
  14. 14.
    Brantingham, P.L., Glasser, U., Kinney, B., Singh, K., Vajihollahi, M.: A computational model for simulating spatial aspects of crime in urban environments. Proceedings of the 2005 IEEE International Conference on Systems Man and Cybernetics 4, 3667–3674 (2005)CrossRefGoogle Scholar
  15. 15.
    Brantingham, P.L., Glasser, U., Kinney, B., Singh, K., Vajihollahi, M.: Modeling urban crime patterns: viewing multi-agent systems as abstract state machines. Proceedings of the 12th International Workshop on Abstract State Machines, pp. 101–117, Paris (2005)Google Scholar
  16. 16.
    Brantingham, P.L., Glasser, U., Jackson, P., Kinney, B., Vajihollahi, M.: Mastermind: computational modeling and simulation of spatiotemporal aspects of crime in urban environments. In: Liu, L., Eck, J., (eds.) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, chapter 13, pp. 252–280. IGI Global, Hershey (2008)Google Scholar
  17. 17.
    Chainey, S., Ratcliffe, J.: GIS and Crime Mapping, 1st edn. Wiley, Chichester (2005)CrossRefGoogle Scholar
  18. 18.
    Cherif, A., Yoshioka, H., Ni, W., Bose, P.: Terrorism: Mechanisms of Radicalization Processes, Control of Contagion and Counter-Terrorist Measures. Santa Fe Institute Working Paper [Online] (2009). http://tuvalu.santafe.edu/events/workshops/images/7/7e/TerrorismWorkingPaper.pdf. Accessed 16 June 2011
  19. 19.
    Cilliers, P.: Complexity and Postmodernism. Routledge, Bury St Edmonds (1998)Google Scholar
  20. 20.
    Clarke, R.V., Cornish, D.B.: Modeling offenders’ decisions: a framework for research and policy. Crime Justice 6, 147–185 (1985)CrossRefGoogle Scholar
  21. 21.
    Cohen, L., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44, 588–608 (1979)CrossRefGoogle Scholar
  22. 22.
    Crooks, A.: Constructing and implementing an agent-based model of residential segregation through vector GIS. CASA Working Paper Series, Paper 133 (2008). http://eprints.ucl.ac.uk/15185/1/15185.pdf
  23. 23.
    Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
  24. 24.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, West Sussex (2001)zbMATHGoogle Scholar
  25. 25.
    Di Paolo, E.A., Noble, J., Bullock, S.: Simulation models as opaque thought experiments. Seventh International Conference on Artificial Life, pp. 497–506. MIT Press, Cambridge (2000)Google Scholar
  26. 26.
    Dray, A., Mazerolle, L., Perez1, P., Ritter, A.: Policing Australia’s heroin drought: using an agent-based model to simulate alternative outcomes. J. Exp. Criminol. 4, 267–287 (2008)Google Scholar
  27. 27.
    Eck, J., Weisburd, D.: Crime places in crime theory. In: Eck, J., Weisburd, D. (eds.) Crime and Place, pp. 1–33. Criminal Justice Press, Monsey (1995)Google Scholar
  28. 28.
    Eck, J.E., Liu, L.: Contrasting simulated and empirical experiments in crime prevention. J. Exp. Criminol. 4(3), 195–213 (2008)CrossRefGoogle Scholar
  29. 29.
    Egesdal, M., Fathauer, C., Louie, K., Neuman, J., Mohler, G., Lewis, E.: Statistical and Stochastic Modeling of Gang Rivalries in Los Angeles. SIAM Undergraduate Research Online (SIURO) 3. [Onine] (2010). http://www.siam.org/students/siuro/vol3/S01045.pdf. Accessed 16 June 2011
  30. 30.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)Google Scholar
  31. 31.
    Efstratiadis, A., Koutsoyiannis, D.: On the practical use of multiobjective optimisation in hydrological model calibration, European Geosciences Union General Assembly 2009, Geophysical Research Abstracts, vol. 11. European Geosciences Union, Vienna (2009)Google Scholar
  32. 32.
    Epstein, J.M., Axtell, R.L.: Growing Artificial Societies: Social Science from the Bottom Up. MIT Press, Cambridge (1996)Google Scholar
  33. 33.
    Evans, A.J.: A sketchbook for ethics in agent-based modelling. Association of American Geographers (AAG) Annual Meeting, 23–26 February 2012, New York [online] (2012). http://www.geog.leeds.ac.uk/presentations/12-2/12-2.pptx
  34. 34.
    Expósito, R.R., Taboada, G.L., Ramos, S., Touriño, J., Doallo, R.: Performance analysis of HPC applications in the cloud. Future Gener. Comput. Syst. 29(1), 218–229 (2013). doi: 10.1016/j.future.2012.06.009 CrossRefGoogle Scholar
  35. 35.
    Farmer, J.D., Foley, D.: The economy needs agent-based modelling. Nature 460, 685–686 (2009)CrossRefGoogle Scholar
  36. 36.
    Grimm, V., Railsback, S.F.: Individual-Based Modeling and Ecology. Princeton University Press, Princeton (2005)zbMATHGoogle Scholar
  37. 37.
    Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S., Huse, G., Huth, A., Jepsen, J.U., Jørgensen, C., Mooij, W.M., Müller, B., Pe’er, G., C., Piou, Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Rüger, N., Strand, E., Souissi, S., Stillman, R.A., Vabø, R., Visser, U., DeAngelis, D.L.: A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198, 115–126 (2006)Google Scholar
  38. 38.
    Groff, E.: Exploring The Geography Of Routine Activity Theory: A Spatio-Temporal Test Using Street Robbery. PhD thesis, University of Maryland (2006)Google Scholar
  39. 39.
    Groff, E.: Simulation for theory testing and experimentation: An example using routine activity theory and street robbery. Journal of Quantitative Criminology, 23:75–103 (2007a)Google Scholar
  40. 40.
    Groff, E.: Situating simulation to model human spatio-temporal interactions: An example using crime events. Transactions in GIS, 11(4):507–530 (2007b)Google Scholar
  41. 41.
    Groff, E., Mazerolle, L. (2008) Simulated experiments and their potential role in criminology and criminal justice. Journal of Experimental Criminology, 4(3):187–193Google Scholar
  42. 42.
    Goldberg, D.: Genetic Algorithms: in Search, Optimisation and Machine Learning. Addison Wesley, Crawfordsville (1989)Google Scholar
  43. 43.
    Goldstein, N.C.: Brains versus brawn—comparative strategies for the calibration of a cellular automata-based urban growth model. In: Atkinson, P., Foody, G., Darby, S., Wu, F. (eds.) Geodynamics, pp. 249–272. CRC Press, Boca Raton (2004)CrossRefGoogle Scholar
  44. 44.
    Hayslett-McCall, K. L., Qiu, F., Curtin, K. M., Chastain, B., Schubert, J., Carver, V.: The simulation of the journey to residential burglary. In: Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, chapter 14. IGI Global, Hershey (2008)Google Scholar
  45. 45.
    Heppenstall, A.J., Evans, A.J., Birkin, M.H.: Genetic algorithm optimisation of an agent-based model for simulating a retail market. Environ. Plan. B: Plan. Design 34, 1051–1070 (2007)Google Scholar
  46. 46.
    Holland, J.: Adaption in Natural and Artificial Systems. MIT Press, Cambridge (1975)zbMATHGoogle Scholar
  47. 47.
    Holland, J.: Genetic algorithms. Scientific American, July 1992, 66–72Google Scholar
  48. 48.
    Huddleston, S.H., Learmonth, G.P., Fox, J.: Changing Knives into Spoons. Proceedings of the 2008 IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, VA, USA, April 25 (2008) [Online]. http://www.sys.virginia.edu/sieds09/papers/0047_FPM2SimDM-02.pdf. Accessed 16 June 2011
  49. 49.
    Johnson, S.D., Bernasco, W., Bowers, K.J., Elffers, H., Ratcliffe, J., Rengert, G.F., Townsley, M.: Near repeats: a cross national assessment of residential burglary. J. Quant. Criminol. 23(3), 201–219 (2007)CrossRefGoogle Scholar
  50. 50.
    Johnson, S.D., Bowers, K.J.: Permeability and burglary risk: are Cul-de-sacs safer? J. Quant. Criminol. 26(1), 89–111 (2009)CrossRefGoogle Scholar
  51. 51.
    Knudsen, D.C., Fotheringham, A.S.: Matrix comparison, goodness-of-fit, and spatial interaction modeling. Int. Reg. Sci. Rev. 10, 127–147 (1986)CrossRefGoogle Scholar
  52. 52.
    Kongmuang, C.: Modelling crime: a spatial microsimulation approach. Ph.D. thesis, School of Geography, University of Leeds, Leeds (2006)Google Scholar
  53. 53.
    Li, X., Yang, Q.S., Liu, X.-P.: Genetic algorithms for determining the parameters of cellular automata in urban simulation. Sci. China Ser. D: Earth Sci. 50(12), 1857–1866 (2007)MathSciNetCrossRefGoogle Scholar
  54. 54.
    Liu, L., Wang, X., Eck, J., Liang, J.: Simulating crime events and crime patterns in a RA/CA models. In: Wang, F. (ed.) Geographic Information Systems and Crime Analysis, pp. 197–213. Idea Publishing, Reading (2005)Google Scholar
  55. 55.
    Lustick, I.S.: Defining Violence: a plausibility probe using agent-based modeling. Paper prepared for LiCEP, Princeton University, May 12–14, 2006 [Online]. http://www.prio.no/files/file48070_lustick_violdef_foroslo_v2.pdf. Accessed 16 June 2011
  56. 56.
    Malleson, N.: An agent-based model of burglary in Leeds. Master’s thesis, University of Leeds, School of Computing, Leeds [Online] (2006). http://www.geog.leeds.ac.uk/fileadmin/downloads/school/people/postgrads/n.malleson/mscproj.pdf. Accessed 16 June 2011
  57. 57.
    Malleson, N.: Agent-based modelling of burglary. Ph.D. thesis, School of Geography, University of Leeds (2010)Google Scholar
  58. 58.
    Malleson, N., Evans, A.J., Jenkins, T.: An agent-based model of burglary. Environ. Plan. B: Plan. Des. 36, 1103–1123 (2009)CrossRefGoogle Scholar
  59. 59.
    Malleson, N., Heppenstall, A.J., Evans, A.J., See, L.M.: Evaluating an agent-based model of burglary. Working paper 10/1, School of Geography, University of Leeds, UK. January 2010 [Online]. http://www.geog.leeds.ac.uk/fileadmin/downloads/school/research/wpapers/10_1.pdf. Accessed 16 June 2011
  60. 60.
    Malleson, N., Heppenstall, A.J., See, L.M.: Crime reduction through simulation: an agent-based model of burglary. Comput. Environ. Urban Syst. 34, 236–250 (2010b)CrossRefGoogle Scholar
  61. 61.
    Malleson, N., See, L.M., Evans, A.J., Heppenstall, A.J.: Implementing comprehensive offender behaviour in a realistic agent-based model of burglary. Simul.: Trans. Soc. Model. Simul. Int. 88(1), 50–71 (2010)Google Scholar
  62. 62.
    Malleson, N., Evans, A.J., Heppenstall, A.J., See, L.M.: Crime from the ground-up: agent-based models of burglary. Geographical Compass (Submitted)Google Scholar
  63. 63.
    Malleson, N., Evans, A., Heppenstall, A., See, L..: The Leeds Burglary Simulator. Informatica e diritto special issue: Law and Computational Social Science 1 211–222 (2013)Google Scholar
  64. 64.
    Maros, I., Mitra, G.: Simplex algorithms. In: Beasley, J.E. (ed.) Advances in Linear and Integer Programming, pp. 1–46. Oxford Science, Oxford (1996)Google Scholar
  65. 65.
    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, Lecture Notes in Computer Science, vol. 3891, pp. 99–114. Springer, Heidelberg (2005)Google Scholar
  66. 66.
    Michalewicz, M.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)Google Scholar
  67. 67.
    Michalewicz, Z., Janikow, C.: Genetic algorithms for numerical. Optim. Stat. Comput. 1(2), 75–91 (1991)CrossRefGoogle Scholar
  68. 68.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)Google Scholar
  69. 69.
    Moss, S., Edmonds, B.: Towards good social science. J. Artif. Soc. Soc. Simul. 8(4), 13 (2005)Google Scholar
  70. 70.
    Omer, I.: How ethnicity influences residential distributions: an agent-based simulation. Environ. Plan. B: Plan. Des. 32(5), 657–672 (2005)CrossRefGoogle Scholar
  71. 71.
    Rengert, G.F., Wasilchick, J.: Suburban burglary: a time and a place for everything. Charles Thomas, Springfield (1985)Google Scholar
  72. 72.
    Schmidt, B.: The Modelling of Human Behaviour. SCS Publications, Erlangen (2000)Google Scholar
  73. 73.
    Schmidt, B.: How to give agents a personality. Proceedings of the 3rd Workshop on Agent- Based Simulation, April 7–9, Passau, Germany (2002)Google Scholar
  74. 74.
    Schelling, T.C.: Dynamic models of segregation. J. Math. Sociol. 1, 143–186 (1971)CrossRefGoogle Scholar
  75. 75.
    Shan, J., Alkheder, S., Wang, J.: Genetic algorithms for the calibration of cellular automata urban growth modeling. Photogram. Eng. Remote Sens. 74(10), 1267–1277 (2008)CrossRefGoogle Scholar
  76. 76.
    Shaw, C., McKay, H.: Juvenile Delinquency and Urban Areas. University of Chicago Press, Chicago (1942)Google Scholar
  77. 77.
    Stonedahl, F.J.: Genetic algorithms for the exploration of parameter spaces of agent-based models. Unpublished Ph.D. thesis, Northwestern University, Evanston (2011). http://forrest.stonedahl.com/thesis/forrest_stonedahl_thesis.pdf
  78. 78.
    Tesfatsion, L., Judd, K.L.: Handbook of Computational Economics: Agent-Based Computational Economics. North Holland, Amsterdam (2006)Google Scholar
  79. 79.
    Urban, C.: PECS: a reference model for the simulation of multi-agent systems. In: Suleiman, R., Troitzsch, K.G., Gilbert, N. (eds.) Tools and Techniques for Social Science Simulation, Chapter 6, pp. 83–114. Physica, Heidelberg (2000)Google Scholar
  80. 80.
    van Baal, P.: Computer Simulations of Criminal Deterrence: From Public Policy to Local Interaction to Individual Behaviour. Boom Juridische Uitgevers, Den Haag, The Netherlands (2004)Google Scholar
  81. 81.
    Vickers, D., Rees, P.: Creating the UK national statistics 2001 output area classification. J. Royal Stat. Soc. Ser. A 170(2), 379–403 (2007)MathSciNetCrossRefGoogle Scholar
  82. 82.
    Vrugt, J.A., Gupta, H.V., Bastidas, L.A., Bouten, W., Sorooshian, S.: Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour. Res. 39(8), 1214 (2003). doi: 10.1029/2002WR001746 Google Scholar
  83. 83.
    Wang, X., Liu, L., Eck, J.: Crime simulation using gis and artificial intelligent agents. In: Liu, L., Eck, J. (eds.) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, chapter 11. Information Science Reference, Hershey (2008)Google Scholar
  84. 84.
    Weisburd, D., Bushway, S., Lum, C., Ang, S.-M.: Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. Criminology 42(1), 283–321 (2004)CrossRefGoogle Scholar
  85. 85.
    Weisburd, D., Bruinsma, G.J.N., Bernasco, W.: Units of analysis in geographic criminology: historical development, critical issues, and open questions. In: Weisburd, D., Bernasco, W., Bruinsma, G.J.N. (eds.) Putting Crime in Its Place. Units of Analysis in Geographic Criminology (pp. 3–31). Springer, Heidelberg (2009)Google Scholar
  86. 86.
    Wiese, T.: Global Optimization Algorithms—Theory and Applications, 2nd edn. University of Kassel, Distributed Systems Group (2009). http://www.it-weise.de
  87. 87.
    Winoto, P.: A simulation of the market for offenses in multiagent systems: is zero crime rates attainable? In: Sichman, J.S., Bousquet, F., Davidsson, P., (eds.) MABS, Lecture Notes in Computer Science, vol. 2581, pp. 181–193. Springer, Heidelberg (2003)Google Scholar
  88. 88.
    Xiao, W.: A unified conceptual framework for geographical optimization using evolutionary algorithms. Ann. Assoc. Am. Geogr. 98(4), 795–817 (2008)CrossRefGoogle Scholar
  89. 89.
    Yapo, P.O., Gupta, H.V., Sorooshian, S.: Multi-objective global optimization for hydrological models. J. Hydrol. 204, 83–97 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Nick Malleson
    • 1
  • Linda See
    • 2
    • 3
  • Andrew Evans
    • 1
  • Alison Heppenstall
    • 1
  1. 1.School of GeographyUniversity of LeedsLeedsUK
  2. 2.International Institute of Applied Systems AnalysisLaxenburgAustria
  3. 3.Centre for Applied Spatial AnalysisUniversity College London (UCL)LondonUK

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