Journal of Quantitative Criminology

, Volume 23, Issue 2, pp 75–103 | Cite as

Simulation for Theory Testing and Experimentation: An Example Using Routine Activity Theory and Street Robbery

Original Paper

Abstract

Achieving a better understanding of the crime event in its spatio-temporal context is an important research area in criminology with major implications for improving policy and developing effective crime prevention strategies. However, significant barriers related to data and methods exist for conducting this type of research. The research requires micro-level data about individual behavior that is difficult to obtain and methods capable of modeling the dynamic, spatio-temporal interaction of offenders, victims, and potential guardians at the micro level. This paper presents simulation modeling as a method for addressing these challenges. Specifically, agent-based modeling, when integrated with geographic information systems, offers the ability to model individual behavior within a real environment. The method is demonstrated by operationalizing and testing routine activity theory as it applies to the crime of street robbery. Model results indicate strong support for the basic premise of routine activity theory; as time spent away from home increases, crime will increase. The strength of the method is in providing a research platform for translating theory into models that can be discussed, shared, tested and enhanced with the goal of building scientific knowledge.

Keywords

Theory testing Simulation Agent-based models Geographic information systems Experiment 

References

  1. Akers RL (2000) Criminological theories: introduction, evaluation, and application. Roxbury Publishing Company, Los AngelesGoogle Scholar
  2. An L, Linderman M, Qi J, Shortridge A, Liu J (2005) Exploring complexity in a human–environment system: an agent-based spatial model for multidisciplinary and multiscale integration. Ann Assoc Am Geogr 95(1):54–79CrossRefGoogle Scholar
  3. Axelrod R (2006) Advancing the art of simulation in the social sciences. In: Rennard J-P (ed), Handbook of research on nature inspired computing for economy and management. Idea Group, Hershey, PAGoogle Scholar
  4. Axtell R (2000) Why agents? On the varied motivations for agent computing in the social sciences. The Brookings Institution. Retrieved 11/5/2004, 2004, from the World Wide Web: http://www.brook.edu/es/dynamics/papers/agents/agents.pdfGoogle Scholar
  5. Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman Group Limited, EssexGoogle Scholar
  6. Blumstein A, Graddy E (1982) Prevalence and recidivism in index arrests: a feedback model. Law Soc Rev 16(2):265–290CrossRefGoogle Scholar
  7. Brantingham P, Brantingham P (1981) Introduction: the dimensions of crime. In Brantingham P, Brantingham P (eds.), Environmental criminology. Prospect heights, Waveland Press, Inc, IL, pp 7–26Google Scholar
  8. Brantingham P, Brantingham P (1981, 1990) Environmental criminology. Prospect Heights, Waveland Press, Inc, ILGoogle Scholar
  9. Brantingham PJ, Brantingham PL (1978) A theoretical model of crime site selection. In: Krohn MD, Akers RL (eds) Crime, law, and sanctions: theoretical perspectives. Sage, Beverly Hills, pp 105–118Google Scholar
  10. Brantingham PL, Brantingham PJ (2004) Computer simulation as a tool for environmental criminologists. Secur J 17(1):21–30Google Scholar
  11. Brantingham PL, Groff ER (2004) The future of agent-based simulation in environmental criminology. In: Paper presented at the American Society of Criminology, Nashville, TNGoogle Scholar
  12. Brown DG, Riolo R, Robinson DT, North M, Rand W (2005) Spatial process and data models: toward integration of agent-based models and GIS. J Geogr Syst 7:25–47CrossRefGoogle Scholar
  13. Bureau of Labor Statistics (2003) Metropolitan area employment and unemployment: January 2003. Bureau of Labor Statistics, United States Department of Labor. Retrieved, 2006, from the World Wide Web: www.bls.gov/news.release/archives/metro_03262003.pdfGoogle Scholar
  14. Bursik RJJ, Grasmick HG (1993) Neighborhoods and crime: the dimensions of effective community control. Lexington Books, New York, NYGoogle Scholar
  15. Calthrope P (1993) The next American metropolis: ecology, community and the American dream. Princeton Architectural Press, New YorkGoogle Scholar
  16. Capone DL, Nichols WW (1976) Urban structure and criminal mobility. Am Behav Sci 20:199–213CrossRefGoogle Scholar
  17. Chaitin G (1990) Information, randomness and incompleteness, 2nd edn. World Scientific, SingaporeGoogle Scholar
  18. Clarke RV, Cornish DB (1985) Modeling offender’s decisions: a framework for research and policy. In: Tonry M, Morris N (eds.), Crime and justice: an annual review of research, vol 6. University of Chicago Press, ChicagoGoogle Scholar
  19. Clarke RV, Cornish DB (2001) Rational choice. In: Paternoster R, Bachman R (eds.) Explaining criminals and crime. Roxbury Publishing Co., Los Angeles, pp 23–42Google Scholar
  20. Cohen LE (1981) Modeling crime trends: a criminal opportunity perspective. J Res Crime Delinq 18:138–163CrossRefGoogle Scholar
  21. Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44:588–608CrossRefGoogle Scholar
  22. Cohen LE, Kluegel JR, Land KC (1981) Social inequality and predatory criminal victimization: an exposition and test of a formal theory. Am Sociol Rev 46:505–524CrossRefGoogle Scholar
  23. Cullen FT, Agnew R (eds.) (1999) Criminological theory: past to present. Roxbury Publishing Company, Los Angeles, CAGoogle Scholar
  24. Dibble C (2006) Computational laboratories for spatial agent-based models. In: Tesfatsion L, Judd KL (eds.) Handbook of computational economics, Vol 2: agent-based computational economics, vol 2. Elsevier, AmsterdamGoogle Scholar
  25. Dowling D (1999) Experimenting on theories. Sci Context 12(2):261–273Google Scholar
  26. Duaney A, Plater-Zyberk E (1993) The neighborhood, the district and the corridor. In: Katz P (ed.) The new urbanism: toward an architecture of community. McGraw-Hill, New YorkGoogle Scholar
  27. Eck J (2005) Using crime pattern simulations to elaborate theory. Paper presented at the American Society of Criminology, TorontoGoogle Scholar
  28. Eck JE (1995) Examining routine activity theory: a review of two books. Justice Q 12(4):783–797CrossRefGoogle Scholar
  29. Eck JE, Liu L (2004) Routine activity theory in a RA/CA crime simulation. Paper presented at the American Society of Criminology, Nashville, TNGoogle Scholar
  30. Eck JE, Weisburd DL (1995) Crime places in crime theory. In: Eck JE, Weisburd DL (eds.) Crime and place. Willow Tree Press, Monsey, NY, pp 1–33Google Scholar
  31. Epstein JM, Axtell R (1996) Growing artificial societies. Brookings Institution Press, Washington DCGoogle Scholar
  32. Epstein JM, Steinbruner JD, Parker MT (2001) Modeling civil violence: an agent-based computational approach (Working Paper). Center on Social and Economic Dynamics, Brookings Institution, Washington DCGoogle Scholar
  33. ESRI (2005) ArcGIS 9.1. Redlands, Environmental Systems Research Institute, CAGoogle Scholar
  34. Felson M (1987) Routine activities and crime prevention in the developing metropolis. Criminology 25(4):911–931CrossRefGoogle Scholar
  35. Felson M (2001) The routine activity approach: a very versatile theory of crime. In: Paternoster R, Bachman R (eds.) Explaining criminals and crime. Roxbury Publishing Co., Los Angeles, pp 43–46Google Scholar
  36. Felson M (2002) Crime in everyday life, 3rd edn. Sage, Thousand Oaks, CAGoogle Scholar
  37. Gilbert N, Terna P (1999) How to build and use agent-based models in social science. Discussion Paper. Retrieved 9–30–2003, 2003, from the World Wide Web: http://web.econ.unito.it/terna/deposito/gil_ter.pdfGoogle Scholar
  38. Gilbert N, Troitzsch KG (1999) Simulation for the social scientist. Open University Press, BuckinghamGoogle Scholar
  39. Gove WR, Hughes M, Geerken M (1985) Are uniform crime reports a valid indicator of the index crimes? An affirmative answer with minor qualifications. Criminology 23:451–501CrossRefGoogle Scholar
  40. Groff ER (2007) ‘Situating’ simulation to model human spatio-temporal interactions: an example using crime events. Transactions in GISGoogle Scholar
  41. Gunderson L, Brown D (2003) Using a multi-agent model to predict both physical and cyber crime. Retrieved 11/12/03, 2003, from the World Wide Web: http://vijis.sys.virginia.edu/publication/SMCMultiAgent.pdfGoogle Scholar
  42. Hindelang MJ, Gottfredson MR, Garofalo J (1978) Victims of personal crime. Ballinger, Cambridge, MAGoogle Scholar
  43. Holland JH (1995) Hidden order: how adaptation builds complexity. Basic Books, New YorkGoogle Scholar
  44. Kennedy LW, Forde DR (1990) Routine activities and crime: an analysis of victimization in Canada. Criminology 28(1):137–151CrossRefGoogle Scholar
  45. Levine N (2005) CrimeStat: a spatial statistics program for the analysis of Crime Incident Locations (v 3.0). Ned Levine and Associates, Houston, TX, and the National Institute of Justice, Washington DCGoogle Scholar
  46. Liu L, Wang X, Eck J, Liang J (2005) Simulating crime events and crime patterns in RA/CA model. In: Wang F (ed.) Geographic information systems and crime analysis. Idea Group, Singapore, pp 197–213Google Scholar
  47. Macy MW, Willer R (2002) From factors to actors: computational sociology and agent-based modeling. Ann Rev Sociol 28:143–166CrossRefGoogle Scholar
  48. Manson SM (2001) Calibration, verification, and validation (Section 2.4). In Parker DC, Berger T, Manson SM, McConnell WJ (mng. ed (eds) Agent-based models of land-use and land-cover change: http://www.csiss.org/resources/maslucc/ABM-LUCC.pdf (last accessed March 14, 2005)Google Scholar
  49. McCord J (1979) Some child-rearing antecedents of criminal behavior in adult men. J Pers Soc Psychol 37(9):1477–1486CrossRefGoogle Scholar
  50. Meier RF, Kennedy LW, Sacco VF (2001) Crime and the criminal event perspective. In: Meier RF, Kennedy LW, Sacco VF (eds) The process and structure of crime: criminal events and crime analysis, Vol 9, Advances in Criminological Theory. Transaction Publishers, New Brunswick, NJ, pp 1–28Google Scholar
  51. Messner SF, Blau JR (1987) Routine leisure activities and rates of crime: a macro-level analysis. Social-Forces 65(4):1035–1052CrossRefGoogle Scholar
  52. Miethe TD, Hughes M, McDowall D (1991) Social change and crime rates: an evaluation of alternative theoretical approaches. Social Forces 70(1):165–185CrossRefGoogle Scholar
  53. Miethe TD, McDowall D (1993) Contextual effects in models of criminal victimization. Social Forces 71:741–759CrossRefGoogle Scholar
  54. Miethe TD, Stafford MC, Long JS (1987) Social differentiation in criminal victimization: a test of routine activities/lifestyle theories. Am Sociol Rev 52(2):184–194CrossRefGoogle Scholar
  55. Mitchell A (1999) The ESRI guide to GIS analysis (Vol. 1: Geographic patterns and relationships). Environmental Systems Research Institute Press, Redlands, CAGoogle Scholar
  56. Nelessen AC (1994) Visions for a new American dream: process, principle and an ordinance to plan and design small communities. Planners, ChicagoGoogle Scholar
  57. Newton RR, Rudestam KE (1999) Your statistical consultant: answers to your data analysis questions. Sage, Thousand OaksGoogle Scholar
  58. North MJ, Collier NT, Vos JR (2006) Experiences creating three implementations of the repast agent modeling Toolkit. ACM Trans Model Comput Simul 16(1):1–25CrossRefGoogle Scholar
  59. Olligschlaeger A, Gorr WA (1997) Spatio-temporal forecasting of crime: application of classical and neural network methods. In: H. John Heinz III School of Public Policy and Management, Carnegie Mellon University. Retrieved 2/15, 2004, from the World Wide Web: http://www.heinz.cmu.edu/wpapers/retrievePDF?id = 1997–23Google Scholar
  60. Osgood DW, Wilson JK, O’Malley PM, Bachman JG, Johnston LD (1996) Routine activities and individual deviant behavior. Am Sociol Rev 61:635–655CrossRefGoogle Scholar
  61. Ostrom TM (1988) Computer simulation: the third symbol system. J Exp Psychol 24:381–392CrossRefGoogle Scholar
  62. O’Sullivan D (2004) Complexity science and human geography. Trans Inst British Geogr 29:282–295CrossRefGoogle Scholar
  63. O’Sullivan D, Haklay M (2000) Agent-based models and individualism: is the world agent-based? Environ Plan A 32(8):1409–1425CrossRefGoogle Scholar
  64. Paternoster R (2001) The structure and relevance of theory in criminology. In: Paternoster R, Bachman R (eds) Explaining criminals and crime: essays in contemporary criminological theory. Roxbury Publishing Company, Los Angeles, CA, pp 1–10Google Scholar
  65. Perez P, Dray A (2005) SimDrug: exploring the complexity of heroin use in Melbourne. Turning Point Alcohol and Drug Centre Inc. Retrieved, from the World Wide Web: http://www.turningpoint.org.au/research/dpmp_monographs/dpmp_monograph11.pdfGoogle Scholar
  66. Ropella GE, Railsback SF, Jackson SK (2002) Software engineering considerations for individual-based models. Nat Resour Model 15(1):5–22CrossRefGoogle Scholar
  67. Rountree PW, Land KC (1996) Burglary victimization, perceptions of crime risk, and routine activities: a multilevel analysis across Seattle neighborhoods. J Res Crime Delinq 33(2):147–180CrossRefGoogle Scholar
  68. Sampson RJ, Lauritsen JL (1990) Deviant lifestyles, proximity to crime, and the offender-victim link in personal violence. J Res Crime Delinq 27(2):110–139CrossRefGoogle Scholar
  69. Sampson RJ, Wooldredge J (1987) Linking micro and macro dimensions of victimization models. J Quant Criminol 3(4):371–393CrossRefGoogle Scholar
  70. Schelling TC (1971) Dynamic models of segregation. J Math Sociol 1:143–186CrossRefGoogle Scholar
  71. Shannon DM, Davenport MA (2001) Using SPSS to solve statistical problems: a self-instruction guide. Prentice-Hall Inc, Upper Saddle River, NJGoogle Scholar
  72. Sherman LW, Weisburd D (1995) General deterrent effects of police patrol in crime ‘Hot Spots’: a randomized, controlled trial. Justice Q 12(4):625–648CrossRefGoogle Scholar
  73. Simon HA (1952) A behavioural model of rational choice. Q J Econ 69:99–118CrossRefGoogle Scholar
  74. SPSS (2002) SPSS for Windows (Version Release 11.5.0). SPSS Inc, ChicagoGoogle Scholar
  75. Troitzsch KG (2004) Validating simulation models. Paper presented at the 18th European Simulation Multiconference, Magdeburg, GermanyGoogle Scholar
  76. U.S. Census Bureau (Cartographer) (2000) Census 2000: Summary Tape File 1 (SF1)Google Scholar
  77. Visher CA, Roth JA (1986) Participation in criminal careers. In: Blumstein A, Cohen J, Roth JA, Visher CA (eds) Criminal careers and “Career Criminals”, Vol I. National Academy Press, Washington DC, pp 211–291Google Scholar
  78. Vold GB, Bernard TJ, Snipes JB (2002) Theoretical criminology. Oxford University Press, OxfordGoogle Scholar
  79. Walsh D (1986) Victim selection procedures among economic criminals: the rational choice perspective. In Cornish DB, Clarke RV (eds) The reasoning criminal: rational choice perspectives on offending. Springer-Verlag, New York, pp 39–52Google Scholar
  80. Wang X, Liu L, Eck J (2004) A spatial dynamic simulation of crime using agent-based modeling. Paper presented at the Association of American Geographers, Philadelphia, PAGoogle Scholar
  81. Weisburd D, Green L (1995) Policing drug hot spots: the Jersey City Drug Market Analysis experiment. Justice Q 12(4):711–735CrossRefGoogle Scholar
  82. Weisburd DL (2002) From criminals to criminal contexts: reorienting crime prevention. In: Waring E, Weisburd D (eds) Crime and social organization. Transactions Publishers, New Brunswick, NJ, Vol 10, pp 197–216Google Scholar
  83. Wilhite A (2001) Protection and social order. Paper presented at the Computational Economics and Finance Meeting, Yale UniversityGoogle Scholar
  84. Williamson D, Mclafferty S, McGuire Philip G, Ross TA, Mollenkopf JH, Goldsmith V, Quinn S (2001) Tools in the spatial analysis of crime. In: Hirschfield A, Bowers K (eds) Mapping and analysing crime data: lessons from research and practice. Taylor and Francis, London, pp 187–203Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Institute for Law and JusticeAlexandriaUSA

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