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

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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.

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Notes

  1. Environmental criminology is another important theory that emphasizes place characteristics and offender travel in the convergence of victims and offenders in space-time (Brantingham and Brantingham 1981, 1990; Brantingham and Brantingham 1978). Other theories relevant to micro level modeling include lifestyle theory (Hindelang et al. 1978) and the criminal event perspective (CEP) (Meier et al. 2001). However, the focus on one theory for the initial model precludes a full examination of these theories.

  2. The extensions to the original 1979 version of the theory are not incorporated into this first effort (Felson 2001, 2002). This was done in order to make the results of the model easier to interpret.

  3. Two studies (Miethe and McDowall 1993; Sampson and Wooldredge 1987) emphasized how opportunity structure changed across areas but neither measured how the spatio-temporal structure of routine activities impacted the observed distribution of crime.

  4. Simulation modeling, as discussed here, comes from the complex systems science tradition (see Holland (1995) for an introduction).

  5. The term computational laboratory refers to the software tools to create and evaluate models through systematic experimentation and descriptive analysis of output data (Dibble 2006, unpublished paper; Epstein and Axtell 1996; Gilbert and Terna 1999).

  6. Following Glaser, they define ‘direct-contact predatory violations’ as crimes where “someone definitely and intentionally takes or damages the person or property of another” (1974, p. 4).

  7. Following Epstein and Axtel (1996) this research does not specifically address how individuals make decisions but rather examines the effect of specific individual behaviors on macro-level social patterns.

  8. Bounded rationality, in particular, lends itself to investigation via agent-based models (O’Sullivan and Haklay 2000).

  9. The inclusion of a non artificial network on which agents move is critical to representing the impact of the street network on travel and subsequently on the opportunity for convergence. For a more thorough treatment of the technical aspects please see Groff (2007).

  10. Using data from 1966, Cohen and Felson calculated the average time spent away from home to be 7.74 h per day (32%). Since the goal is to test increases in time spent away from home from that point, the experimental conditions begin at 7.2 h per day spent away from home (30%) and increase by 10% with each subsequent condition to a high of 16.8 h per day (70%).

  11. The values of several of these parameters are assigned using random number generators (RNGs). In simulation models, random numbers have two main functions: (1) provide a stochastic element into what would otherwise be deterministic models of human behavior and (2) enable the replication of model results through assignment of a random number seed at the start of a simulation. The seed is the starting point for all random numbers that are produced during the course of a model run. A particular seed produces the same sequence of numbers each time. This attribute enables testing of the robustness of model outcomes since in simulation modeling the results of a single model run are vulnerable to being atypical (Axelrod 2006). This research applies an explicit random number seed based on the Mersenne Twister algorithm, currently considered to be the most robust available, as the basis for all random number distributions used in the model (Ropella et al. 2002).

  12. The choice of distribution (e.g., normal, poisson, etc.) and the mean and standard deviation used to assign values affect the allotment of the characteristics across all the agents. While the choices made here are not necessarily reflective of the actual distributions they offer an easily understood base for comparison.

  13. The simple depiction of agents at home or not at home provide a baseline from which to compare more complex representations of agent travel behavior (Groff 2007).

  14. The term kernel refers to size of the ‘neighborhood’ (also called bandwidth) that is taken into account when computing the density. The total number of street robberies within the bandwidth are summed and divided by the area under the circle. The resulting value is assigned to the current cell.

  15. Thanks to Ned Levine for pointing out this issue.

  16. Because of the positive skew to the distribution of robberies, additional tests regarding the equality of means were conducted. Both the Brown-Forsythe and the Welch tests for equality of the means are significant at .000. These tests are preferable to the F-test when the equality of variances assumption is violated as it is here (SPSS 2002).

  17. The Levene statistic is significant indicating the variances are significantly different among the groups. However, ANOVA is robust in the face of this violation when the group sizes are equal which they are in this research (Newton and Rudestam 1999; Shannon and Davenport 2001). A Tamhane’s T2 post hoc test is used because it does not assume equal variances.

  18. A bandwidth of 1,320 feet (one quarter mile) and a cell size of 100 feet are the basis for all kernel density surfaces. The quarter mile distance is often employed to represent the potential walking area for individuals in urban areas and by extension their potential area of interaction (Calthrope 1993; Duaney and Plater-Zyberk 1993; Nelessen 1994). The surfaces are generated in ArcGIS version 9.1 and the output is in robberies per square mile (Mitchell 1999).

  19. The reported Ripley’s K functions are generated using CrimeStat III. No edge correction is applied since approximately three quarters of the perimeter of Seattle is bounded by water.

  20. The CSR K function distribution is generated by using a uniform random number generator to create 100 distributions with the same N as the observed distribution, in this case N=16,035 (Levine 2005). A significance level of P < 0.05 is used. The random distribution generated under CSR is truly random in that any location can be selected, not just an intersection.

  21. The results of the sensitivity tests with random number seeds of 200, 300, 400 and 500 are available upon request.

References

  • Akers RL (2000) Criminological theories: introduction, evaluation, and application. Roxbury Publishing Company, Los Angeles

    Google Scholar 

  • 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):54–79

    Article  Google Scholar 

  • 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, PA

  • 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.pdf

  • Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman Group Limited, Essex

    Google Scholar 

  • Blumstein A, Graddy E (1982) Prevalence and recidivism in index arrests: a feedback model. Law Soc Rev 16(2):265–290

    Article  Google Scholar 

  • 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 7–26

    Google Scholar 

  • Brantingham P, Brantingham P (1981, 1990) Environmental criminology. Prospect Heights, Waveland Press, Inc, IL

  • 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 105–118

    Google Scholar 

  • Brantingham PL, Brantingham PJ (2004) Computer simulation as a tool for environmental criminologists. Secur J 17(1):21–30

    Google Scholar 

  • Brantingham PL, Groff ER (2004) The future of agent-based simulation in environmental criminology. In: Paper presented at the American Society of Criminology, Nashville, TN

    Google Scholar 

  • 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 7:25–47

    Article  Google Scholar 

  • 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.pdf

  • Bursik RJJ, Grasmick HG (1993) Neighborhoods and crime: the dimensions of effective community control. Lexington Books, New York, NY

    Google Scholar 

  • Calthrope P (1993) The next American metropolis: ecology, community and the American dream. Princeton Architectural Press, New York

    Google Scholar 

  • Capone DL, Nichols WW (1976) Urban structure and criminal mobility. Am Behav Sci 20:199–213

    Article  Google Scholar 

  • Chaitin G (1990) Information, randomness and incompleteness, 2nd edn. World Scientific, Singapore

    Google Scholar 

  • 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, Chicago

    Google Scholar 

  • Clarke RV, Cornish DB (2001) Rational choice. In: Paternoster R, Bachman R (eds.) Explaining criminals and crime. Roxbury Publishing Co., Los Angeles, pp 23–42

    Google Scholar 

  • Cohen LE (1981) Modeling crime trends: a criminal opportunity perspective. J Res Crime Delinq 18:138–163

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Cohen LE, Kluegel JR, Land KC (1981) Social inequality and predatory criminal victimization: an exposition and test of a formal theory. Am Sociol Rev 46:505–524

    Article  Google Scholar 

  • Cullen FT, Agnew R (eds.) (1999) Criminological theory: past to present. Roxbury Publishing Company, Los Angeles, CA

    Google Scholar 

  • 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, Amsterdam

  • Dowling D (1999) Experimenting on theories. Sci Context 12(2):261–273

    Google Scholar 

  • 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 York

    Google Scholar 

  • Eck J (2005) Using crime pattern simulations to elaborate theory. Paper presented at the American Society of Criminology, Toronto

  • Eck JE (1995) Examining routine activity theory: a review of two books. Justice Q 12(4):783–797

    Article  Google Scholar 

  • Eck JE, Liu L (2004) Routine activity theory in a RA/CA crime simulation. Paper presented at the American Society of Criminology, Nashville, TN

  • 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 1–33

    Google Scholar 

  • Epstein JM, Axtell R (1996) Growing artificial societies. Brookings Institution Press, Washington DC

    Google Scholar 

  • 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 DC

  • ESRI (2005) ArcGIS 9.1. Redlands, Environmental Systems Research Institute, CA

    Google Scholar 

  • Felson M (1987) Routine activities and crime prevention in the developing metropolis. Criminology 25(4):911–931

    Article  Google Scholar 

  • 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 43–46

    Google Scholar 

  • Felson M (2002) Crime in everyday life, 3rd edn. Sage, Thousand Oaks, CA

    Google Scholar 

  • 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.pdf

  • Gilbert N, Troitzsch KG (1999) Simulation for the social scientist. Open University Press, Buckingham

    Google Scholar 

  • 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 23:451–501

    Article  Google Scholar 

  • Groff ER (2007) ‘Situating’ simulation to model human spatio-temporal interactions: an example using crime events. Transactions in GIS

  • 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.pdf

  • Hindelang MJ, Gottfredson MR, Garofalo J (1978) Victims of personal crime. Ballinger, Cambridge, MA

    Google Scholar 

  • Holland JH (1995) Hidden order: how adaptation builds complexity. Basic Books, New York

    Google Scholar 

  • Kennedy LW, Forde DR (1990) Routine activities and crime: an analysis of victimization in Canada. Criminology 28(1):137–151

    Article  Google Scholar 

  • 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 DC

    Google Scholar 

  • 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 197–213

    Google Scholar 

  • Macy MW, Willer R (2002) From factors to actors: computational sociology and agent-based modeling. Ann Rev Sociol 28:143–166

    Article  Google Scholar 

  • 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)

  • McCord J (1979) Some child-rearing antecedents of criminal behavior in adult men. J Pers Soc Psychol 37(9):1477–1486

    Article  Google Scholar 

  • 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 1–28

    Google Scholar 

  • Messner SF, Blau JR (1987) Routine leisure activities and rates of crime: a macro-level analysis. Social-Forces 65(4):1035–1052

    Article  Google Scholar 

  • Miethe TD, Hughes M, McDowall D (1991) Social change and crime rates: an evaluation of alternative theoretical approaches. Social Forces 70(1):165–185

    Article  Google Scholar 

  • Miethe TD, McDowall D (1993) Contextual effects in models of criminal victimization. Social Forces 71:741–759

    Article  Google Scholar 

  • Miethe TD, Stafford MC, Long JS (1987) Social differentiation in criminal victimization: a test of routine activities/lifestyle theories. Am Sociol Rev 52(2):184–194

    Article  Google Scholar 

  • Mitchell A (1999) The ESRI guide to GIS analysis (Vol. 1: Geographic patterns and relationships). Environmental Systems Research Institute Press, Redlands, CA

    Google Scholar 

  • Nelessen AC (1994) Visions for a new American dream: process, principle and an ordinance to plan and design small communities. Planners, Chicago

    Google Scholar 

  • Newton RR, Rudestam KE (1999) Your statistical consultant: answers to your data analysis questions. Sage, Thousand Oaks

    Google Scholar 

  • North MJ, Collier NT, Vos JR (2006) Experiences creating three implementations of the repast agent modeling Toolkit. ACM Trans Model Comput Simul 16(1):1–25

    Article  Google Scholar 

  • 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 = 1997–23

  • Osgood DW, Wilson JK, O’Malley PM, Bachman JG, Johnston LD (1996) Routine activities and individual deviant behavior. Am Sociol Rev 61:635–655

    Article  Google Scholar 

  • Ostrom TM (1988) Computer simulation: the third symbol system. J Exp Psychol 24:381–392

    Article  Google Scholar 

  • O’Sullivan D (2004) Complexity science and human geography. Trans Inst British Geogr 29:282–295

    Article  Google Scholar 

  • O’Sullivan D, Haklay M (2000) Agent-based models and individualism: is the world agent-based? Environ Plan A 32(8):1409–1425

    Article  Google Scholar 

  • 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 1–10

    Google Scholar 

  • 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.pdf

  • Ropella GE, Railsback SF, Jackson SK (2002) Software engineering considerations for individual-based models. Nat Resour Model 15(1):5–22

    Article  Google Scholar 

  • 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):147–180

    Article  Google Scholar 

  • Sampson RJ, Lauritsen JL (1990) Deviant lifestyles, proximity to crime, and the offender-victim link in personal violence. J Res Crime Delinq 27(2):110–139

    Article  Google Scholar 

  • Sampson RJ, Wooldredge J (1987) Linking micro and macro dimensions of victimization models. J Quant Criminol 3(4):371–393

    Article  Google Scholar 

  • Schelling TC (1971) Dynamic models of segregation. J Math Sociol 1:143–186

    Article  Google Scholar 

  • Shannon DM, Davenport MA (2001) Using SPSS to solve statistical problems: a self-instruction guide. Prentice-Hall Inc, Upper Saddle River, NJ

    Google Scholar 

  • Sherman LW, Weisburd D (1995) General deterrent effects of police patrol in crime ‘Hot Spots’: a randomized, controlled trial. Justice Q 12(4):625–648

    Article  Google Scholar 

  • Simon HA (1952) A behavioural model of rational choice. Q J Econ 69:99–118

    Article  Google Scholar 

  • SPSS (2002) SPSS for Windows (Version Release 11.5.0). SPSS Inc, Chicago

  • Troitzsch KG (2004) Validating simulation models. Paper presented at the 18th European Simulation Multiconference, Magdeburg, Germany

  • U.S. Census Bureau (Cartographer) (2000) Census 2000: Summary Tape File 1 (SF1)

  • 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 211–291

    Google Scholar 

  • Vold GB, Bernard TJ, Snipes JB (2002) Theoretical criminology. Oxford University Press, Oxford

    Google Scholar 

  • 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 39–52

    Google Scholar 

  • 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, PA

  • Weisburd D, Green L (1995) Policing drug hot spots: the Jersey City Drug Market Analysis experiment. Justice Q 12(4):711–735

    Article  Google Scholar 

  • 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 197–216

    Google Scholar 

  • Wilhite A (2001) Protection and social order. Paper presented at the Computational Economics and Finance Meeting, Yale University

  • 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 187–203

    Google Scholar 

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Acknowledgments

This research was supported in part by the Grant 2005-IJ-CX-0015 from the National Institute of Justice. The author wishes to thank Ronald Clarke and Marcus Felson for reviewing the validity of the conceptual representations of their respective theories. Discussions with Jochen Albrecht, Catherine Dibble, and Tobi Glensk contributed to the formalization of theory in conceptual model and the choice of implementation strategy. David Weisburd, Ned Levine, Tom McEwen and the anonymous reviewers provided illuminating comments on earlier drafts of this paper.

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    Elizabeth R. Groff

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Groff, E.R. Simulation for Theory Testing and Experimentation: An Example Using Routine Activity Theory and Street Robbery. J Quant Criminol 23, 75–103 (2007). https://doi.org/10.1007/s10940-006-9021-z

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