Adding the Temporal and Spatial Aspects of Routine Activities: A Further Test of Routine Activity Theory
- 141 Downloads
Routine activity theory identifies the routine activities of individuals as important to understanding the convergence of elements necessary for a crime to occur. Two recent studies have demonstrated how geographically aware agent-based models can be used to provide a virtual rather than empirical laboratory for testing theory. Those studies trace the development of three versions of a basic street robbery model with different representations of routine activities (random, temporal constraints, and spatio-temporal constraints). This research uses the existing model to test whether the core premise of routine activity theory (i.e., as time away from home increases so will street robbery) holds true under the different versions of activity spaces. The findings indicate that temporal and spatial constraints have separate and unequal influences on the incidence of crime. These results substantiate the key role of spatio-temporal constraints in determining the opportunities for and incidence of street robbery events.
Keywordstheory testing simulation agent-based models geographic information systems activity spaces
This research was supported in part by the Grant 2005-IJ-CX-0015 from the National Institute of Justice. The author wishes to thank the anonymous reviewers who provided helpful comments on an earlier draft of this paper.
- Akers, R.L. (2000) Criminological Theories: Introduction, Evaluation, and Application. Los Angeles: Roxbury Publishing Company.Google Scholar
- Axelrod, R. (2006) Simulation in the Social Sciences. In Rennard, J.-P. (ed.) Handbook of Research on Nature Inspired Computing for Economy and Management, Vol. 1, Hershey PA: Idea Group, pp 90–100.Google Scholar
- 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.Google Scholar
- Bonabeau, E. (2002) Agent-Based Modeling: Methods and Techniques for Simulating Human Systems. Paper presented at the Arthur M. Sackler Colloquium of the National Academy of Sciences, Irvine, CA.Google Scholar
- Brantingham, P. and Brantingham, P. (eds) (1981a) Introduction to the 1991 Reissue: Notes on Environmental Criminology. Environmental Criminology. Prospect Heights: Waveland Press Inc., pp 1–6.Google Scholar
- Brantingham, P. and Brantingham, P. (eds) (1981b) Notes on the Geometry of Crime.Environmental Criminology. Prospect Heights, IL: Waveland Press, Inc., pp 27–54.Google Scholar
- Brantingham, P.J. and Brantingham, P.L. (1984) Patterns in Crime. New York: Macmillan.Google Scholar
- Brantingham, P.L. and Groff, E.R. (2004) The Future of Agent-Based Simulation in Environmental Criminology. Paper presented at the American Society of Criminology, Nashville, TN.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.
- Clarke, R.V. and Cornish, D.B. (1985) Modeling Offender's Decisions: A Framework for Research and Policy. In Tonry, M. and Morris, N. (eds) Crime and Justice: An Annual Review of Research, Volume 6. Chicago: University of Chicago Press.Google Scholar
- Clarke, R.V. and Cornish, D.B. (2001) Rational Choice. In Paternoster, R. and Bachman, R. (eds) Explaining Criminals and Crime. Los Angeles: Roxbury Publishing Co., pp 23–42.Google Scholar
- Dibble, C. (2001) Theory in a Complex World: GeoGraph Computational Laboratories. Unpublished Ph.D. Dissertation, University of California Santa Barbara, Santa Barbara.Google Scholar
- Dibble, C. (Unpublished paper) Theory in a Complex World: GeoGraph Computational Laboratories.Google Scholar
- Eck, J.E. (2005) Using Crime Pattern Simulations to Elaborate Theory. Paper presented at the American Society of Criminology, Toronto.Google Scholar
- Eck, J.E. and Liu, L. (2004) Routine Activity Theory in a RA/CA Crime Simulation. Paper presented at the American Society of Criminology, Nashville, TN.Google Scholar
- Epstein, J.M. and Axtell, R. (1996) Growing Artificial Societies. Washington DC: Brookings Institution Press.Google Scholar
- ESRI (2003) Business Location Data. Redlands, CA: Environmental Systems Research Institute.Google Scholar
- ESRI (2005) ArcGIS 9.1. Redlands, CA: Environmental Systems Research Institute.Google Scholar
- Gilbert, N. and Doran, J. (eds) (1994) Simulating Societies: The Computer Simulation of Social Phenomena. London: University College London Press.Google Scholar
- Gilbert, N. and 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. and Troitzsch, K.G. (1999) Simulation for the Social Scientist. Buckingham: Open University Press.Google Scholar
- Groff, E.R. (2007c) Spatio-Temporal Aspects of Routine Activities and the Distribution of Street Robbery. In Liu, L. and Eck, J. (eds) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. Hershey, PA: Idea Group.Google Scholar
- Harvey, D. (1969) Explanation in Geography. London: Edward Arnold Publishers.Google Scholar
- Huisman, O. and Forer, P. (1998) Computational Agents and Urban Life Spaces: A Preliminary Realization of the Time–Geography of Student Lifestyles. Paper presented at the GeoComputation 98, Bristol, UK.Google Scholar
- Jeffery, C.R. (1971) Crime Prevention Through Environmental Design. Beverly Hills, CA: Sage Publications.Google Scholar
- Lempert, R. (2002) Agent-Based Modeling as Organizational and Public Policy Simulators. Paper presented at the Arthur M. Sackler Colloquium of the National Academy of Sciences, Irvine, CA.Google Scholar
- Newman, O. (1972) Defensible Space: Crime Prevention Through Environmental Design. New York: Macmillan.Google Scholar
- Newton, R.R. and Rudestam, K.E. (1999) Your Statistical Consultant: Answers to Your Data Analysis Questions. Thousand Oaks: Sage.Google Scholar
- Ostrom, T.M. (1988) Computer Simulation: The Third Symbol System. Journal of Experimental Psychology. Vol. 24, pp 381–392.Google Scholar
- Parker, D.C., Berger, T. and Manson, S.M. (2001) Agent-Based Models of Land-Use/Land-Cover Change in LUCC Report Series No. 6: LUCC Focus 1 Office Anthropological Center for Training and Research on Global Environmental Change, Indiana University.Google Scholar
- Shannon, D.M. and Davenport, M.A. (2001) Using SPSS to Solve Statistical Problems: A Self-Instruction Guide. Upper Saddle River, NJ: Prentice-Hall Inc.Google Scholar
- Tesfatsion, L. (2001) Guest Editorial Agent-Based Modeling of Evolutionary Economic Systems. Computation. Vol. 5, No. 5, pp 437–441.Google Scholar
- U.S. Census Bureau (Cartographer) (2000) Census 2000: Summary Tape File 1 (SF1).Google Scholar
- U.S. Census Bureau. (2002) County Business Patterns. U.S. Census Bureau. Retrieved, from the World Wide Web:http://censtats.census.gov/cbpnaic/cbpnaic.shtml.
- Visher, C A and Roth, J.A. (1986) Participation in Criminal Careers. In Blumstein, A., Cohen, J., Roth, J. A., and Visher, C. A. (eds) Criminal Careers and “Career Criminals”. Vol. I. Washington DC: National Academy Press, pp 211–229.Google Scholar