Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-22T16:20:13.588Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling policing strategies for departments with limited resources

Published online by Cambridge University Press:  06 November 2015

ALEJANDRO CAMACHO ,
HYE RIN LINDSAY LEE  and
LAURA M. SMITH
ALEJANDRO CAMACHO
Affiliation:
California State University, Fullerton Department of Mathematics email: [email protected], [email protected]
HYE RIN LINDSAY LEE
Affiliation:
Case Western Reserve University Department of Mathematics, Applied Mathematics, and Statistics email: [email protected]
LAURA M. SMITH
Affiliation:
California State University, Fullerton Department of Mathematics email: [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Crime prevention is a major goal of law-enforcement agencies. Often, these agencies have limited resources and officers available for patrolling and responding to calls. However, patrolling and police visibility can influence individuals to not perform criminal acts. Therefore, it is necessary for the police to optimize their patrolling strategies to deter the most crime. Previous studies have created agent-based models to simulate criminal and police agents interacting in a city, indicating a “cops on the dots” strategy as a viable method to mitigate large amounts of crime. Unfortunately, police departments cannot allocate all of the patrolling officers to seek out these hotspots, particularly since they are not immediately known. In large cities, it is often necessary to keep a few officers in different areas of the city, frequently divided up into beats. Officers need to respond to calls, possibly not of a criminal nature. Therefore, we modify models for policing to account for these factors. Through testing the policing strategies for various hotspot types and number of police agents, we found that the methods that performed the best varied greatly according to these factors.

Keywords

Agent Based MethodsCrime ModelsHot SpotsPolicing Strategies
Type
Papers
Information
European Journal of Applied Mathematics , Volume 27 , Issue 3: Mathematical Modelling of Crime and Security , June 2016 , pp. 479 - 501
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Barclay, P., Buckley, J., Brantingham, P. J., Brantingham, P. L. & Whinn-Yates, T. (1996) Preventing auto theft in suburban Vancouver commuter lots: Effects of a bike patrol. In: Clark, R.V. (Ed.), Preventing Mass Transit Crime, 133–161. Monsey, NY: Criminal Justice Press.Google Scholar
[2]Birks, D., Townsley, M. & Stewart, A. (2012) Generative explanations of crime: Using simulation to test criminological theory. Criminology 50 (1), 221.Google Scholar
[3]Block, R. L. & Block, C. R. (1995) Space, place and crime: Hot spot areas and hot places of liquor-related crime. Crime Place 4 (2), 145–184.Google Scholar
[4]Braga, A. A. (2001) The effects of hot spots policing on crime. Ann. Am. Acad. Political Soci. Sci. 578 (1), 104–125.CrossRefGoogle Scholar
[5]Brantingham, P. J. & Brantingham, P. L. (1984) Patterns in Crime, Macmillan, New York.Google Scholar
[6]Brantingham, P. L. & Brantingham, P. J. (1999) A theoretical model of crime hot spot generation. Stud. Crime Crime Prevention, 8 (1), 7–26.Google Scholar
[7]Chaturapruek, S., Breslau, J., Yazdi, D., Kolokolnikov, T. & McCalla, S. G. (2013) Crime modeling with Lévy flights. SIAM J. Appl. Math. 73 (4), 1703–1720.CrossRefGoogle Scholar
[8]Eck, J., Chainey, S., Cameron, J. & Wilson, R. (2005) Mapping crime: Understanding hotspots. National Institute of Justice.Google Scholar
[9]Eck, J. E. & Liu, L. (2008) Contrasting simulated and empirical experiments in crime prevention. J. Exp. Criminology 4 (3), 195–213.Google Scholar
[10]Eck, J. E. & Weisburd, D. (1995) Crime places in crime theory. Crime Place Prevention Stud. 4 (1), 1–33.Google Scholar
[11]Felson, M. & Clarke, R. V. (1998) Opportunity makes the thief. Police Research Series. Paper 98. London: Home Office, 1–36.Google Scholar
[12]Hegemann, R. A., Smith, L. M., Barbaro, A. B. T., Bertozzi, A. L., Reid, S. & Tita, G. E. (2011) Geographical influences of an emerging network of gang rivalries. Phys. Stat. Mech. Appl. 390 (21).Google Scholar
[13]Johnson, S. D., Bernasco, W., Bowers, K., Elffers, H., Ratcliffe, J., Rengert, G. & Townsley, M. (2007) Space–time patterns of risk: A cross national assessment of residential burglary victimization. J. Quant. Criminology, 23 (3), 201–219.CrossRefGoogle Scholar
[14]Johnson, S. D., Bowers, K. & Hirschfield, A. (1997) New insights into the spatial and temporal distribution of repeat victimization. Br. J. Criminology 37 (2), 224–241.CrossRefGoogle Scholar
[15]Jones, P. A., Brantingham, P. J. & Chayes, L. R. (2010) Statistical models of criminal behavior: The effects of law enforcement actions. Math. Models Methods Appl. Sci. 20 (supp01), 1397–1423.Google Scholar
[16]Kearney, M. S., Harris, B. H., Jácome, E. & Parker, L. (2014) Ten economic facts about crime and incarceration in the United States. The Hamilton Project, Washington, DC.Google Scholar
[17]Mather, K. & Winton, R. (2015) LAPD uses its helicopters to stop crimes before they start. Los Angeles Times, March 7.Google Scholar
[18]Mitchell, P. S. (1972) Optimal selection of police patrol beats. J. Criminal Law Criminology Police Sci. 63 (4), 577–584.CrossRefGoogle Scholar
[19]Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P. & Tita, G. E. (2011) Self-exciting point process modeling of crime. J. Am. Stat. Assoc. 106 (493), 100–108.Google Scholar
[20]Moreto, W. D., Piza, E. L. & Caplan, J. M. (2014) “A plague on both your houses?”: Risks, repeats and reconsiderations of urban residential burglary. JQ: Justice Q. 31 (6), 1102–1126.Google Scholar
[21]Nam, S. H. (2012) Optimal Temporal-Spatial Deployment of Urban Law Enforcement Personnel: Theory, Analysis and Implementation, PhD thesis, Princeton University.Google Scholar
[22]Parks, R. B., Mastrofski, S. D., Dejong, C., & Gray, M. K. (1999) How officers spend their time with the community. Justice Q. 16 (3), 483–518.Google Scholar
[23]Pitcher, A. B. (2010) Adding police to a mathematical model of burglary. Eur. J. Appl. Math. 21 (4–5), 401–419.Google Scholar
[24]Ratcliffe, J. H. (2004) The hotspot matrix: A framework for the spatio-temporal targeting of crime reduction. Police Practice Res. 5 (1), 5–23.Google Scholar
[25]Rodriguez, N. & Bertozzi, A. L. (2010) Local existence and uniqueness of solutions to a PDE model for criminal behavior. Math. Models Methods Appl. Sci. 20 (supp01), 1425–1457.Google Scholar
[26]Sherman, L. W. & Weisburd, D. (1995) General deterrent effects of police patrol in crime “hot spots”: A randomized, controlled trial. Justice Q. 12 (4), 625–648.Google Scholar
[27]Short, M. B., D'Orsogna, M. R., Pasour, V. B., Tita, G. E., Brantingham, P. J., Bertozzi, A. L. & Chayes, L. B. (2008) A statistical model of criminal behavior. Math. Models Methods Appl. Sci. 18 (supp01), 1249–1267.Google Scholar
[28]Sutanto, A. (June 2011) Optimal police patrol, RWTH Aachen University Center for Computational Engineering Sciences – Undergraduate Research Opportunities Program.Google Scholar
[29]Taylor, B., Koper, C., & Woods, D. (2011) A randomized controlled trial of different policing strategies at hot spots of violent crime. J. Exp. Criminology 7 (2), 149–181.Google Scholar
[30]Wilson, J. Q. & Kelling, G. L. (1982) Broken windows. Atlantic Mon. 249 (3), 29–38.Google Scholar
[31]Zipkin, J. R., Short, M. B. & Bertozzi, A. L. (2014) Cops on the dots in a mathematical model of urban crime and police response. Discrete Continuous Dyn. Syst. 34 (2014), 1479–1506.CrossRefGoogle Scholar