Data-informed crime prevention at convenience stores in Atlantic City

ABSTRACT

The Atlantic City Police Department intervened to reduce robberies with an evidence-based approach grounded in problem-oriented policing. Informed by risk terrain modeling and hot spot analysis, police commanders implemented a place-based intervention focused around convenience stores. Target areas throughout the city were reprioritized each month to create a dynamic deployment strategy that efficiently allocated resources to the most vulnerable places. Risk reduction actions, such as business checks, were favored over law enforcement against people. Robberies significantly decreased by 63% within four months. There was a significant spatial diffusion of benefits and there were fewer arrests, as should be expected with fewer crimes and a tactical place-based, not person-oriented, approach. Implications for policy and practice are discussed within the contexts of rapid evidence-based police responses to urgent crime problems, police culture, and data analytics.

KEYWORDS:

• Convenience stores
• risk terrain modeling
• police intervention

Acknowledgments

The authors would like to thank the ACPD for their assistance and participation in this project.

Conflict of Interest

Leslie Kennedy and Joel Caplan are co-founders of Simsi Inc., a Rutgers University startup.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. U.S. Census Bureau Quick Facts (2011 estimate) http://quickfacts.census.gov/qfd/states/34/3402080.html.

2. http://www.atlanticcitynj.com/!userfiles/pdfs/Reports/ACVP08_summ.pdf, http://atlanticcitymaps.com/, http://www.visitnj.org/atlantic-city.

3. The clustering that was found in November and December could reflect changes in the larger routines of people given the holidays, or the related vacation travels to this tourist destination. The before and after ‘randomness’ could reflect changes in populations and their (new)routine activities at these points leading to changes in victim-offender opportunity convergence. That is, there may have been a turbulence of human activity, so to speak, that resulted in seemingly more stochastic crime patterns. Just as it is important to use small spatial units of analysis to identify geographic variation, the variation across these monthly assessments is a good reminder of how larger temporal scales could have missed the nuance (i.e., Modifiable Temporal Unit Problem – MTUP; see Cheng et al., 2014).

4. From the Rutgers University Center on Public Security: www.rutgerscps.org/software.html.

5. For a full bibliography, see http://www.riskterrainmodeling.com/rtmworks.html.

6. InfoGroup is a data and marketing services company that provides information about public entities, such as businesses/retail stores.

7. Dr. Wheeler provides further discussion of the WDD on his website. This also includes a post on the WDD when the pre/post time periods are different. See https://andrewpwheeler.com/.

8. See Wheeler’s blog post for the area weighted WDD: https://andrewpwheeler.com/2021/02/23/the-wdd-test-with-different-area-sizes.

Additional information

Funding

This work was supported by the Atlantic Country Prosecutor’s Office.

Notes on contributors

Leslie W. Kennedy

Leslie W. Kennedy is a University Professor at Rutgers School of Criminal Justice.

Joel M. Caplan

Joel M. Caplan is a Professor at Rutgers School of Criminal Justice.

Grant Drawve

Grant Drawve is an Assistant Professor in the Department of Sociology and Criminology at the University of Arkansas.