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The effects of merging proactive CCTV monitoring with directed police patrol: a randomized controlled trial

Journal of Experimental Criminology volume 11, pages 43–69 (2015)Cite this article

Abstract

Objectives

This study was designed to test the effect of increased certainty of punishment on reported crime levels in CCTV target areas of Newark, NJ. The experimental strategy was designed for the purpose of overcoming specific surveillance barriers that minimize the effectiveness of CCTV, namely high camera-to-operator ratios and the differential response policy of police dispatch. An additional camera operator was deployed to monitor specific CCTV cameras, with two patrol cars dedicated to exclusively responding to incidents of concern detected on the experimental cameras.

Methods

A randomized controlled trial was implemented in the analysis. A randomized block design was used to assign each of the 38 CCTV schemes to either a treatment or control group. Schemes were grouped into pairs based upon their levels of three types of calls for service: violent crime, social disorder, and narcotics activity. Negative binomial regression models tested the effect that assignment to the treatment group had on levels of the aforementioned crime categories.

Results

The experimental strategy was associated with significant reductions of violent crime and social disorder in the treatment areas relative to the control areas. Incidence Rate Ratio (IRR) and Total Net Effect (TNE) values suggest that the number of crime incidents prevented was sizable in numerous instances. The experiment had much less of an effect on narcotics activity.

Conclusions

Overall, the findings support the hypothesis that the integration of CCTV with proactive police activity generates a crime control benefit greater than what research suggests is achievable via “stand-alone” camera deployment, particularly in the case of street-level crime.

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Fig. 1
Fig. 2

Notes

  1. We recognize the debate regarding researcher involvement in the design and implementation of interventions. Some scholars have argued that close working relationships between practitioners and academics may create a conflict of interest, which can threaten the validity of the analysis (Eisner 2009). Others have argued that such collaboration is necessary in order to develop, test, and disseminate evidence-based practices (Braga 2010; Olds 2009). While the debate is ongoing, a meta-analysis of experimental and quasi-experimental evaluations of police interventions by Welsh et al. (2012) found no evidence that evaluator involvement in program implementation biased the results. Furthermore, the recent study of Telep et al. (2012) focused on a field experiment carried out primarily by the Sacramento Police Department, adding further support for researcher/practitioner relationships.

  2. This grid is the standard deviation ellipse discussed below.

  3. See Piza et al. (2014a) and Piza (2012) for more detailed descriptions of viewshed and catchment zone creation.

  4. The ellipse was extended slightly to the northeast so that it encompassed the catchment zones of each scheme.

  5. The CONSORT Checklist was also used for this report. The checklist provides 25 items that must be included in a Randomized Controlled Trial report to comply with CONSORT standards. We referenced the checklist to ensure that each of the applicable items was presented within this report (see: http://www.consort-statement.org/consort-2010).

  6. Violent crime included the following incident types: assault, carjacking, fight, robbery, shooting (including shots fired), stabbing, and weapons possession. Social disorder included the following incident types: disorderly persons, drinking in public, noise complaint, obstruction of public passage, panhandling, prostitution, and urinating in public. Narcotics activity included the following incident types: Drug activity reported via the anonymous tip line, street-level drug activity, and unverified drug activity (meaning the complainant did not directly witness the drug transaction, but has ample reason to believe a drug transaction occurred).

  7. Calls-for-service may provide advantages over Part 1 crime data since they are less influenced by police discretion (Braga, et al. 1999; Warner and Pierce 1993). However, a potential shortcoming of calls data is they are subject to over reporting due to multiple citizens calling about the same incident (Klinger and Bridges 1997). To protect against this, we deleted duplicate calls-for-service (identified by their police-assigned event numbers) prior to our analysis.

  8. The standard deviation ellipse, rather than the entirety of Newark, was considered the control area in the LQ formula. Therefore, the crime count and square footage of the ellipse were used as X and T, respectively, in the denominator of the LQ formula.

  9. As will be discussed later in the text, experiment effect on crime was measured across three different time periods. Additional t tests conducted for each of these time periods further demonstrated balance between treatment and control areas, with test statistics not achieving significance in a single instance. Due to limited space, the t tests results are not presented in text, but are available from the lead author upon request.

  10. X 2 goodness-of-fits tests conducted after exploratory Poisson regression models confirmed that each crime type was distributed as a negative binomial process. For the “Tours” models: Violence, Pearson X 2 = 57.05, p = 0.01; Disorder, Pearson X 2 = 65.18, p = 0.001; Narcotics, Pearson X 2 = 56.06, p = 0.01. For the “Days” models: Violence, Pearson X 2 = 62.75, p = 0.002; Disorder, Pearson X 2 = 92.98, p = 0.000; Narcotics, Pearson X 2 = 67.39, p = 0.001. For the “11-Week” models: Violence, Pearson X 2 = 63.89, p = 0.001; Disorder, Pearson X 2 = 133.19, p = 0.000; Narcotics, Pearson X 2 = 106.10, p = 0.000. For all tests df = 34.

  11. We recognize that another option was to conduct a multi-level analysis treating the number of events in the pretest period as a random effect across the 19 pairs of schemes. However, due the small size of the level one sample (pairs of schemes) and the fact that a single measurement was made during the “pre” and “post” periods, we concluded that a multi-level model was inappropriate for this data (Snijders 2005). This decision aligns with prior place-based policing experiments (see Braga and Bond 2008: p. 589, footnote 10).

  12. Separate correlation matrices were created for the main and residual models, which are discussed below. Given limited space, the correlation matrices are not presented in text, but are available from the lead author upon request.

  13. It should be noted that, since these events were not reported via CAD, the operator activity was not captured within the calls-for-service counts. Thus, the analysis of outcome measures was not influenced by operator activity.

  14. Record checks refer to incidents where police officers detain a suspect at the scene and contact the communications division via radio to ascertain whether the individual has any open warrants, or is otherwise wanted for a crime. Field interrogations refer to instances where police officers detain a suspect at the scene and question them regarding the incident observed by the CCTV operator. In most cases, record checks and field interrogations were conducted in unison, so we report them both together as “other enforcement”. In the event an arrest occurred as a result of a record check or field interrogation, the incident was counted as an arrest. Therefore, the enforcement categories are mutually exclusive.

  15. While each of these 64 enforcement actions occurred within the treatment area, there were two incidents (which are not reflected in these figures) where patrol officers enacted a proactive enforcement action outside of the treatment area. In one instance, a patrol unit was flagged down by a pedestrian who reported that he had just been robbed at gunpoint. The officers thus pursued and apprehended the suspect. In the other instance, officers observed what they believed to be a hand-to-hand narcotics transaction between two males in front of a house. After detaining the suspects, the officers found both individuals to be in possession of narcotics and placed both of them under arrest. It is important to note that these two incidents had no bearing on the evaluation since both arrests occurred outside of both the treatment and control schemes.

  16. As explained by Lipsey (1990), in statistical power calculations “[s]ample size refers to the number of subjects in each group—for example, ten treatment subjects compared with ten control subjects is represented as a sample size of 10” (p. 71: emphasis in original text).

  17. Increasing sample size is typically considered as the most straightforward way to increase statistical power. However, as noted by Weisburd et al. (1993), increasing sample size may negatively affect field experiments by creating a treatment group too large for each case to receive proper dosage. Indeed, Buerger (1993) reported that such an instance occurred in the Minneapolis RECAP experiment (Sherman et al. 1989). We, therefore, decided to not increase our sample size at the outset of the experiment in order to protect against spreading the treatment too thinly across the treatment area.

  18. It is common practice for a pre-intervention power analysis, identifying the necessary sample size to achieve statistical power, to guide the scope of experimental studies. Unfortunately, the changing fiscal situation of the Newark Police Department reduced the anticipated scope of the experiment. The grant funding this experiment provided funds for officers to work the experiment over 20 separate 4-h tours of duty. The Newark Police Department originally agreed to dedicate additional resources towards the experiment so that more tours-of-duty could be implemented. Unfortunately, prior to the start of the experiment extreme budget cuts led to the termination of 167 police officers in Newark (Star Ledger 2010). This led the newly appointed police leadership to determine that the Department could no longer afford to dedicate additional resources towards the experiment.

  19. Crime in both the treatment and catchment zones experienced a reduction (from 80 to 38 incidents and from 87 to 77 incidents, respectively) during the “residual days” period. However, the control area also experienced a sizable decline (from 42 to 28 incidents), which led to the negative WDQ.

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Acknowledgments

This research was supported by the National Institute of Justice, Grant Number 2010-IJ-CX-0026. We are truly indebted to a number of individuals at the Newark Police Department whose support made this project possible, including former Director Garry McCarthy, former Director Samuel DeMaio, former Chief-of-Staff Gus Miniotis, Captain Phil Gonzalez, Lieutenant Joseph Alferi, Lieutenant Brian O’Hara, Lieutenant Angelo Zamora, Sergeant Marvin Carpenter, and Sergeant Catherine Gasavage. We are especially grateful to the CCTV operators, patrol supervisors, and patrol officers who worked on the experiment for diligently carrying out their experimental tasks. We also thank Editor-in-Chief Lorraine Mazerolle, Associate Editor Emma Antrobus, and the anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. Department of Law and Police Science, John Jay College of Criminal Justice, 524 West 59th St., Haaran Hall, Room 422.14T, New York, NY, 100109, USA

    Eric L. Piza

  2. School of Criminal Justice, Rutgers University, Newark, NJ, USA

    Joel M. Caplan & Leslie W. Kennedy

  3. School of Criminal Justice, University of Cincinnati, Cincinnati, OH, USA

    Andrew M. Gilchrist

Authors
  1. Eric L. Piza
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  2. Joel M. Caplan
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  3. Leslie W. Kennedy
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  4. Andrew M. Gilchrist
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Correspondence to Eric L. Piza.

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Piza, E.L., Caplan, J.M., Kennedy, L.W. et al. The effects of merging proactive CCTV monitoring with directed police patrol: a randomized controlled trial. J Exp Criminol 11, 43–69 (2015). https://doi.org/10.1007/s11292-014-9211-x

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  • DOI: https://doi.org/10.1007/s11292-014-9211-x

Keywords

  • CCTV
  • Crime prevention
  • Randomized block design
  • Randomized field experiment
  • Video surveillance
  • Viewsheds