Abstract
Objectives
To determine whether the use and display of license plate readers (LPRs) enhance the crime prevention effects of police patrol, particularly by increasing initial and residual deterrence from patrol presence.
Methods
Crime hot spots in a large suburban jurisdiction were randomly assigned to receive intermittent patrols (15–30 min each) by officers with or without LPRs on their vehicles for 4.5 months. Data on 785 patrols at 33 hot spots were used to compare initial and residual outcomes of LPR and non-LPR patrols. Outcomes analyzed for each patrol visit via chi-square tests, survival analyses, and/or logistic regression included the following: the likelihood of a vehicle recovery or arrest; the likelihood of a new crime or disorder call in the location while the officer was present (measuring initial deterrence); time until a new crime or disorder call following the officer’s departure (measuring residual deterrence); and first type of call (crime or disorder) following the officer’s departure (measuring residual deterrence).
Results
LPR use increased stolen vehicle recoveries but not arrests. LPR use did not reduce the likelihood of a new call in the hot spot while an officer was present, nor did it affect the timing or seriousness of the next call following a patrol.
Conclusions
This study provides little clear evidence for the crime prevention efficacy of using LPRs in general patrol, which is a common practice in the USA. Police and researchers should give more attention to testing the relative benefits of different LPR uses and modes of deployment.
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Notes
Like prior LPR patrol studies, this study examines LPR use in the context of geographically focused, “hot spot” policing—i.e., police efforts focused on small areas or very specific places (e.g., addresses, intersections, and street blocks) where crime is concentrated (e.g., Sherman et al., 1989; Weisburd et al., 2004). Evidence from numerous studies indicates that directed patrol and other police interventions reduce crime and disorder at such locations without obvious signs of displacement (for reviews, see Braga et al., 2019a; Lum & Koper, 2017; National Academies of Sciences, Engineering, and Medicine, 2017; Telep & Weisburd, 2012). Hot spots are logical places for deployment of LPRs, whether for auto theft detection or other proactive activities. Like many crimes, auto theft is concentrated geographically in a manner linked to various environmental characteristics (Barclay et al., 1995; Fleming et al., 1994; Plouffe & Sampson, 2004; Potchak et al., 2002; Rengert, 1996; Rice & Smith, 2002). Hence, the likelihood of detecting both stolen vehicles and wanted persons through the use of LPRs would seem to be greatest in hot spot locations. In addition, focusing LPR use in hot spots should also maximize the deterrent value of displaying the cameras.
To the extent that CCTV helps police solve a greater share of criminal investigations, crime prevention impacts associated with CCTV could reflect incapacitation as well as deterrence effects. However, evidence on how CCTV affects investigations is very limited (Ashby, 2017).
We reference the agency anonymously based on an agreement made with the agency at the start of the project.
These capabilities are relatively advanced compared to those of other American agencies with LPRs (see Lum et al., 2019).
This level of LPR deployment is typical of what would be available in many American jurisdictions. Among large US police agencies that own LPRs, three-quarters own fewer than 8 units, 90% own less than 15, and only 5% own more than 25 (Lum et al., 2019). Further, US agencies use half of their LPRs for purposes other than general patrol. For further discussion of the potential benefits and limitations of large-scale LPR deployment, see Koper and Lum et al. (2019) and Willis et al. (2018), as well as LPR evaluations conducted in the UK (PA Consulting Group, 2004, 2006).
Types of incidents analyzed included homicides, robberies, assaults, domestic violence incidents, sex offenses, juvenile-related offenses, thefts, auto thefts, burglaries, and other criminal incidents.
Parameters were specified as 0.1 square miles for cell size and 0.1 mile for search radius. In deciding on the desired size of the hot spots, we considered multiple factors. Selecting smaller locations in general maximizes the effects of patrols by targeting them more precisely on the highest risk locations and maximizing officers’ ability to affect perceptions through visibility. Also, we sought to define hot spots that were small enough for officers to cruise them multiple times in their entirety during a short patrol visit (e.g., 15 min). At the same time, we selected hot spots larger than the sorts of micro places used in some hot spot studies (e.g., specific blocks or intersection areas). This was based in part on the desire to maximize the base rates of crime in the chosen hot spots (for more robust statistical analyses). In addition, it was felt that making the hot spots somewhat larger than a micro place would enable the LPR officers to better capitalize on the value of their LPRs by giving them the opportunity to scan a wider range of vehicles in and around specific micro hot spot street segments and intersections. This style LPR hot spot patrol has appeared more effective for detecting problematic vehicles in other settings (Koper et al., 2019).
The project team sought to draw boundaries that closely corresponded to the natural layout of each location and to avoid creating multiple artificial hot spots out of one connected area. We say more below about the proximity of some hot spots and the potential for spillover effects.
Most of the commercial locations were mixed-use locations with businesses and residences.
To gain some further sense of the comparability of the LPR and non-LPR hot spots, we also examined the social characteristics of the Census tracts in which they were located. Tracts containing LPR locations did not differ from those containing only non-LPR locations with respect to unemployment, household income, poverty levels, racial and ethnic diversity, and residential stability. Census tracts with LPR hot spots as initially assigned had lower levels of home ownership. However, this difference was diminished and not statistically significant after adjusting for the final sample of hot spots used in the analysis (see below on final sample adjustments).
Patrol officers in this agency typically worked 4–5 days in a row followed by 3–4-days off.
Officers were unable to conduct the patrols as frequently as planned due to several factors, namely, temporary or permanent reassignments, vehicle breakdowns or damage, periodic in-service training, annual leave, and other service demands (e.g., heavy call volume).
As noted, the hot spots were identified based on more serious criminal incidents in order to ensure that the study locations would be places with both serious and minor crime and disorder issues. For the outcome analyses, we examined measures of overall crime and disorder as well as measures of more serious offending based on previously discussed literature on the effects of LPRs and other surveillance technologies. For this purpose, we utilized calls for service data, which provide the widest net of problem behaviors that come to the attention of police and are especially useful for measuring minor offenses and disorderly behaviors that are less likely to result in official reports or arrests (Sherman et al., 1989; Warner & Pierce, 1993). Calls for service are thus widely used in studies of crime and policing (e.g., Braga & Bond, 2008; Braga et al., 1999; Kochel et al., 2015; Koper et al., 2013; Mazerolle et al., 2000; Sherman & Weisburd, 1995; Weisburd & Green, 1995; Wu & Lum, 2017; but see Klinger & Bridges, 1997 on potential sources of error in calls for service). The use of calls for service was also necessary for this study in order to measure each recorded police stop at the study locations, including proactive stops as well as stops in response to citizen calls (see below on the measurement of survival times to the next police presence or the next criminal or disorder event).
Although residual deterrence from a police patrol might conceivably last beyond the occurrence of the next crime, disorder, or police presence event, the immediate period from the end of the patrol until the next event should provide the clearest measure of this residual effect. Afterwards, the measurement of a patrol’s residual deterrence will be confounded by the impacts that subsequent crimes, disorders, and/or police actions have on future events at the location. This method of measuring residual deterrence also has practical and operational significance, as it reflects time until the next event that will require a police response.
In the life table method, the analyst groups the event times into intervals of a chosen length—in this application, hours—and calculates St, which is the probability that the case “survived” (i.e., did not experience the event of interest) to the start of interval t. For each interval, the value of St is based on the probabilities of events occurring in prior intervals. For example, the probability of surviving to the third interval or beyond would be the product of (1−q1)(1−q2), where q1 and q2 represent the probabilities of events occurring during intervals 1 and 2, respectively. For a given interval, the probability of an event (conditional on survival to the start of the interval) is denoted as
q = d / (n − m/2), where d equals the number of events occurring during the interval, n refers to the sample at risk at the start of the interval (i.e., the number of cases that have not experienced an event or been censored by the start of the interval), and m is the number of cases censored during the interval (Teachman, 1983:270). For further discussion of the life table method, see Allison (1995) and Teachman (1983).
The Cox proportional hazards model is often expressed as: hi(t) = λ0(t)exp(B1xi1+…+Bkxik), where hi(t) represents the hazard for subject i at time t, λ0(t) represents a baseline hazard function (which can be regarded as the hazard function for a subject whose covariates all have values of zero), xi1 through xik represent a set of fixed covariates, and B1 through Bk represent the effects of those covariates (these effects are then exponentiated) (Allison, 1995: pp. 113–114). The model assumes that the ratio of the hazards for any two subjects remains constant over time (i.e., that they remain proportional to one another), but makes no assumption about the distribution, or shape, of the baseline hazard rate.
It is commonplace for criminal offenders to engage in a variety of criminal and other anti-social behaviors (e.g., see Gottfredson & Hirschi, 1990). Accordingly, driving troublesome people away from a location or otherwise moderating their conduct could conceivably affect various problem behaviors at that location.
The officer’s presence and time of arrival were determined using the on-scene time for these calls.
In a logistic regression model for a binary outcome measure, the model variables are used to predict the natural logarithm of the odds of the outcome of interest. The odds are defined as p / (1−p), where p is the probability of the outcome of interest. Exponentiating the model coefficients reveals each variable’s multiplicative effect on the odds of the outcome. Here, the outcome of interest is a failure due to a criminal event as opposed to a disorder event.
Results are based on life tables constructed with 1-h intervals. The estimates of a crime or disorder failure are based on 1 minus the likelihood that the case would “survive” without a crime or disorder to the selected hour, with the difference then converted to a percentage.
LPR officers reported issuing citations or tickets in 15% of their hot spot patrols, in contrast to non-LPR officers who reported doing so in 7% of their patrols. Non-LPR officers, on the other hand, reported making premise checks during 11% of their patrols and investigating suspicious persons or vehicles during 15%. The comparable figures for LPR officers were approximately 1−2%.
With regard to cost efficiency considerations, reported LPR costs range from $10,000 to $25,000 per unit for mobile units, with fixed costs as high as $100,000 depending on where they are placed (Gierlack et al., 2014). Additional long-term operational and maintenance costs must also be considered.
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Acknowledgments
This project was supported by Grant 2013-IJ-CX-0017 from the National Institute of Justice (Office of Justice Programs, U.S. Department of Justice). The authors are also grateful to the agency and individual officers that took part in this project.
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Koper, C.S., Lum, C., Wu, X. et al. Do license plate readers enhance the initial and residual deterrent effects of police patrol? A quasi-randomized test. J Exp Criminol 18, 725–746 (2022). https://doi.org/10.1007/s11292-021-09473-y
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DOI: https://doi.org/10.1007/s11292-021-09473-y