Abstract
Objectives
To test the effects of short-term police patrol operations using license plate readers (LPRs) on crime and disorder at crime hot spots in Mesa, Arizona.
Methods
The study employed a randomized experimental design. For 15 successive 2-week periods, a four-officer squad conducted short daily operations to detect stolen and other vehicles of interest at randomly selected hot spot road segments at varying times of day. Based on random assignment, the unit operated with LPRs on some routes and conducted extensive manual checks of license plates on others. Using random effects panel models, we examined the impact of these operations on violent, property, drug, disorder, and auto theft offenses as measured by calls for service.
Results
Compared to control conditions with standard patrol strategies, the LPR locations had reductions in calls for drug offenses that lasted for at least several weeks beyond the intervention, while the non-LPR, manual check locations exhibited briefer reductions in calls regarding person offenses and auto theft. There were also indications of crime displacement associated with some offenses, particularly drug offenses.
Conclusions
The findings suggest that use of LPRs can reduce certain types of offenses at hot spots and that rotation of short-term LPR operations across hot spots may be an effective way for police agencies to employ small numbers of LPR devices. More generally, the results also provide some support for Sherman’s (1990) crackdown theory, which suggests that police can improve their effectiveness in preventing crime through frequent rotation of short-term crackdowns across targets, as it applies to hot spot policing.
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Notes
In the United Kingdom, this technology is referred to as automated number plate recognition technology (ANPR).
In addition to helping police detect auto thieves and other wanted persons, LPRs may also aid criminal investigations by providing records of vehicles that were in or near a crime location around the time of a criminal act.
A limitation to the use of LPR technology for apprehending vehicle thieves is that thieves may often abandon stolen vehicles before the vehicles are reported stolen and entered into police data systems. In Mesa, Arizona (our study location), we estimate that only one-third of auto thefts are reported within 3 h of occurrence, based on analysis of data from 2006 and 2007 (these are rough estimates because the time of many auto thefts can only be approximated). These delays reflect lags in the discovery of vehicle thefts (e.g., a vehicle stolen at night might not be discovered as missing until the following morning) as well as delays in reporting by victims after their discovery of a theft. Further, some vehicle thieves switch the license plates of stolen vehicles with those stolen from other vehicles; victims who have had their plates swapped for those of a stolen vehicle may be unaware of this for a long period, thus providing thieves with additional time to operate and abandon their stolen vehicles. For a discussion of other technical limitations to LPR technology, see Taylor et al. (2011a, 2012).
Note that the study tested the impact of the LPR patrols in hot spots relative to normal patrol. It did not assess hot spot patrols with and without LPRs, as we do here.
These locations are often nodes for business, leisure, and/or travel activities, and they have features or facilities that create criminal opportunities and facilitate offending (Eck and Weisburd 1995). In the language of routine activities theory (Cohen and Felson 1979), they are places that bring together motivated offenders, suitable targets, and an absence of capable guardians. Examples include locations with bars, convenience stores, parks, bus depots, apartment buildings, parking lots, shopping centers, motels or hotels, adult businesses, and the like (e.g., Braga et al. 1999: 551–552; Sherman et al. 1989: 45; see also Eck and Weisburd 1995).
Rice and Smith (2002), for example, report that vehicle theft is higher in areas close to pools of motivated offenders, where social control mechanisms are lacking, and where there are suitable targets such as bars, gas stations, motels, and other businesses. Further, a number of studies have identified non-residential locations as hot spots for vehicle theft, including: parking lots close to interstate highways (Plouffe and Sampson 2004), high-traffic areas (Rice and Smith 2002), areas near schools (Kennedy et al. n.d.), mall parking lots (Henry and Bryan 2000), and entertainment venues (Rengert 1996).
Similarly, in their study of CCTV in Cincinnati, Mazerolle et al. (2002) found short-term effects on anti-social behavior that they argued might be optimized by rotating CCTV across crime and disorder hot spots every 1–2 months.
One study of note, however, is Katz et al.’s (2001) evaluation of a quality-of-life police initiative in Chandler, Arizona, that was rotated multiple times across a number of target areas.
Indeed, there were a number of instances in which passers-by stopped to question the officers (sometimes in brazen ways) about what they were doing. On one occasion, a passing motorist circled one of the unmarked cars a number of times and threw rocks at it, not realizing that an officer was inside the vehicle (the windows were tinted). These incidents were perhaps manifestations of a heated public controversy that had arisen in Arizona around this time regarding the use of speed cameras on roadways.
Based on discussions with MPD and analysis of MPD data, we elected not to use the LPR devices in specific vehicle theft hot spots due to the lag that often occurs between the theft of a vehicle and the reporting of that theft to police (see note 3). Therefore, we focused on roads where auto thieves are most likely to drive stolen vehicles.
In defining the routes, we divided roads into smaller segments based on natural divisions (i.e., intersections and other natural breaks).
The journey after crime is an offender’s trip with the stolen vehicle in order to realize its expected utility, such as a trip to sell or strip the vehicle, a trip to another offense, or a joy-ride (Lu 2003). In a study of vehicle theft offenses in Buffalo, Lu (2003) found that vehicle thieves’ trips from vehicle theft locations to vehicle recovery locations were mostly local in nature, with travel distances significantly shorter than those of randomly simulated trips. Lu found that the difference in travel direction between observed and simulated trips was a combined result of both criminals’ spatial perceptions and a city’s geography (e.g., street networks). Lu thus recommended that police focus on nearby locations when responding to vehicle thefts.
Estimated vehicle theft trips on these roadways ranged from 2 to 57. This approach is not without its limitations given that it was based on recovered vehicles only, leaving out a considerable percentage of stolen vehicles that were never recovered. If the routes used to steal unrecovered automobiles differ systematically from those used to steal recovered ones, then our sample may not be a representative sample of all hot routes. But while this may affect the generalizability of the findings, it does not affect the internal validity of the study.
This analysis was conducted by Dr. Yongmei Lu of Texas State University.
At the outset of the project, the research team and the auto theft unit agreed that the officers would remain within approximately 500 ft of the main hot routes when working side streets, and these boundaries were drawn into maps that officers used during the operation.
Because some of the routes were in close proximity to one another (e.g., intersecting streets), crimes could occur in the buffer zones of multiple routes. When this occurred, the crimes were counted against each of the routes in question. Across the various categories of calls for service analyzed below, roughly one-quarter to one-third of the calls occurred within the buffer zones of multiple routes. This overlap balanced out across the experimental design and was thus uncorrelated with the treatment and control group designations.
The hot routes and their buffers cover 13 square miles, equating to approximately 10 % of the city’s land area.
It is worth noting that all three conditions (LPR, manual license plate checking, and the control group) received standard patrol services, but the control group received no other interventions beyond standard patrol services. The study was designed with three groups because our original objective was to assess the effectiveness of LPR technology independent of its use by a specialized unit. Therefore, we included two types of control groups: one that would be patrolled by a specialized vehicle theft unit doing manual license plate checks and another that would be patrolled by regular patrol officers (who may conduct license plate checks on a discretionary and ad hoc basis).
This type of randomized block design minimizes the effects of variability on a study by ensuring that like cases will be compared with one another (see Fleis 1986; Lipsey 1990; Weisburd 1993). Pre-stratification ensures that groups start out with some identical characteristics and will ensure that we have adequate numbers of places in each of the cells of the study.
The LPR and manual routes were scheduled in alternating order each day (i.e., the officers would work an LPR route, followed by a manual route, followed by another LPR route, etc.). On some days, the unit could not work all scheduled routes due to special circumstances (such as making an arrest that took the unit out of commission for the rest of the shift). In these instances, the unit resumed patrolling the next day according to the schedule set for that day. These deviations cancelled out over the course of the experiment so that the unit spent equivalent amounts of time working LPR and manual check routes.
The research team developed the study procedures in consultation with the auto theft unit and there were no changes to the operation during the course of the study. The composition of the auto theft unit also remained the same throughout the study with the exception that the unit’s supervisory officer changed early in the project.
During 2007 and 2008, the hot routes experienced an average of about three UCR part I incidents per bi-weekly period (including both violent and property crimes combined). These base rates declined further during the study period, as Mesa experienced a general decline in crime. For the CFS categories we examined, the hot routes had base rates ranging from less than one to about six calls per bi-weekly period (see Table 2).
Data points for the weeks before and after the experiment were included in order to examine pre-post changes and lagged effects for routes that were treated during the first and last periods of the experiment.
Our desire to boost the statistical power of the analysis by pooling the data stemmed from the fact that the auto theft unit operated at each hot route for only 2 weeks. Disaggregating the data down to these very small locations and short time intervals resulted in relatively “noisy” data, with outcome measures that have very low means and standard deviations that are large relative to their corresponding means (see Table 2). Under such conditions, even relatively large percentage reductions in an outcome measure may produce only small standardized effects (Cohen 1988). Preliminary calculations with some of these measures suggested, for example, that reductions on the order of 20 % might produce only standardized effects in the range of 0.10–0.20 or less for ANOVA analyses. Detecting such effects at standard levels of statistical significance (and with a design having the customarily preferred statistical power level of 80 %) would require sample sizes with hundreds in each group (calculated using GPower software; see Faul et al. 2007).
As shown in Table 2, calls for service generally declined in the LPR and manual check routes during the post-intervention weeks. However, this pattern may be misleading because crime was declining in Mesa throughout the study period. Post-intervention call levels in the intervention routes were generally more comparable to those in the control routes, averaged over all weeks of the study period. In the models below, we examine these patterns more rigorously by controlling for common time trends and other noted factors.
Our use of the random effects approach allows for dependence between observations from the same route and assumes that unmeasured differences between routes are uncorrelated with the treatment effects by virtue of the experimental design. We estimated the models using STATA 10.1 xt commands for cross-sectional time series data.
Hence, the routes randomly assigned to the LPR group were by chance locations with somewhat higher crime levels. As shown in Table 2, the bi-weekly averages of these call categories had the following ranges across groups during the seasonal lag period: 0.55–0.86 for auto theft, 5.34–6.38 for property crime, 5.88–6.76 for disorder, and 0.61–0.91 for drug incidents.
As shown in the illustration of hypothetical hot routes, the intervention observations in the models of long-term effects are disproportionately clustered near the end of the study period. As a result, models of long-term intervention effects that do not include the time trend variable produce numerous coefficients suggesting that both patrol interventions produced long-term crime reductions. Most of these results change when the time trend indicator is added. In contrast, inclusion of the time trend indicator has no effect on the results of models estimating short-term intervention impacts (in these models, the intervention observations are randomly dispersed in time throughout the study period).
Although not strictly necessary, the introduction of these covariates allows us to potentially improve the precision of the treatment comparisons by reducing error variance and correcting for any imbalances in the distribution of these covariates across the treatment and control groups that may have occurred due to chance (Armitage 1996). Adding covariates can also help adjust for the natural variation between cases within the comparison groups (Gelber and Zelen 1986).
Less than 2 % of the hot route/bi-weekly observations had more than 1 adjacent route receiving treatment simultaneously (nearly all were instances in which two adjacent routes were being treated). Additional analyses differentiating between LPR and manual check patrols in adjacent routes produced similar effects for both types of patrol. In the interest of parsimony, the models presented in the text include only one indicator for the presence of either type of patrol.
The unit recovered 10 stolen vehicles when using the LPR devices and 5 when doing manual license checks (they also detected 8 stolen license plates when using the LPRs).
The magnitudes of the estimated displacement effects were smaller than those of the estimated reductions in the hot routes, suggesting that the former did not completely offset the latter. However, given the imprecision of the displacement analyses, we have not tried to formally quantify these effects relative to one another.
More generally, there has been relatively little research on the impacts of technology in policing (beyond technical, efficiency, or process evaluations), and that which does exist suggests that technology does not necessarily bring about desired crime reduction benefits (Koper et al. 2009; Lum 2010; Manning 1992). Accordingly, there is a need to better understand both how technology affects various organizational and behavioral aspects of policing and how, in turn, these and other factors shape the uses and effectiveness of policing technology (e.g., see Chan 2001, 2003; Koper and Lum 2010).
This conclusion may also be contingent on the types of locations where police focus their efforts. In another phase of this study, Mesa officers conducted similar LPR patrols over larger areas, averaging about one square mile in size. Neither LPR nor manual check patrols produced measurable declines in auto theft in these areas (impacts on other crimes were not examined) (Taylor et al. 2011a). Consistent with research on hot spots policing more generally, this supports the merits of focusing LPRs on well defined micro-places (in this case, high-risk road segments).
After the study, the auto theft unit conducted a number of multi-week “freestyle” operations in which their activities were guided by recent auto theft and traffic patterns. During these periods, they recovered stolen automobiles at a rate four to five times as high as during the experiment. This does not invalidate our findings, given that the LPR, non-LPR, and control routes and times were randomly selected for the experiment and that the unit faced the same constraints when working LPR and non-LPR locations (hence, our study provides valid estimates of the effects of the patrols). However, this does provide some evidence that police can potentially recover more vehicles and apprehend more persons of interest under normal operating conditions.
In the United Kingdom, for example, police have a nationally integrated system for LPR data analysis and storage that receives up to 14 million reads per day from over 10,000 LPR devices (both fixed and mobile) throughout England and Wales and that matches them to a wide variety of data sources (Kable 2010; PA Consulting Group 2006). New York City, to provide another illustration, had 238 fixed and mobile LPRs linked to data on stolen automobiles, wanted persons, and unregistered vehicles as of April 2011 (Baker 2011).
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Acknowledgments
This project was supported by grant 2007-IJ-CX-0023 awarded by the National Institute of Justice (Office of Justice Programs, U.S. Department of Justice). The authors thank the Mesa, AZ Police Department (MPD) for its strong commitment to the project. We especially thank the auto theft unit officers, (Officers James Baxter, Joel Calkins, Stan Wilbur, and Brandon Hathcock), supervisory officer Cory Cover, Deputy Chief John Meza, and other MPD commanders. Also, the authors are very appreciative of Dr. Yongmei Lu for her work conducting geographic analyses. Finally, the authors thank David Weisburd and other anonymous peer reviewers for their helpful comments on an earlier version of this paper. The views expressed here are those of the authors and should not be attributed to the U.S. Department of Justice, the Mesa, AZ Police Department, the authors’ respective institutions, or any of the aforementioned individuals.
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Koper, C.S., Taylor, B.G. & Woods, D.J. A randomized test of initial and residual deterrence from directed patrols and use of license plate readers at crime hot spots. J Exp Criminol 9, 213–244 (2013). https://doi.org/10.1007/s11292-012-9170-z
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DOI: https://doi.org/10.1007/s11292-012-9170-z