Abstract
Focusing police efforts on “hot spots” has gained acceptance among researchers and practitioners. However, little rigorous evidence exists on the comparative effectiveness of different hot spots strategies. To address this gap, we randomly assigned 83 hot spots of violence in Jacksonville, Florida, to receive either a problem-oriented policing (POP) strategy, directed-saturation patrol, or a control condition for 90 days. We then examined crime in these areas during the intervention period and a 90-day post-intervention period. In sum, the use of POP was associated with a 33% reduction in “street violence” during the 90 days following the intervention. While not statistically significant, we also observed that POP was associated with other non-trivial reductions in violence and property crime during the post-intervention period. In contrast, we did not detect statistically significant crime reductions for the directed-saturation patrol group, though there were non-significant declines in crime in these areas during the intervention period. Tests for displacement or a diffusion of benefits provided indications that violence was displaced to areas near the POP locations, though some patterns in the data suggest this may have been due to the effects of POP on crime reporting by citizens in nearby areas. We conclude by discussing the study’s limitations and the implications of the findings for efforts to refine hot spots policing.
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Notes
However, this does not tell us whether violent places are more likely to be clustered near one another, which is another important consideration (Groff et al. 2010).
As we discuss below, POP is a strategy that calls for police to analyze and address underlying issues that contribute to chronic crime and disorder problems.
To varying degrees, police conducted these three types of activities at all of the target hot spots. Hence, the comparisons between these categories were not based on experimental assignment.
More informal evidence on the most effective hot spots strategies comes from a recent survey with a national convenience sample of police agencies affiliated with the Police Executive Research Forum (PERF), a membership organization for police chiefs and sheriffs in medium to large jurisdictions. Respondents indicated that the strategies they use most commonly and find most effective at violent crime hot spots include directed patrol, targeting known offenders, problem solving, and community partnerships (Koper 2008; also see Police Executive Research Forum 2008). As noted earlier, however, the respondents’ used the term hot spot in a broader sense than it is used here.
Note that our discussion focuses on directed patrol studies involving micro hot spots of the sort discussed previously. See Sherman et al. (2002), for example, for a review of the broader body of research on directed patrol.
The comparison areas also experienced a drop in violence that was two-thirds as large as that in the target locations, but this change was not statistically significant. The authors also examined changes in crime at the city level but found that the program effects were not large enough to produce a statistically significant reduction in violence for the city as a whole (438).
Results were overwhelmingly positive and showed much larger effects in 45 less rigorous POP studies examined by Weisburd et al. (2010).
Some might argue that hot spots policing, in any form, is a variety of POP in that it involves targeted responses based on crime analysis.
The length of the intervention period was reported in Braga (2007: 29).
U.S. Census (2006). July 1, 2006 estimates. Retrieved March 10, 2010, from www.census.gov/popest/cities/tables/SUB-EST2006-01.xls.
U.S. Census (2008). Annual estimates of the population of metropolitan and micropolitan statistical areas:
April 1, 2000 to July 1, 2008. US Census Bureau. Retrieved March 10, 2010, from www.census.gov/popest/metro/tables/2008/CBSA-EST2008-01.xls.
Prior studies of hot spots policing have generally lacked specific information about officer-hours committed to the intervention, thus precluding contrasts between the intensity of those efforts and that in Jacksonville.
JSO used a mix of on-duty officers and officers on overtime.
This is based on 30 officers working the POP locations per day for a 10-h shift, 7 days a week.
We discussed the option of an “override process” as a safety valve for the JSO. That is, if a location is deemed by the Sheriff to require an intervention, then that place will receive it. Despite this option, no “overrides” were deemed necessary by the JSO.
It is worth noting that all three conditions (saturation, problem-solving, and the control group) received standard patrol services, except the control group received no other interventions beyond standard patrol services.
Based on collection of data at the area level, we did not have any missing data for these measures.
The control group hot spots were 40% residential, 37.5% business, and 22.5% mixed use. The saturation group hot spots were 24% residential, 29% business, and 47% mixed use. The POP hot spots were 36% residential, 27% business, and 37% mixed use.
The average size of the control, saturation, and POP spots were, respectively, .22 square miles, .23 square miles, and .28 square miles.
We did not use ordinary least squares (OLS) regression because of the limited distribution of our data (most hot spots had a few crimes occur and very few had more than ten occur), and the potential for violating the normality assumption of OLS regression. Also, in these types of cases OLS can yield negative predicted values and inefficient, inconsistent, and biased estimates (Long 1997).
All count models were estimated using STATA 10.1 xt commands for cross-sectional time series data.
For example, when our outcome variable is police self-initiated activity, we have independent variables for police self-initiated activity the year before the intervention and police self-initiated activity during the 90-day period immediately before the experimental period.
For instance, when our outcome variable is calls-for-service 90 days after the treatment period we have an independent variable for calls-for-service during the 90-day experimental period.
Because some of our hot spots were in close proximity to one another, CFS/incidents could occur in the buffer zones of multiple hot spots. When this occurred, each individual CFS/incident was counted against each nearby hot spot. Approximately, one-third of the hot spots had overlap in their buffer zones, but the distribution of this overlap was approximately equally across the assigned treatments. We conducted some sensitivity analyses to determine if this double-counting unduly affected our results. First, we excluded two hot spots (both assigned to POP) that had near 50% overlap in their displacement counts and found our results did not change. Second, we constructed both a percent overlap indicator and a dichotomous overlap indicator to be included in separate analyses. Neither of these variables was significant nor had an appreciable effect on the assigned treatment estimates. Finally, we tested interaction terms between the above overlap variables and assigned treatment and found that these analyses also did not change our substantive conclusions about the impact of assigned treatment on our crime impact outcomes.
We thank an anonymous peer reviewer of an earlier version of this paper for this comment.
The average number of UCR non-domestic violent incidents in the POP locations was 2.9 during the post-intervention period (see Table 1). Our model implies that this represents a 33% reduction from what would have been expected without the intervention. From this, we estimate that the POP intervention prevented an average of roughly 1.4 crimes per hot spot, or about 31 crimes across the 22 POP locations.
Using GPOWER software (Erdfelder et al. 1996), we conducted a power test examining differences of means with an ANOVA test. Detecting a drop of approximately one-third in our violence measures (a “small” standardized effect size of 0.17 based on our sample sizes and standard deviations) with an alpha level of 0.05 and 80% power would require a total sample size of 339 cases. With our actual sample size (83 cases), we have an 80% chance of detecting a medium to large effect size of .35 with an alpha level of 0.05.
We calculated these effect sizes using data reported by Braga et al. (1999: 563). They also found significant reductions of 18 to 40% in property crime.
This notion is also consistent with the more general body of studies on directed patrol, many of which suggest that directed patrol is an effective strategy for reducing crime (e.g., see review in Sherman et al. 2002). Prior directed patrol studies have generally focused on well-defined but larger areas than those examined in this study. Hence, the strategy may be more optimal when officers can cover larger numbers of hot spots over somewhat larger areas.
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Acknowledgements
The authors thank the Jacksonville Sheriff’s Office for its strong commitment to the research project throughout the organization including the crime analysis unit, managers Matt White and Jamie Rousch, the Operation Safe Street officers, and other JSO commanders. Also, we are very appreciative of former PERF research fellows Rachel Bambery of the New Zealand Police Service and Sergeant Jeff Egge of the Minneapolis Police Department for their assistance with different stages of the project.
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This project was supported by contract number 9237-9107 awarded by the Jacksonville Sheriff’s Office to the Police Executive Research Forum through a Bureau of Justice Assistance, Office of Justice Programs, U.S. Department of Justice grant. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the Jacksonville Sheriff’s Office, the U.S. Department of Justice or any other organization.
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Taylor, B., Koper, C.S. & Woods, D.J. A randomized controlled trial of different policing strategies at hot spots of violent crime. J Exp Criminol 7, 149–181 (2011). https://doi.org/10.1007/s11292-010-9120-6
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DOI: https://doi.org/10.1007/s11292-010-9120-6