This paper reports on the results of an experiment examining the community impact of collaborative problem solving versus directed patrol hot spots policing approaches relative to standard policing practices. The focus is the impact on community perceptions of police.
We randomly assigned 71 crime hot spots to receive problem solving, directed patrol, or standard police practices. The data are a panel survey of St Louis County, MO, hot spots residents before the treatment, immediately following treatment, and 6 to 9 months later. Applying mixed effects regression, we assessed the impact on residents’ perceptions of police abuse, procedural justice and trust, police legitimacy, and willingness to cooperate with police.
The residents receiving directed patrol were most impacted, experiencing depleted growth in procedural justice and trust relative to standard practice residents and nonsignificant declines in police legitimacy immediately following the treatment period. However, in both cases, views recover in the long term, after treatment ends. Problem-solving residents did not experience significant backfire effects. There was no increase in perceived police abuse in the hot spots conditions. Both treatment group residents, in the long term, were more willing to cooperate with police.
Though there is strong evidence that hot spots policing is effective in reducing crime, it has been criticized as negatively impacting citizen evaluations of police legitimacy, and leading to heightened perceptions of police abuse. However, our results suggest that there is no long-term harm to public opinion by implementing problem solving or temporarily implementing directed patrol in hot spots.
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Since the survey was conducted with only 52 community residents that were purposely selected for their knowledge about and involvement in the community, they lacked statistical power to examine significant effects and the results are not representative.
The choice to use Part I and Part II incidents and not to focus solely on violent crime or prioritize a specific type of crime was a strong preference by the police agency. The Principal Investigator respected their preference, which appeared motivated by a desire to deal with problems that were common as well as problems that were very serious, thus reflecting a sense of equity. The choice to include a diversity of crime problems became apparent when officers conducting problem solving analyzed the nature of the crime problem. Some hot spots showed a burglary problem, others an assault problem, some primarily had youth problem behaviors, others had primarily narcotics and drug-related incidents, etc. As the nature of the DP or SPP treatment did not depend on the nature of the crime problem and, by design in PS sites, the response would be tailored to the nuanced nature of the specific crime problem, we did not feel that honoring this preference to allow all Part I and Part II offenses would alter our conclusions about how different types of policing in hot spots (SPP, DP, PS) affect residents’ perceptions of police.
We recognized that low response rates as well as attrition were likely among this difficult to reach population (Pashea and Kochel 2016). Statistical power in multilevel models is affected by the number of groups and the number of individuals within groups, as well as the expected variability within and across groups. Scherbaum and Ferreter (2009) found that, with 40 or more groups (hot spots), a medium effect size can be detected at a power level of .8 with as few as 7 subjects per group. In our case, subjects range from 6 to 29, with an average of 14 respondents per hot spot. Optimal Design software suggests that with 20 clusters, and an average of 14 subjects per cluster, we can detect a moderate effect (.5) at a power level of .8.
We did not force hot spots boundaries to align with apartment complex boundaries. In fact, most of the hot spots located within apartment complexes only contained a portion of the complex.
We included the maximum number of identified hot spots, including an unequal number of control sites because we had the resources to conduct the surveys to assess community impact at the 11 additional sites, and including these sites improved statistical power to assess community impact.
All surveys in wave 1 were in-person; a few surveys in waves 2 and 3 were conducted by phone at the request of the respondent.
Like others who have surveyed high crime areas, our response rate is not enviable (e.g., Ferguson and Mindel (2007) had a 33% response rate, Chermak et al. (2001) had a 31% response rate and a 49% cooperation rate, while Hinkle et al. (2013) had a 46.1% cooperation rate). See Pashea and Kochel (2016) for an explication of the difficulties of conducting surveys in high crime areas.
In the case of the model examining cooperation, the inclusion of the demographic factors caused significant model instability. Accordingly, our reported results do not include these co-variates. At the same time, the coefficients gained by including the co-variates and measuring North County as a fixed effect are similar to the ones reported in our analyses.
See Cohen et al. (1999) for a detailed discussion of the value of POMP as a meaningful measurement unit for the social sciences.
Likelihood ratio tests comparing the two-level to the one-level models for each outcome were statistically significant, revealing that the multilevel model was a better fit than a one-level model.
The exception is that, for two outcomes (legitimacy and satisfaction with treatment), we had to model North County as a fixed effect due to insufficient variation to properly model the random effect. Additionally, the address level is excluded from the random effects for three models where it lacked variability and so did not contribute to the model (legitimacy, cooperation, satisfaction during stops). As a sensitivity test, we also re-ran all models with North County as a fixed effect. The coefficients are similar to when we model North County as a random effect, although, as would be expected, the efficiency of the estimates of standard errors decline. Allison (2009: 32–34) reports that standard errors are often larger with fixed effects models than random effects, and that the random effects model will provide more efficient estimates.
This linear mixed effects analysis strategy is commonly used in experimental psychology. Gueorguieva and Krystal (2004), in reviewing the Archives of General Psychiatry in 2001, found that 30% of clinical trials used mixed-effects analysis, for many of the same reasons we do: (1) an interest in individual-level effects, (2) to handle repeat measures, (3) to accommodate missing data, (4) to account for nesting of the sample, and (5) the capacity to be able to add controls. Gueorguieva and Krystal (2004) explain, “Mixed-effects models use all available data, can properly account for correlation between repeated measurements on the same subject, have greater flexibility to model time effects, and can handle missing data more appropriately [e.g., than ANOVA]. Their flexibility makes them the preferred choice for the analysis of repeated-measures data” (p. 310).
Larger increases in time spent within hot spots than were provided in our treatment may be more noticeable and have larger effects. As it is, on average, hot spots that already experienced an average of 2.25 h of officer presence saw an increase of just over 1 h per week. This may provide a limited test.
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Kochel, T.R., Weisburd, D. Assessing community consequences of implementing hot spots policing in residential areas: findings from a randomized field trial. J Exp Criminol 13, 143–170 (2017). https://doi.org/10.1007/s11292-017-9283-5
- Hot spots policing
- Police legitimacy
- Procedural justice
- Police abuse
- Problem solving
- Directed patrol