To examine the impacts of broken windows policing at crime hot spots on fear of crime, ratings of police legitimacy and reports of collective efficacy among residents of targeted hot spots.
A block randomized experimental design with a police intervention targeting disorder delivered to 55 treatment street segments with an equal number of segments serving as controls. Main outcomes were measured using a panel survey of 371 persons living or working in these sites.
The broken windows police intervention delivered to crime hot spots in this study had no significant impacts on fear of crime, police legitimacy, collective efficacy, or perceptions of crime or social disorder. Perceptions of physical disorder appear to have been modestly increased in the target areas.
The findings suggest that recent criticisms of hot spots policing approaches which focus on possible negative “backfire” effects for residents of the targeted areas may be overstated. The study shows that residents are not aware of, or much affected by, a three hour per week dosage of aggressive order maintenance policing on their blocks (in addition to routine police responses in these areas). Future research needs to replicate these findings focusing on varied target populations and types of crime hot spots, and examining different styles of hot spots policing.
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For selection of study street segments, disorder was defined to include all calls for service for prostitution, drug possession, disturbing the peace, vandalism, public drinking, misdemeanor DUI, noise complaints, fights, and thefts from automobiles. We use a different measure of disorder in our analyses in this study, as we later decided that DUI, fights and automobile burglaries did not fit with Wilson and Kelling’s (1982) concept of disorder. However, for selecting the study sites, they were considered as good proxies as places with such problems were deemed likely to also have issues with other types of social disorder (loitering, panhandling, vagrancy, etc.) which do not tend to generate many police calls for service.
Part 1 crime included the FBI defined part 1 offenses, excluding thefts from autos which were included as a disorder for site selection purposes as outlined in footnote 1.
For Redlands and Colton, we were able to bolster the PowerFinder database with a list of phone numbers on the study blocks provided by each city’s water department. We were unable to obtain such data for Ontario, but this was less of a concern as the number of phones per street segment in the PowerFinder data were higher in Ontario than the other two cities.
The survey was first piloted in a city removed from the study. All interviewers were required to satisfactorily complete pilot shifts before calling respondents in the study sample.
The cooperation rate excludes cases that were coded as chronic no answer/busy/answering machine (n = 307) and cases where there was a language (not an English or Spanish speaker) or cognitive barrier (n = 59) from the denominator.
Specifically, a study using random digit dialing in the state of Kentucky had a response rate of 27.5% (Rader et al. 2007), a study of fear in Dallas neighborhoods had a response rate of 33.4% (Ferguson and Midel 2007), and a study by Xu et al. (2005) achieved a response rate of 60%, and a study in Philadelphia had a response rate of 77% (Wyant 2008). It is worth noting that the Xu et al. data was collected by a police department (and respondents may be less likely to refuse a survey collected directly by the police) and the Wyant study involved a $10 monetary reward for respondents to encourage participation.
The crimes asked about included: robbery, assault (attacked by stranger), murder, sexual assault, burglary, car stolen, vandalism
The perceived social disorder measure includes: fist fights, people loitering or being disorderly, public drinking, drunk or high in public, panhandlers, vandalism, people making too much noise late at night/early morning, gambling in the street, drug sales, and prostitution.
The perceived physical disorder measure includes: broken windows, graffiti, abandoned or boarded-up buildings, vacant lots, abandoned cars, litter, street or sidewalks in need of repair, and areas in need of better lighting.
The perceived crime measure included: cars being broken into, burglary, robbery, shooting guns in public, sexual assaults.
These power estimates were calculated using the Optimal Design software.
This power analysis was conducted in the Power and Precisions Software, and based on t tests assuming no change from the pre-intervention mean in the control areas, and hypothesized changes from the pre-intervention mean in the target areas.
These results are based on regression models that include treatment, city, pre-crime counts, and an interaction of treatment and pre-crime counts. In the simple analyses of all cases only for crime outcomes, we do find a significant effect for treatment and the interaction of treatment and crime baseline outcomes. However, a sensitivity analysis shows that this effect is highly unstable and impacted strongly by outliers.
As such there is some nesting of the data. However, given the small number of subjects per street segment, there are not enough cases in the level-one clusters to justify hierarchical data modeling. The mean number of subjects per segment is 3.5 with a range of 1 to 10. Sixteen segments have only one respondent, which means there is no within-cluster variation for nearly 15 percent of our segments. See Silver (2000) for a similar justification for not using multilevel modeling with a low number of subjects per level-two unit.
We think that this approach is reasonable given our assumption that the treatment is delivered relatively similarly across sites and the balance of treatment and control cases in the survey. Nonetheless, we recognize that treatment was randomly allocated at the street-segment level and not the individual level. In order to examine the outcomes of the experiment strictly at that level, we also aggregated outcome variables to the segment level and ran the analyses on those variables as a sensitivity check. All results were substantively identical to the individual-level findings presented below in terms of direction of changes, and significance levels were also very similar. As an additional sensitivity test, we also analyzed the data using repeated measures split plot ANOVA tests with the pre- and post-intervention outcome variables as the within subjects repeated factor and the treatment and city as between subjects factors. These analyses were also substantively identical to both the analyses below and the segment-level analyses.
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This research was supported by grant no. 2007-91116-MD-IJ from the National Institute of Justice. The authors would like to thank Chief Jim Bueerman of the Redlands Police Department, Chief Jim Doyle of the Ontario Police Department and Chief Bob Miller of the Colton Police Department for their willingness to participate in an experimental study, and for their support throughout the project. Appreciation is also due to a number of graduate students who assisted with the project: Jill Christie for her tireless work supervising the survey data collection and data entry; Julie Willis for her invaluable assistance with data cleaning and geocoding; and Cody Telep, Dave McClure and Breanne Cave for their valuable comments on, and edits to, early drafts of this manuscript. Finally, thanks are also due to the anonymous peer reviewers for their helpful feedback.
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Weisburd, D., Hinkle, J.C., Famega, C. et al. The possible “backfire” effects of hot spots policing: an experimental assessment of impacts on legitimacy, fear and collective efficacy. J Exp Criminol 7, 297–320 (2011). https://doi.org/10.1007/s11292-011-9130-z
- Hot spots policing
- Broken windows
- Fear of crime