There is growing evidence that crime is strongly concentrated in micro-geographic hot spots, a fact that has led to the wide-scale use of hot spots policing programs. Such programs are ordinarily focused on deterrence due to police presence, or other law enforcement interventions at hot spots. However, preliminary basic research studies suggest that informal social controls may also be an important mechanism for crime reduction on high crime streets. Such research has been hindered by a lack of data on social and attitudinal characteristics of residents, and the fact that census information is not available at the micro-geographic level. Our study, conducted in Baltimore, MD, on a sample of 449 residential street segments, overcame these limitations by collecting an average of eight surveys (N = 3738), as well as physical observations, on segments studied. This unique primary data collection allowed us to develop the first direct indicators of collective efficacy at the micro-geographic level, as well as a wide array of indicators of other possible risk and protective factors for crime. Using multilevel negative binomial regression models, we also take into account community-level influences, and oversample crime hot spots to allow for robust comparisons across streets. Our study confirms the importance of opportunity features of streets such as population size and business activity in understanding crime, but also shows that informal social controls, as reflected by collective efficacy, are key for understanding crime on high crime streets. We argue that it is time for police, other city agencies, and NGOs to begin to work together to consider how informal social controls can be used to reduce crime at residential crime hot spots.
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The initial threshold for violent and drug crime was 18 drug calls and 19 violence-related calls, respectively--approximately the top 2.5% of segments in the city for each category. We chose this threshold because it was about midway between the 1% and 5% thresholds that have often been used to define crime hot spots (e.g., see Weisburd 2015). Although, this was the final threshold for the combined violent and drug crime hot spots, to meet sampling goals for streets that were hot spots of violence or hot spots of drug crime the threshold was reduced to 17 violent calls and 16 drug crime calls respectively (approximately the top 3% of all city street segments in that category). We also required that streets evidence drug or violent crime throughout the year by setting a criterion that calls be spread across at least 6 months. For full details regarding the methodology of this study, see https://cebcp.org/wp-content/uploads/2020/07/NIDA-Methodology.pdf.
The survey instrument was adapted from different surveys used in the social sciences, particularly communities and crime research, as well as health surveys. Different surveys included the Project on Human Development in Chicago Neighborhoods: Community Survey, the National Crime Victimization Survey, the RAND 36 Item Health Survey, the HCSIS Baseline Questionnaire, the National Survey on Drug Use and Health, the National Youth Panel, among others. Question indexes were written based on prior research and scale development. Focus groups were used during the development of the survey, and the instrument was reviewed by experts, pilot tested in another city, and revisions made prior to the start of the survey in Baltimore.
With a street segment population of 25 households for example, the variance of the estimator is between 20 and 28% of the within-street segment variance of the variable in question at a sample size of between 10 and 7.
All street segments are included in the final analyses because there are no missing values for variables at the street segment level. It is also the case that there were very few missing values for measures from the survey that are aggregated up to develop street segment estimates. Except for income, which included 26% missing values, the maximum percentage for other measures utilized was below 3%. To create street level measures, we averaged individual responses.
This business activity measure was highly correlated (r = 0.7) with our measure of percent commercial buildings collected during the physical observations. Results do not change substantively when using either measure of business activity.
Because our street segments only represent 449 out of 25,045 streets in the city, Moran’s I was not an appropriate statistic for estimating the impacts of spatial auto correlation on our estimates. For example, using a 1/4 mile distance, a number of our streets do not have a neighbor in the data. We ran Spatial Lag and Spatial Error models in GeoDa for the one-level model with the dependent variable, 2017 crime incidents, logged, including the same level-1 independent variables as in our multi-level model, and the main findings are similar (output available by request). We report the negative binomial multilevel model because of the value added from the multilevel negative binomial modeling component.
The boundaries of 26 streets fell in more than one CSA. We used the approach of placing such streets in the CSA where the largest proportion of the street was found. To address whether the definition of CSA impacted our analyses, we also ran the regressions with these streets removed. We do not observe meaningful differences in these analyses as contrasted with those reported in the article.
While we cannot compute an ICC for this model (see Raudenbush and Bryk 2002), it is possible to calculate the ratio of the variability in the community-level means in the logged crime incident to the total variability in logged crime incidents. For our data, this value is 0.325, indicating that 32.5% of the variability in logged crime incidents is at the community-level.
In a paper published in the British Journal of Criminology, Weisburd, White and Wooditch (2020) examined the correlation of collective efficacy to citizen crime calls (as contrasted with crime incidents) close to the baseline year. That study did not control for baseline crime estimates or spatial lag effects, but suggested that citizen evaluations of crime and collective efficacy are strongly linked.
Our data provide strong support for this view in the relationship between social disorder and collective efficacy (r = − 0.55). The correlation is more modest for physical disorder (r = − 0.19).
We think this is an intriguing result which should be examined more carefully in future studies. The outcome we observe could result from positive prevention impacts of women on crime on high crime streets (Anderson 1999; St. Jean 2007), or the fact that women in the sample are less likely to employed and therefore more likely to be at home and act as guardians on the street (χ2 = 30.61; p < .001) (Cohen and Felson 1979).
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This work was supported by the National Institutes of Health [grant number 5R01DA032639-03, 2012].
Approval was obtained from the ethics committee of George Mason University. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
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The authors declare that they have no conflict of interest.
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Weisburd, D., White, C., Wire, S. et al. Enhancing Informal Social Controls to Reduce Crime: Evidence from a Study of Crime Hot Spots. Prev Sci 22, 509–522 (2021). https://doi.org/10.1007/s11121-020-01194-4
- Hot spots of crime
- Collective efficacy
- Informal social controls
- Opportunities for crime