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Enhancing Informal Social Controls to Reduce Crime: Evidence from a Study of Crime Hot Spots

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Abstract

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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Notes

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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).

  11. 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).

References

  • Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of inner city. New York: W. W. Norton and Company, Inc..

    Google Scholar 

  • Andresen, M. A., & Malleson, N. (2011). Testing the stability of crime patterns: Implications for theory and policy. Journal of Research in Crime and Delinquency, 48, 58–82.

    Article  Google Scholar 

  • Apparicio, P., Abdelmajid, M., Riva, M., & Shearmur, R. (2008). Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues. International Journal of Health Geographics, 7(1), 7.

    Article  PubMed  PubMed Central  Google Scholar 

  • Armstrong, T. A., Katz, C. M., & Schnebly, S. M. (2015). The relationship between citizen perceptions of collective efficacy and neighborhood violent crime. Crime and Delinquency, 61(1), 121–142.

    Article  Google Scholar 

  • Babbie, E. (2007). The practice of social research (11th ed.). Belmont: Wadsworth.

  • Baumer, E. P., Lauritsen, J. L., Rosenfeld, R., & Wright, R. (1998). The influence of crack cocaine on robbery, burglary, and homicide rates: A cross-city, longitudinal analysis. Journal of Research in Crime and Delinquency, 35, 316–340.

    Article  Google Scholar 

  • Bellair, P. E. (1997). Social interaction and community crime: Examining the importance of neighbor networks. Criminology, 35, 677–704.

    Article  Google Scholar 

  • Bellair, P. E. (2000). Informal surveillance and street crime: A complex relationship. Criminology, 38, 137–170.

    Article  Google Scholar 

  • Braga, A. A., & Bond, B. J. (2008). Policing crime and disorder hot spots: A randomized controlled trial. Criminology, 46(3), 577–607.

    Article  Google Scholar 

  • Braga, A. A., & Clarke, R. V. (2014). Explaining high-risk concentrations of crime in the city: Social disorganization, crime opportunities, and important next steps. Journal of Research in Crime and Delinquency, 51, 480–498.

    Article  Google Scholar 

  • Braga, A. A., Papachristos, A. V., & Hureau, D. M. (2014). The effects of hot spots policing on crime: An updated systematic review and meta-analysis. Justice Quarterly, 31(4), 633–663.

    Article  Google Scholar 

  • Brantingham, P. L., & Brantingham, P. J. (1993). Environment, routine, and situation: Toward a pattern theory of crime. In R. V. Clarke & M. Felson (Eds.), Routine activity and rational choice. Crime prevention studies (Vol. 5, pp. 259–294). New Brunswick: Transaction.

    Google Scholar 

  • Brantingham, P., & Brantingham, P. (1995). Criminality of place. European Journal on Criminal Policy and Research, 3(3), 5–26.

    Article  Google Scholar 

  • Browning, C. R., Feinberg, S. L., & Dietz, R. D. (2004). The paradox of social organization: Networks, collective efficacy, and violent crime in urban neighborhoods. Social Forces, 83, 503–534.

    Article  Google Scholar 

  • Bursik Jr., R. J., & Webb, J. (1982). Community change and patterns of delinquency. American Journal of Sociology, 88(1), 24–42.

    Article  Google Scholar 

  • Bursik Jr., R. J. (1988). Social disorganization and theories of crime and delinquency: Problems and prospects. Criminology, 26, 519–551.

    Article  Google Scholar 

  • Chant, S. (2004). Dangerous equations? How female-headed households became the poorest of the poor: Causes, consequences and cautions. IDS Bulletin, 35(4), 19–26.

    Article  Google Scholar 

  • Clarke, R. V. (1995). Situational crime prevention. Crime and Justice, 19, 91–150.

    Article  Google Scholar 

  • Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 558–608.

    Article  Google Scholar 

  • Dong, B., White, C., & Weisburd, D. (2020). Poor health and violent crime hot spots: Mitigating the undesirable co-occurrence through focused place-based interventions. American Journal of Preventive Medicine. https://doi.org/10.1016/j.amepre.2019.12.012.

  • Felson, M. (1986). Predicting crime potential at any point on the city map (pp. 127–136). Monsey: Metropolitan crime patterns. Criminal Justice Press.

    Google Scholar 

  • Gomez, M. B. (2016). Policing, community fragmentation, and public health: Observations from Baltimore. Journal of Urban Health, 93, S154–S167.

    Article  Google Scholar 

  • Grannis, R. (1998). The importance of trivial streets: Residential streets and residential segregation. American Journal of Sociology, 103(6), 1530–1564.

    Article  Google Scholar 

  • Grannis, R. (2005). T-communities: Pedestrian street networks and residential segregation in Chicago, Los Angeles, and New York. City & Community, 4(3), 295–321.

    Article  Google Scholar 

  • Green Mazerolle, L., Ready, J., Terrill, W., & Waring, E. (2000). Problem-oriented policing in public housing: The Jersey City evaluation. Justice Quarterly, 17(1), 129–158.

    Article  Google Scholar 

  • Groff, E. R., & Lockwood, B. (2014). Criminogenic facilities and crime across street segments in Philadelphia: Uncovering evidence about the spatial extent of facility influence. Journal of Research in Crime and Delinquency, 51(3), 277–314.

    Article  Google Scholar 

  • Hipp, J. R., & Kim, Y.-A. (2019). Explaining the temporal and spatial dimensions of robbery: Differences across measures of the physical and social environment. Journal of Criminal Justice, 60, 1–12.

    Article  Google Scholar 

  • Holbrook, A., Krosnick, J., & Pfent, A. (2008). The causes and consequences of response rates in surveys by the news media and government contractor survey research firms. In J. M. Lepkowski, C. Tucker, J. M. Brick, E. D. Leeuw, L. Japec, P. J. Lavrakas, M. W. Link, & R. L. Sangster (Eds.), Advances in telephone survey methodology (pp. 499–528). New York: Wiley.

    Google Scholar 

  • Irvin-Erickson, Y., & La Vigne, N. (2015). A spatio-temporal analysis of crime at Washington, DC metro rail: Stations’ crime-generating and crime-attracting characteristics as transportation nodes and places. Crime Science, 4(1), 14.

    Article  Google Scholar 

  • Jones, R. W., & Pridemore, W. A. (2019). Toward an integrated multilevel theory of crime at place: Routine activities, social disorganization, and the law of crime concentration. Journal of Quantitative Criminology, 35(3), 543–572.

    Article  Google Scholar 

  • Kingston, B., Huizinga, D., & Elliott, D. S. (2009). A test of social disorganization theory in high-risk urban neighborhoods. Youth and Society, 41, 53–79.

    Article  Google Scholar 

  • Klinger, D. A., & Bridges, G. S. (1997). Measurement error in calls-for-service as an indicator of crime. Criminology, 35(4), 705–726.

    Article  Google Scholar 

  • Kornhauser, R. (1978). Social sources of delinquency. Chicago: University of Chicago Press.

    Google Scholar 

  • Krivo, L. J., & Peterson, R. D. (1996). Extremely disadvantaged neighborhoods and urban crime. Social Forces, 75, 619–650.

    Article  Google Scholar 

  • Levas, M., & Nimmer, M. (2017). An independent evaluation of Safe & Sound’d community building strategies. Safe & Sound. Retrieved from: https://safesound.org/wp-content/uploads/2017/05/Evaluation-Report_gen-use.pdf.

  • Loukaitou-Sideris, A. (1999). Hot spots of bus stop crime: The importance of environmental attributes. Journal of the American Planning Association, 65(4), 395–411.

    Article  Google Scholar 

  • Mazerolle, L., Wickes, R., & McBroom, J. (2010). Community variations in violence: The role of social ties and collective efficacy in comparative context. Journal of Research in Crime and Delinquency, 47(1), 3–30.

    Article  Google Scholar 

  • McCord, E. S., & Ratcliffe, J. H. (2009). Intensity value analysis and the criminogenic effects of land use features on local crime patterns. Crime Patterns and Analysis, 2(1), 17–30.

    Google Scholar 

  • Merse, C. L., Buckley, G. L., & Boone, C. G. (2008). Street trees and urban renewal: A Baltimore case study. The Geographical Bulletin, 50, 65–81.

    Google Scholar 

  • Morenoff, J. D., Sampson, R. J., & Raudenbush, S. W. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39(3), 517–558.

    Article  Google Scholar 

  • Nagin, D. S., Solow, R. M., & Lum, C. (2015). Deterrence, criminal opportunities, and police. Criminology, 53(1), 74–100.

    Article  Google Scholar 

  • Parker, K. F., & Reckdenwald, A. (2008). Concentrated disadvantage, traditional male role models, and African-American juvenile violence. Criminology, 46(3), 711–735.

    Article  Google Scholar 

  • Perkins, D. D., Florin, P., Rich, R. C., Wandersman, A., & Chavis, D. M. (1990). Participation and the social and physical environment of residential blocks: Crime and community context. American Journal of Community Psychology, 18, 83–115.

    Article  Google Scholar 

  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publication Inc.

    Google Scholar 

  • Rhew, I. C., Hawkins, J. D., Murray, D. M., Fagan, A. A., Oesterle, S., Abbott, R. D., & Catalano, R. F. (2016). Evaluation of community-level effects of communities that care on adolescent drug use and delinquency using a repeated cross-sectional design. Prevention Science, 17(2), 177–187.

    Article  PubMed  PubMed Central  Google Scholar 

  • Roman, C. G., Reid, S. E., Bhati, A. S., & Tereshchenko, B. (2008). Alcohol outlets as attractors of violence and disorder: A closer look at the neighborhood environment. Washington, DC: Urban Institute.

    Google Scholar 

  • Sampson, R. J. (1988). Local friendships ties and community attachment in mass society: A multilevel systemic model. American Sociological Review, 53, 766–779.

    Article  Google Scholar 

  • Sampson, R. J. (2006). Collective efficacy theory: Lessons learned and directions for future inquiry. In F. T. Cullen, J. P. Wright, & K. R. Blevins (Eds.), Taking stock: The status of criminological theory. Advances in criminological theory (Vol. 15, pp. 149–167). New Brunswick: Transaction.

    Google Scholar 

  • Sampson, R. J. (2012). Great American City: Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Sampson, R. J., & Groves, W. B. (1989). Community structure and crime: Testing social-disoranization theory. The American Journal of Sociology, 94, 774–802.

    Article  Google Scholar 

  • Sampson, R. J., & Lauritsen, J. L. (1993). Violent victimization and offending: Individual-, situational-, and community-level risk factors. In A. J. Reiss Jr. & J. A. Roth (Eds.), Understanding and preventing violence, Panel on the understanding and control of violent behavior (Vol. 3, pp. 1–114). Washington: National Academy Press.

    Google Scholar 

  • Sampson, R. J., & Raudenbush, S. W. (1999). Systematic observation of public spaces: A new look at disorder in urban neighborhoods. The American Journal of Sociology, 105, 603–651.

    Article  Google Scholar 

  • Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhood and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924.

    Article  CAS  PubMed  Google Scholar 

  • Sampson, R. J., Morenoff, J. D., & Earls, F. (1999). Beyond social capital: Spatial dynamics of collective efficacy for children. American Sociological Review, 64, 633–660.

    Article  Google Scholar 

  • Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas. In A study of rates of delinquency in relation to differential characteristics of local communities in American cities. Chicago: University of Chicago Press.

    Google Scholar 

  • Sherman, L. W., & Weisburd, D. (1995). General deterrent effects of police patrol in crime “hot spots”: A randomized, controlled trial. Justice Quarterly, 12, 625–648.

    Article  Google Scholar 

  • Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27, 27–56.

    Article  Google Scholar 

  • Spoth, R., & Greenberg, M. (2011). Impact challenges in community science-with-practice: Lessons from PROSPER on transformative practitioner-scientist partnerships and prevention infrastructure development. American Journal of Community Psychology, 48(1–2), 106–119.

    Article  PubMed  PubMed Central  Google Scholar 

  • Spoth, R., Greenberg, M., Bierman, K., & Redmond, C. (2004). PROSPER community-university partnership model for public education systems: Capacity-building for evidence-based, competence-building prevention. Prevention Science, 5(1), 31–39.

    Article  PubMed  Google Scholar 

  • St. Jean, P. K. B. (2007). Pockets of crime: Broken windows, collective efficacy, and the criminal point of view. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Telep, C. W., & Weisburd, D. (2018). Crime concentrations at places. In G. J. N. Bruinsma & S. D. Johnson (Eds.), The Oxford Handbook of Environmental Criminology. New York, NY: Oxford University Press.

    Google Scholar 

  • Thrasher, F. M. (1927 [1963]). The gang: A study of 1,313 gangs in Chicago. Chicago: Phoenix.

    Google Scholar 

  • Velez, M. B. (2001). The role of public social control in urban neighborhoods: A multi-level analysis of victimization risk. Criminology, 39, 837–864.

    Article  Google Scholar 

  • Weisburd, D. (2015). The law of crime concentration and the criminology of place. Criminology, 53(2), 133–157.

    Article  Google Scholar 

  • Weisburd, D., & Majmundar, M. K. (2018). Proactive policing: Effects on crime and communities. Washington: National Academies Press .

    Book  Google Scholar 

  • Weisburd, D., Wyckoff, L., Ready, J., Eck, J. E., Hinkle, J., & Gajewski, F. (2006). Does crime just move around the corner? A controlled study of spatial displacement and diffusion of crime control benefits. Criminology, 44, 549–592.

    Article  Google Scholar 

  • Weisburd, D., Lawton, B., Ready, J., & Haviland, A. (2011). Longitudinal study of community health and anti-social behavior at drug hot spots. National Institute on Drug Abuse, National Institutes of Health. [Grant no. 5R01DA032639–03, 2012].

  • Weisburd, D., Groff, E. R., & Yang, S. M. (2012). The criminology of place: Street segments and our understanding of the crime problem. New York and Oxford: Oxford University Press.

    Book  Google Scholar 

  • Weisburd, D., Groff, E. R., & Yang, S. M. (2014). Understanding and controlling hot spots of crime: The importance of formal and informal social controls. Prevention Science, 15(1), 31–43.

    Article  PubMed  Google Scholar 

  • Weisburd, D. L., Cave, B., Nelson, M., White, C., Haviland, A., Ready, J., et al. (2018). Mean streets and mental health: Depression and post-traumatic stress disorder at crime hot spots. American Journal of Community Psychology, 61, 285–295.

    Article  PubMed  PubMed Central  Google Scholar 

  • Weisburd, D., Gill, C., Wooditch, A., Barritt, W., & Murphy, J. (2020a). Building collective action at crime hot spots: Findings from a randomized field experiment. Journal of Experimental Criminology. https://doi.org/10.1007/s11292-019-09401-1.

  • Weisburd, D., White, C., & Wooditch, A. (2020b). Does collective efficacy matter at the micro geographic level?: Findings from a study of street segments. British Journal of Criminology, 60(4), 873–891.

    Article  Google Scholar 

  • White, C., & Weisburd, D. (2018). A co-responder model for policing mental health problems at crime hot spots: Findings from a pilot project. Policing: a journal of policy and practice, 12(2), 194–209.

    Article  Google Scholar 

  • Wicker, A. W. (1987). Behavior settings reconsidered: Temporal stages, resources, internal dynamics, context. In D. Stokols & I. Altman (Eds.), Handbook of environmental psychology (pp. 613–653). New York: Wiley.

    Google Scholar 

  • Wikström, P. O. H., Oberwittler, D., Treiber, K., & Hardie, B. (2012). Breaking rules: The social and situational dynamics of young people's urban crime. Oxford: OUP.

    Google Scholar 

  • Wilcox, P., & Tillyer, M. S. (2018). Place and neighborhood contexts. In D. Weisburd & J. E. Eck (Eds.), Unraveling the crime-place connection. New York: Routledge.

    Google Scholar 

  • Wilcox, P., Land, K. C., & Hunt, S. C. (2003). Criminal circumstance: A dynamic multicontextual criminal opportunity theory. New York: Walter de Gruyster.

    Google Scholar 

  • Wilcox, P., Madensen, T. D., & Tillyer, M. S. (2007). Guardianship in context: Implications for burglary victimization risk and prevention. Criminology, 45(4), 771–803.

    Article  Google Scholar 

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Funding

This work was supported by the National Institutes of Health [grant number 5R01DA032639-03, 2012].

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Authors and Affiliations

  1. Department of Criminology, Law and Society, George Mason University, 4400 University Drive, MS 6D12, Fairfax, VA, 22030, USA

    David Weisburd

  2. Institute of Criminology, Faculty of Law, Jerusalem, Israel

    David Weisburd

  3. University of Wyoming, Laramie, WY, USA

    Clair White

  4. George Mason University, Fairfax, VA, USA

    Sean Wire & David B. Wilson

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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

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