Reducing crime by remediating vacant lots: the moderating effect of nearby land uses



Place-based blight remediation programs have gained popularity in recent years as a crime reduction approach. This study estimated the impact of a citywide vacant lot greening program in Philadelphia on changes in crime over multiple years, and whether the effects were moderated by nearby land uses.


The vacant lot greening program was assessed using quasi-experimental and experimental designs. Entropy distance weighting was used in the quasi-experimental analysis to match control lots to be comparable to greened lots on pre-existing crime trends. Fixed-effects difference-in-differences models were used to estimate the impact of the vacant lot greening program in quasi-experimental and experimental analyses.


Vacant lot greening was estimated to reduce total crime and multiple subcategories in both the quasi-experimental and experimental evaluations. Remediating vacant lots had a smaller effect on reducing crime when they were located nearby train stations and alcohol outlets. The crime reductions from vacant lot remediations were larger when they were located near areas of active businesses. There is some suggestive evidence that the effects of vacant lot greening are larger when located in neighborhoods with higher pre-intervention levels of social cohesion.


The findings suggest that vacant lot greening provides a sustainable approach to reducing crime in disadvantaged neighborhoods, and the effects may vary by different surrounding land uses. To better understand the mechanisms through which place-based blight remediation interventions reduce crime, future research should measure human activities and neighborly socialization in and around places before and after remediation efforts are implemented.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3


  1. 1. (Accessed September 4, 2020).

  2. 2.

    The location and violation data were provided by Philadelphia Office of License and Inspection (L&I) and retrieved from:

  3. 3.

    The original study by Branas et al. (2011) examined yearly crime data from 1999 to 2008 around 4436 vacant lots remediated by PLC compared to a matched sample of 13,308 lots that remained vacant in the same sections of the city.

  4. 4.

    The crime incident data includes the type of offense, the date, time and location (geocoded to the nearest latitude-longitude coordinate, GCS WGS 1984). Monthly crime incident data retrieved from:

  5. 5.

    According to this bandwidth of 500 ft., crimes occurring at the 500, 10, and 1 ft. boundary are given weights of 0, 0.616, and 1 respectively. When the buffers around lots overlap and crime incidents fall within the range of multiple lots, kernel density estimates are advantageous to a simple count as it will give the approximate weight to each discrete distance from a given crime to a given lot. Kernel density estimates were calculated for each crime to each lot per month using the dnorm function in R.

  6. 6. (accessed September 1, 2020)

  7. 7.

    The assessment data contains the description of the zoning code for each property (e.g., multi-family, single family, commercial, industrial, and mixed-use). We focus on commercial and mixed-use as areas of commerce have been shown be associated criminal activity (Bernasco and Block 2009).

  8. 8.

    We define nearby as distances of 750 ft., or roughly 1.5 city blocks, for schools and transit stations and 500 ft. for alcohol outlets.

  9. 9.

    We use Euclidean distance for these land use measures. Euclidean and street network (Manhattan) distance measures were highly correlated (.95–.99).

  10. 10.

    The distributions of the count of nearby commercial (kurtosis = 27.21; skewness = 3.84) and mixed used (kurtosis = 11.75; skewness = 2.45) properties were skewed to the right. By creating dichotomous variables measuring prevalence, we mitigate against extreme outliers of counts of commercial or mixed-use properties.

  11. 11.

    The eight common types of business identified are as follows: food establishments and restaurants, food manufacturers and wholesalers, motor vehicle repair and sale shops, vendors, childcare facilities, amusement-related businesses, public garages and parking lots, and pawn shops.

  12. 12.

    Lots with high business activity had an average of 12 active businesses within 500 ft. compared to 3 for lots that were assigned low business activity.

  13. 13.

    Principal components analysis showed that one component explained 51% of the variance across the eight items.

  14. 14.

    Bertrand et al. (2004) show that this version of a fixed-effects estimator is a difference-in-differences model. For example, the standard difference-in-differences model of vacant lot (i) greening treatment (T) by post (P) intervention time period (t) could be estimated by the following form:

    Yit = β0 + β1Ti + β2Pt + β3(Ti × Pt) + εit (1). In contrast, a fixed-effects estimator could be estimated by the following form: (2) Yit = αi + θt + δDit + εit, where D is time varying dummy of the interaction of greening and post treatment period (Ti*Pt). By integrating equations 1 and 2, one can see that the matrix of fixed-effects for lots α (i) and time θ (t) cancels out β1 and β2 in equation 1, thus leaving one with only the difference-in-differences coefficient that is identified by β3 or δ.

  15. 15.

    This approach to assessing spatial autocorrelation is more flexible than imposing a given spatial distance structure on the data, like a distance weight or nearest neighbor matrix.

  16. 16.

    The method also guards against the influence of unusually large weights from driving results, as large weights reduce the effective sample size, thereby increasing variance and reducing the precision of estimates.

  17. 17.

    Weights are chosen by the following reweighting scheme subject to balance and normalization constraints: minwiH(w) = ∑{i| t = 0}wi log wi/qi (Hainmueller 2012).

  18. 18.

    A separate analysis examining single models for each interaction term shows substantively similar results.

  19. 19.

    Crime trends are parallel between the treatment and control arms when we adjust standard errors for the clusters.

  20. 20.

    Percentage reductions were calculated from difference-in-difference estimates by the following formula: (estimate/(absolute value of estimate + post mean of greened lots))*100. For example, the estimates from robbery are − .011 and the post-mean for greened vacant lots is .042, using this formula then yields (− 0.011/(0.011 + .042))*100 = − 20.75.

  21. 21.

    An F-test of the joint significance of the interaction of the greening intervention with quartiles of business activity indicates that they explain a significant share of the variation in change in total crime (F(dfn = 3, dfd = 2653) = 2.84; p = 0.036).

  22. 22.

    These results are slightly different from those published by Branas et al. (2018) because our replication includes a different follow-up period and set of crime categories.

  23. 23.

    The results are substantively similar if we model the interaction of social cohesion quartiles and greening, or only the upper 75th percentile of social cohesion.

  24. 24.

    The quasi-experimental analysis also included a weight for the entropy distance between greened and control lots on the average count of crime in the months preceding remediation.


  1. Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: an empiricist’s companion. Princeton: Princeton University Press.

  2. Bernasco, W., & Block, R. (2009). Where offenders choose to attack: a discrete choice model of robberies in Chicago. Criminology, 47(1), 93–130.

    Article  Google Scholar 

  3. Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust difference in differences estimates? Quarterly Journal of Economics, 119(1), 249–275.

    Article  Google Scholar 

  4. Bester, C. A., Conley, G. T., & Hansen, C. B. (2011). Inference with dependent data using cluster covariance estimators. Journal of Econometrics, 165(2), 137–151.

    Article  Google Scholar 

  5. Branas, C. C., Cheney, R. A., Macdonald, J. M., Tam, V. W., Jackson, T. D., & Ten Have, T. R. (2011). A difference-in-differences analysis of health, safety, and greening vacant urban space. American Journal of Epidemiology, 174(11), 1296–1306.

    Article  Google Scholar 

  6. Branas, C. C., South, E., Kondo, M. C., Hohl, B. C., Bourgois, P., Wiebe, D. J., & Macdonald, J. M. (2018). Citywide cluster randomized trial to restore blighted vacant land and its effects on violence, crime, and fear. PNAS, 115(12), 2946–2951.

    Article  Google Scholar 

  7. Brantingham, P. L., & Brantingham, P. J. (1993). Brantingham, Patricia L., and Paul J. Brantingham. Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology, 13(1), 3–28.

    Article  Google Scholar 

  8. City of Philadelphia. (2002). Five-Year Action Plan (Fiscal Years 2003–2007). Philadelphia.

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

    Article  Google Scholar 

  10. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: a routine activity approach. American Sociological Review, 44(4), 588–608.

    Article  Google Scholar 

  11. Cozens, P. M., Saville, G., & Hillier, D. (2005). Crime prevention through environmental design (CPTED): a review and modern bibliography. Property Management, 23(5), 23(5), 328–356.

    Article  Google Scholar 

  12. Curman, A. S., Andersen, M. A., & Brantingham, P. J. (2015). Crime and place: a longitudinal examination of street segment patterns in Vancouver, BC. Journal of Quantitative Criminology, 31, 127–147.

    Article  Google Scholar 

  13. Econsult Corporations and Penn Institute for Urban Research. (2000). Vacant land management in Philadelphia: the costs of the current system and the benefits of reform.

  14. Hainmueller, J. (2012). Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25–46.

    Article  Google Scholar 

  15. Heckert, M., & Kondo, M. (2018). Heckert, Megan, and MCan “cleaned and greened” lots take on the role of public greenspace? Journal of Planning Education and Research, 38(2), 211–221.

  16. Heinze, J. E., Krusky-Morey, A., Vagi, K. J., Reischl, T. M., Franzen, S., Pruett, N. K., et al. (2018). Busy streets theory: the effects of community-engaged greening on violence. American Journal of Community Psychology, 62(1–2), 101–109.

    Article  Google Scholar 

  17. Jacobs, J. (1961). The death and life of greater American cities. New York: Random House.

    Google Scholar 

  18. Jay, J., Miratrix, L. W., Branas, C. C., Zimmerman, M. A., & Hemenway, D. (2019). Urban building demolitions, firearm violence and drug crime. Journal of Behavioral Medicine, 42(2), 626–634.

    Article  Google Scholar 

  19. Jeffery, C. R. (1971). Crime prevention through environmental design. Beverly Hills: Sage Publications.

    Google Scholar 

  20. Kondo, M. C., Keen, D., Hohl, B. C., MacDonald, J. M., & Branas, C. C. (2015). A difference-in-differences study of the effects of a new abandoned building remediation strategy on safety. PLoS One, 10(7), e0129582.

    Article  Google Scholar 

  21. Kondo, M., Hohl, B., Han, S., & Branas, C. (2016). Effects of greening and community reuse of vacant lots on crime. Urban Studies, 53(15), 3279–3295.

    Article  Google Scholar 

  22. Kondo, M., Morrison, C., Jacoby, S. F., Elliott, L., Poche, A., Theall, K. P., & Branas, C. C. (2018). Blight abatement of vacant land and crime in New Orleans. Public Health Reports, 133(6), 650–657.

    Article  Google Scholar 

  23. MacDonald, J. (2015). Community design and crime: the impact of housing and the built environment. Crime and Justice, 44(1), 333–383.

    Article  Google Scholar 

  24. MacDonald, J. M., & Stokes, R. J. (2020). Gentrification, land use, and crime. Annual Review of Criminology, 3, 121–138.

    Article  Google Scholar 

  25. MacDonald, J., Branas, C., & Stokes, R. (2019). Changing places: the science and art of new urban planning. Princeton: Princeton University Press.

    Google Scholar 

  26. McGovern, S. J. (2006). Philadelphia’s neighborhood transformation initiative: a case of study of mayoral leadership, bold planning, and conflict. Housing Policy Debate, 17(3), 529–570.

    Article  Google Scholar 

  27. Moyer, R., MacDonald, J. M., Ridgeway, G., & Branas, C. C. (2019). Effect of remediating blighted vacant land on shootings: a citywide cluster randomized trial. American Journal of Public Health, 109(1), 140–144.

    Article  Google Scholar 

  28. Nagin, D. S., & Sampson, R. J. (2019). The real gold standard: measuring counterfactual worlds that matter most to social science and policy. Annual Review of Criminology, 2, 123–145.

    Article  Google Scholar 

  29. Olaghere, A., & Lum, C. (2018). Classifying “micro” routine activities of street-level drug transactions. Journal of Research in Crime and Delinquency, 55(4), 466–492.

    Article  Google Scholar 

  30. Pearsall, H., Lucas, S., & Lenhardt, J. (2014). The contested nature of vacant land in Philadelphia and approaches for resolving competing objectives for redevelopment. Cities, 40(Part B), 163–174.

    Article  Google Scholar 

  31. Ratcliffe, J. H. (2012). The spatial extent of criminogenic places: a changepoint regression of violence around bars. Geographical Analysis, 44(4), 302–320.

    Article  Google Scholar 

  32. Ridgeway, G., Grogger, J., Moyer, R. A., & MacDonald, J. M. (2019). Effect of gang injunctions on crime: a study of Los Angeles from 1988-2014. Journal of Quantitative Criminology, 35, 517–541.

    Article  Google Scholar 

  33. Rosenblatt, M. (1956). Remarks on some nonparametric estimates of a density function. The Annals of Mathematical Statistics, 27(3), 832–837.

    Article  Google Scholar 

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

    Google Scholar 

  35. Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: a multilevel study of collective efficacy. Science, 5328, 918–924.

    Article  Google Scholar 

  36. SEPTA. (2019). SEPTA GIS. Retrieved from SEPTA: . Accessed June 2019.

  37. Shaw, C. R., & McKay, H. D. (1972). Juvenile delinquency and urban areas. Chicago: Universit of Chicago Press.

    Google Scholar 

  38. Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hot spots of predatory crime: routine activities and the criminology of place. Criminlogy, 27(1), 27–56.

    Article  Google Scholar 

  39. Skogan, W. G. (1990). Disorder and decline. New York: Free Press.

    Google Scholar 

  40. Spader, J., Schuetz, J., & Cortes, A. (2006). Fewer vacants, fewer crimes? Impacts of neighborhood revitalization policies on crime. Regional Science and Urban Economics, 60, 73–84.

    Article  Google Scholar 

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

    Google Scholar 

  42. Steinberg, M. P., Ukert, B., & MacDonald, J. M. (2019). Schools as places of crime? Evidence from closing chronically underperforming schools. Regional Science and Urban Economics, 77, 125–140.

    Article  Google Scholar 

  43. Taylor, R. B. (1988). Human territorial functioning. Cambridge: Cambridge University Press.

    Google Scholar 

  44. Taylor, R. (2012). Community criminology: Fundamentals of spatial and temporal scaling, ecological indicators, and selectivity bias. New York: NYU Press.

    Google Scholar 

  45. Tita, G. E., Cohen, J., & Engberg, J. (2005). An ecological study of the location of gang “set space”. Social Problems, 52(2), 272–299.

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Weisburd, D. (2016). Place matters: criminology for the twenty-first century. Cambridge: Cambridge University Press.

    Google Scholar 

  48. Weisburd, D., Groff, E. R., Yang, & Sue-Ming. (2012). The criminology of place: street segments and our understanding of the crime problem. New York: Oxford University Press.

    Google Scholar 

  49. Wheeler, A., Kim, D.-Y. K., & Phillips, S. W. (2018). The effect of housing demolitions on crime in Buffalo, New York. Journal of Research in Crime and Delinquency, 55(3), 390–424.

    Article  Google Scholar 

  50. Wilcox, P., & Cullen, F. T. (2018). Situational opportunity theories of crime. Annual Review of Criminology, 1, 123–148.

    Article  Google Scholar 

  51. Wilson, W. J. (1996). When work disappears: the world of the new urban poor. New York: Random House.

    Google Scholar 

  52. Wilson, J. Q., & Kelling, G. L. (1982). Broken windows: the police and neighborhood safety. Atlantic Monthly, 29(3), 29–38.

    Google Scholar 

Download references


We owe special thanks to the Pennsylvania Horticultural Society for their collaboration and data. We thank Robert J. Sampson, Ben Hansen, Greg Ridgeway, and the anonymous reviewers for helpful comments on an earlier draft.


This study was funded in part by the National Institutes of Health (grants R01AA020331, R01AA024941) and the Centers for Disease Control and Prevention (grants R49CE002474, R49CE003094). The funders had no role in the design and conduct of the study; collection management, analysis, and interpretation of the data; preparation, review, or decision to submit the article for publication.

Author information



Corresponding author

Correspondence to John Macdonald.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Appendix 1

Table 4 Sensitivity estimates for spatial autocorrelation

Appendix 2

Table 5 Displacement test for total crime

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Macdonald, J., Nguyen, V., Jensen, S.T. et al. Reducing crime by remediating vacant lots: the moderating effect of nearby land uses. J Exp Criminol (2021).

Download citation


  • Quasi-experimental
  • Experimental
  • Vacant lots
  • Remediation
  • Place-based interventions