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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.

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  1. (Accessed September 4, 2020).

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

  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. 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. 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. (accessed September 1, 2020)

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

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

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

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

  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. 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. 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. 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. 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.


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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.

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Appendix 1

Table 4 Sensitivity estimates for spatial autocorrelation

Appendix 2

Table 5 Displacement test for total crime

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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).

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