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Effects of demolishing abandoned buildings on firearm violence: a moderation analysis using aerial imagery and deep learning
  1. Jonathan Jay1,
  2. Jorrit de Jong2,
  3. Marcia P Jimenez3,
  4. Quynh Nguyen4,
  5. Jason Goldstick5
  1. 1 Department of Community Health Sciences, Boston University School of Public Health, Boston, Massachusetts, USA
  2. 2 Harvard University John F Kennedy School of Government, Cambridge, Massachusetts, USA
  3. 3 Boston University School of Public Health, Boston, Massachusetts, USA
  4. 4 Department of Epidemiology and Biostatistics, University of Maryland at College Park, College Park, Maryland, USA
  5. 5 Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, USA
  1. Correspondence to Jonathan Jay, Boston University School of Public Health, Boston, MA 02118, USA; jonjay{at}bu.edu

Abstract

Purpose Demolishing abandoned buildings has been found to reduce nearby firearm violence. However, these effects might vary within cities and across time scales. We aimed to identify potential moderators of the effects of demolitions on firearm violence using a novel approach that combined machine learning and aerial imagery.

Methods Outcomes were annual counts of fatal and non-fatal shootings in Rochester, New York, from 2000 to 2020. Treatment was demolitions conducted from 2009 to 2019. Units of analysis were 152×152 m grid squares. We used a difference-in-differences approach to test effects: (A) the year after each demolition and (B) as demolitions accumulated over time. As moderators, we used a built environment typology generated by extracting information from aerial imagery using convolutional neural networks, a deep learning approach, combined with k-means clustering. We stratified our main models by built environment cluster to test for moderation.

Results One demolition was associated with a 14% shootings reduction (incident rate ratio (IRR)=0.86, 95% CI 0.83 to 0.90, p<0.001) the following year. Demolitions were also associated with a long-term, 2% reduction in shootings per year for each cumulative demolition (IRR=0.98, 95% CI 0.95 to 1.00, p=0.02). In the stratified models, densely built areas with higher street connectivity displayed following-year effects, but not long-term effects. Areas with lower density and larger parcels displayed long-term effects but not following-year effects.

Conclusions The built environment might influence the magnitude and duration of the effects of demolitions on firearm violence. Policymakers may consider complementary programmes to help sustain these effects in high-density areas.

  • violence
  • environmental modification
  • statistical Issues
  • urban
  • firearm

Data availability statement

Data are available in a public, open access repository. Rochester, NY, shootings data: https://data-rpdny.opendata.arcgis.com/datasets/rochester-ny-shooting-victimsRochester, NY, demolitions data: https://data.cityofrochester.gov/datasets/7f9cb2accfa244db96fd18e47b34dead_0/exploreNY State orthoimagery portal: https://gis.ny.gov/gateway/mg/index.html.

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Footnotes

  • Contributors JJ and JdJ conceived the study. JJ and JG designed the study. JJ conducted the analysis and drafted the manuscript. JdJ, MPJ, QN and JG provided critical feedback and revisions.

  • Funding This study was supported by the Bloomberg Harvard City Leadership Initiative, funded by a gift to Harvard University by Bloomberg Philanthropies. MPJ received support from the National Institutes of Health (NIA 5K99AG066949-02). JG received support from the Firearm-safety Among Children and Teens (FACTS) Consortium (NICHD 1R24HD087149).

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  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.