Transforming vacant land for community benefit

Explore our innovative data-driven approach to prioritizing vacant land redevelopment. We identify key parcels to enhance social equity, accessibility, and overall community well-being, creating spaces that truly serve their neighborhoods.

Data-driven redevelopment for high-impact parcels

 

 

This study develops a comprehensive, data-driven framework to identify the 50 highest-impact parcels for vacant land stabilization. We move beyond isolated vacancy management by integrating multiple criteria: social equity, accessibility, and development feasibility. Our goal is to target areas where intervention will generate the greatest community benefit.

This analysis applies a spatial, multi-criteria prioritization framework in R to identify high-impact vacant parcels. Using packages such as sf, dplyr, and spatial functions, the workflow integrates demographic, crime, accessibility, and parcel-level data.

Key factors for impactful intervention

Key steps include:

  • Data preparation: Census data (race, population) were filtered to identify block groups with >50% Black population. Crime and amenity datasets (hospitals, free meal sites) were geocoded and converted into spatial objects using st_as_sf().
  • Accessibility analysis: A 1-mile buffer was generated around hospitals and meal sites using st_buffer() and merged (st_union()) to define walkable service areas.
  • Spatial filtering: High-priority zones were identified by intersecting (st_intersection()) three layers:
    1. High-crime block groups (top 100),
    2. Majority-Black neighborhoods,
    3. Areas with access to essential services.
  • Vacant parcel selection: Parcels within these zones were extracted and further evaluated.
  • Proximity and clustering metrics:
    • Distance to streets calculated using nearest-neighbor functions (st_nn()).
    • Distance to schools used as a proxy for youth exposure and pedestrian relevance.
    • Parcel clustering measured by counting nearby vacant lots within a defined radius.
  • Ranking system: A composite score was generated using normalized variables, with higher weights assigned to:
    • Proximity to schools
    • Parcel clustering
      Lower weights were assigned to parcel size and zoning (development feasibility).

Spatial analysis for systematic prioritization

 

 

  • The initial model strongly concentrated high-priority vacant parcels in North Central Philadelphia, indicating:
    • High levels of disinvestment
    • Strong clustering of vacancy
    • Overlapping social vulnerability and accessibility needs
  • After adjusting the model (reducing clustering and road-distance weighting), results became more spatially distributed across the city, while still highlighting North Central as a critical hotspot.
  • Additional neighborhoods such as Tioga also emerged in the revised model, suggesting broader patterns of need beyond a single concentrated area.