Python Final Project: Passyunk Square Character Map [Collaboration]

Python Final Project: Passyunk Square Character Map [Collaboration] preview

Takeaway: A composite accessibility index reveals clear neighborhood-level differences in mobility, amenities, environmental quality, and social context across Philadelphia despite this project’s limitations with demographic data integration.

Overview This project was developed as the final project for MUSA 5500: Geospatial Analytics and evaluates how accessibility varies at the neighborhood scale in Philadelphia, with a focus on Passyunk Square. Working in a two-person team, we integrated multiple spatial datasets into a single, interpretable accessibility framework.

Key Outputs

Methods Accessibility was modeled using four normalized components:

  • Mobility: walkability, bikeability, pedestrian infrastructure, and transit access
  • Land Use & Amenities: proximity to essential services
  • Environmental Quality: vegetation health, parks, and tree canopy
  • Social & Demographic Context: socioeconomic indicators shaping lived accessibility

Key Findings

  • Accessibility varies substantially across neighborhoods depending on component weighting.
  • Passyunk Square performs strongly on mobility and amenities relative to citywide patterns.
  • Composite scoring highlights tradeoffs between environmental and social accessibility dimensions.

Limitations: While the project demonstrates multi-source spatial integration and index construction, the census-based demographic component is subject to limitations stemming from data harmonization and communication challenges during implementation. These constraints are documented and reflected on in the final site.