Green Space Accessibility for Affordable Housing

Interactive dashboard analyzing disparities in access to high-quality public parks near affordable housing buildings in Chicago

Overview

Green Space Accessibility for Affordable Housing is a data science project that analyzes disparities in access to high-quality public parks near affordable housing buildings in Chicago. By combining housing data, census tract information, and spatial/ratings data on city green spaces, we developed an Accessibility Index that quantifies access based on park ratings, size, and proximity.

What It Does

The project includes:

  • Accessibility Index: A quantitative measure combining park ratings, size, and proximity to affordable housing
  • Interactive Dashboard: Built with Python Dash to visualize the Accessibility Index and enable exploration of green space access patterns
  • Spatial Analysis: Integration of OpenStreetMap park data, Yelp/Google Places API reviews, and U.S. Census Bureau ACS data
  • Inequity Identification: Reveals patterns of inequity in green space access across Chicago neighborhoods

Why I Made It

This project was developed for CAPP 30122: Data Science for Public Policy at the University of Chicago. Public parks and green spaces are vital to community well-being, providing spaces for recreation, social connection, and mental health. Ensuring equitable access to high-quality green spaces is a key responsibility for urban planners and policymakers—especially for residents of affordable housing.

Technologies & Skills

  • Python for data processing and analysis (Pandas, GeoPandas)
  • OSMnx API for extracting OpenStreetMap data on Chicago parks
  • Yelp & Google Places APIs for park ratings and reviews
  • U.S. Census Bureau ACS API for census tract data
  • Python Dash for interactive dashboard development
  • Spatial Analysis using GeoPandas for proximity calculations and spatial joins
  • Data Pipeline Design integrating multiple data sources (housing, parks, reviews, census)

Real-World Impact

The interactive dashboard empowers planners and decision-makers with a data-driven approach to:

  • Identify areas with limited access to quality parks near affordable housing
  • Promote more equitable urban green space planning through evidence-based insights
  • Support policy decisions on park development and resource allocation

Team & My Role

Collaboration: Begum Akkas, Andrés Camacho, Evan Fantozzi, Grace Kluender

My contributions:

  • Designed and developed the end-to-end data pipeline architecture integrating multiple data sources
  • Created the interactive Python Dash dashboard with accessibility index visualizations
  • Developed Kepler.gl maps for spatial visualization of green space accessibility patterns
  • Implemented OpenStreetMap data extraction workflows using OSMnx API
  • Built Google Places API integration for park ratings and reviews data collection
  • Developed comprehensive pytest test suite for key dashboard functionalities and data pipeline components
  • Processed and cleaned multi-source data (housing developments, park locations, reviews, census tracts)

Source Code

Repository: uchicago-2025-capp30122/30122-project-treehuggers

Data Sources

  • OpenStreetMap Chicago Parks Data - Extracted via OSMnx API for Python
  • Affordable Rental Housing Developments - City of Chicago data portal
  • Yelp Business Search API - Park ratings and reviews
  • Google Places Nearby Search API - Additional park ratings data
  • U.S. Census Bureau ACS - Census tract demographic and economic data