Momahmoses/healthcare-facility-access

Travel-time analysis to identify underserved communities and guide health facility placement in Nigeria. # Healthcare Facility Access, Travel-Time Analysis Road-network travel-time analysis to calculate healthcare coverage, identify underserved Nigerian communities, and recommend optimal sites for new clinics and mobile health units. --- ## Problem Statement Millions of Nigerians live in communities where the nearest health facility is more than an hour away by road. This tool quantifies actual travel time (not straight-line distance) to expose real access gaps and guide facility placement decisions. --- ## Features | Feature | Description | |---------|-------------| | Travel-Time Computation | Haversine + road-speed adjustment per community | | Access Classification | Good / Moderate / Poor / Critical per settlement | | Population-Weighted Coverage | State-level coverage scores weighted by population | | Underserved Community Ranking | Top priority locations for new facility placement | | Interactive Access Map | Folium map with facility markers and coverage layers | --- ## Tech Stack | Layer | Technology | |-------|-----------| | Geospatial | GeoPandas, Folium, Shapely | | Analysis | pandas, NumPy, scikit-learn | | Visualisation | Matplotlib, Seaborn | --- ## Project Structure ``` healthcare-facility-access/ ├── src/ │ ├── data_loader.py # Community and facility data generation │ ├── analysis.py # Travel-time, access classification, coverage scoring │ └── visualize.py # Interactive map and coverage charts ├── data/raw/ # Road network, facility CSVs, population rasters ├── outputs/ # Generated maps and reports ├── config.yaml # Speed assumptions, access thresholds ├── main.py # Pipeline entry point └── requirements.txt ``` --- ## Quick Start ```bash git clone github.com cd healthcare-facility-access pip install -r requirements.txt python main.py ``` --- ## Data Sources - GRID3 Nigeria healthcare facility locations - OpenSt …