joas-geomatics/healthcare-access-benIN
Domain:
geospatialhealthcareCreator:
joa
Host:
Commune-level analysis of healthcare accessibility in Benin using a composite spatial index combining distance to health facilities, demographic pressure and facility capacity, with sensitivity and urban–rural comparison.
## Healthcare Accessibility in Benin 🇧🇯
Commune-level spatial analysis of healthcare accessibility using GIS and Python
This project analyzes healthcare accessibility across the 77 communes of Benin using a composite spatial index integrating:
Distance to health facilities
Demographic pressure
Facility capacity (weighted by type)
It also explores urban–rural disparities and tests the robustness of results through sensitivity analysis.
🚀 Update – Fully Reproducible GeoPandas Workflow (v2)
The analysis has been upgraded to a fully spatially reproducible pipeline using GeoPandas.
Instead of relying on intermediate CSV files, the index is now computed directly from:
Administrative boundaries (communes)
Localities (settlements)
Health facility point layers
Using:
Spatial joins (sjoin)
Nearest distance computation (sjoin_nearest)
Automated aggregation at commune level
This improves:
Reproducibility
Automation
Methodological transparency
Analytical robustness
## 🎯 Objectives
Measure healthcare accessibility at the commune level
Identify spatial inequalities across Benin
Test whether urban status guarantees better access
Assess the sensitivity of results to modeling assumptions
## 🧠 Key Findings
Urban status does not guarantee good healthcare access
Major urban communes (Cotonou, Abomey-Calavi, Porto-Novo, Parakou) are classified between medium and very poor access
Some rural communes show moderate accessibility due to lower demographic pressure
Accessibility patterns remain generally stable, but specific communes are sensitive to weighting choices
## 🗺️ Method Overview
Indicators
Mean distance from localities to nearest health facility
Population pressure (population / number of facilities)
Facility capacity proxy (weighted by facility type)
Processing Steps
Reprojection to metric CRS
Nearest distance computation (GeoPandas sjoin_nearest)
Spatial aggregation at commune level
Indicator normalization (min–max scaling)
Composite index …