joas-geomatics/healthcare-access-benIN

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 …