leviupendo/healthcare-access-africa

A GIS + data analytics pipeline that measures physical access to healthcare facilities across nine African countries — five in East Africa (Kenya, Uganda, Tanzania, Ethiopia, Rwanda) and four of Africa's more industrialised economies (South Africa, Egypt, Morocco, Tunisia) — and benchmarks them against each other and against published research. # Healthcare Access Africa — GIS Benchmarking Pipeline This is a GIS + data analytics pipeline I built to measure physical access to healthcare facilities across nine African countries — five in East Africa (Kenya, Uganda, Tanzania, Ethiopia, Rwanda) and four of Africa's more industrialised economies (South Africa, Egypt, Morocco, Tunisia) — and benchmark them against each other and against published research. I set it up to run **end-to-end out of the box** on synthetic sample data, so anyone cloning it can see the full pipeline work immediately. It's also built so I can drop in **real facility data** and get a real, sellable analysis — see Using real data below. ## What this actually does 1. Loads a country's boundary polygon (real data — see Data sources). 2. Loads health facility point locations (**synthetic/sample by default**). 3. Buffers each facility by a chosen radius (default 5 km) in an equal-area projection, so distances are measured in real metres, not degrees. 4. Computes what % of the country's **land area** falls within that buffer. 5. Generates a map per country and a comparison bar chart across all nine. 6. Joins the result against a table of **real, cited, published** accessibility figures (`data/reference/published_access_benchmarks.csv`) pulled from peer-reviewed geospatial studies, so I can see where my own analysis lines up with, or fills a gap in, the existing literature. ### Area coverage vs. population coverage — read this before presenting results The pipeline computes **% of land area** within N km of a facility. That's a legitimate, standard GIS screening metric, but it is **not** the same as "% of population" within N km — the number funders and governments actually care about, because people aren't spread evenly across land area. Sparse land (northern Kenya, the Sahara in Egypt/Morocco) will always look like a bigger "gap" on an area basis than a population basis. To get a population-weighted figure — the metric used in the publ …