Per-school public-vs-private ownership classifier for the SIRS School Infrastructure Risk Screening workstream. Benin pilot.
# sirs-public-private
Per-school public-vs-private ownership classifier for the SIRS (School Infrastructure Risk Screening) workstream. Pilot country is Benin; the architecture is designed to extend to Niger, Mali, Guinea, and Ghana with per-country rule files.
**Live dashboard:**
gfdrr.github.io · **Methodology:** `methodology.md`
## Why this exists
The Bank funds public infrastructure. Vulnerability rollups against the "public school stock" need to know which schools are actually public; misclassifying a private school as public inflates the public-stock denominator. Existing school-location datasets (Giga, OpenStreetMap, national EMIS exports) carry public/private signal inconsistently - some have explicit tags, most don't. This project derives a calibrated `ownership_label` per school with an explicit `unknown` band, optimised for high precision on the `private` flag.
## How it works
Four-stage pipeline:
1. **Stage A - deterministic labels.** Per-country YAML rules (regex on school names + OSM tag mining) produce a label with an audit trail (`source_signal`, `rule_id`, `confidence`). High precision, modest recall. See `stage-a-rules/README.md`.
2. **Stage B - probabilistic features.** Distance to urban centers, population density (GHSL SMOD), Relative Wealth Index, religious-POI proximity, INFRE department-level priors. See `stage-b-features/`.
3. **Stage C - calibrated classifier.** Logistic model trained on Stage A labels with ADM1-grouped cross-validation. Output is a probability with a three-band discretization (`public`, `private`, `unknown`). See `stage-c-model/`.
4. **Stage D - manual evaluation.** Sampled review against authoritative ministry / EMIS data per country.
The full method, model choices, calibration logic, and pilot results are in **`methodology.md`**.
## Repository layout
```
sirs-public-private/
├── README.md
├── methodology.md # method + Benin pilot results
├── requirements.txt …