Mapkathon2026-UseOSM/lga-osm-extractor

A reusable Python toolkit for extracting, processing, and exporting OpenStreetMap (OSM) data for any Local Government Area (LGA) in Nigeria. Supports roads, buildings, healthcare facilities, schools, points of interest, administrative boundaries, and other OSM features, enabling mapping and geospatial analysis, . Developed for Map<>kathon 2026. # Nigerian LGA OSM Extractor [ ] (github.com) **Live demo:** lga-extractor.streamlit.app · **GitHub:** github.com Turn a plain Nigerian LGA name into a clean, ready-to-use, richly attributed OSM dataset which includes roads, buildings, waterways, land use, health facilities, and schools with a verifiable boundary, a correct projection, and a formal manifest of exactly what was extracted, with no Overpass query syntax, GIS software, or manual data wrangling required. Built for **Map<>kathon 2026** (Lightweight Tool / Demo track), as the data-extraction engine behind the sibling submission **akure-accessibility-dashboard**, *"Mapping the Gap: Health and Education Accessibility in Akure North and Akure South."* The tool itself is generalized to work for **any** Nigerian LGA, not just those two: constructing correct Overpass queries, resolving inconsistent administrative boundaries, and projecting into the correct UTM zone are real, recurring friction points for Nigerian GIS students and researchers, and this tool removes them entirely, for any of Nigeria's 774 LGAs. > **How this serves the public good:** every Nigerian GIS student or > researcher who has tried to pull OSM data for their own state or LGA > knows the friction of hand-writing Overpass queries, resolving > inconsistent administrative boundaries, and guessing which UTM zone > applies. This tool removes that friction entirely, and does so with > a traceable, auditable output - not just a file, but a signed record > of how that file was produced - so more people can go from "I have > an idea" to "I have data I can actually trust" and build something > real with OpenStreetMap. ## Try it The live demo requires no setup: lga-extractor.streamlit.app Or run it locally: ```bash pip install -e ".[app]" streamlit run app.py ``` Type in an LGA name, watch a **live, per-s …