A Topic Modeling Approach to Classifying Open Street Map Health Clinics and Schools in Sub-Saharan Africa

TL;DR

This paper presents an unsupervised topic modeling approach to classify and locate schools and health clinics in OpenStreetMap data across ten African countries, improving data utility for policy and development efforts.

Contribution

It introduces a scalable, unsupervised method to extract public service locations from unstructured OSM data, enhancing classification accuracy over traditional key-based methods.

Findings

Improved classification performance using topic modeling.

Validated OSM-derived locations against WHO data.

Identified coverage gaps in OSM data across Africa.

Abstract

Data deprivation, or the lack of easily available and actionable information on the well-being of individuals, is a significant challenge for the developing world and an impediment to the design and operationalization of policies intended to alleviate poverty. In this paper we explore the suitability of data derived from OpenStreetMap to proxy for the location of two crucial public services: schools and health clinics. Thanks to the efforts of thousands of digital humanitarians, online mapping repositories such as OpenStreetMap contain millions of records on buildings and other structures, delineating both their location and often their use. Unfortunately much of this data is locked in complex, unstructured text rendering it seemingly unsuitable for classifying schools or clinics. We apply a scalable, unsupervised learning method to unlabeled OpenStreetMap building data to extract the location of schools and health clinics in ten countries in Africa. We find the topic modeling approach greatly improves performance versus reliance on structured keys alone. We validate our results by comparing schools and clinics identified by our OSM method versus those identified by the WHO, and describe OSM coverage gaps more broadly.