  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.
