The World Bank is increasingly using OSM in its spatial analysis to support operational decision making. A great example is a recent dialogue the World Bank has had with the municipal government of Tbilisi, Georgia. The Geospatial Operations Support Team made extensive use of OSM data and derivative services to support dialogue with the government: 1.) We used OSRM to identify and map the under-served population - those whose access to green space is currently poor; 2.) We worked with Global Green City Watch to deploy a machine learning model to interpret park land cover types. Their algorithm ranked Tbilisi’s parks on a range of environmental, social and economic measures. Though dialogue is still ongoing, the mayor recently announced that the city will be investing in four new parks, some in areas that our models predicted would benefit greatly from more green space. Finally, if there is time, I’d like to cap the presentation by talking about other ways the Bank is interacting with / using OSM (delivering OSM training / community building with TeachOSM, mapathons, and Rural Accessibility Index approximation).