OpenStreetMap is a valuable, widely used data source for transport and geographic analysis, owing partly to its global coverage and permissive license. Micro-mobility, electrification and automation transport trends are motivating the production of increasingly high-resolution maps of urban streetspace. Simultaneously, sustainable transport within urban areas is motivating increased prioritisation of placemaking, impacting streetspace allocation. Extracting OSM data from 117 worldwide cities and comparing to alternative data sources we highlight how the OSM data model could be used to support the collection and analysis of streetspace data. Furthermore, we discuss the implicit assumptions of streetspace use encoded by particular streetspace data representations.