Mapping public space in urban neighbourhoods using OpenStreetMap data

Abstract

Space in cities is contested and public space is an important resource for the urban society in this context. Against the background of social justice, sufficient and fairly distributed public space in a city is to be strived for. In order to investigate the status quo of spatial distribution and availability of public spaces, this research aims to develop a framework for identifying and categorizing public spaces using open geospatial data. Public accessibility is used as the defining criterion for public space, which is derived from freely available OpenStreetMap data on three levels of analysis. The framework is implemented as a partially automated Python script and is available as open source via the online platform GitHub. To test the applicability of the approach, two case studies are conducted in Vienna, Austria. In the form of a map, inspired by the cartographic design of the historic Nolli map, and a quantitative comparison of the areas, the two case studies can be contrasted in terms of their public space. The case study application shows that the OpenStreetMap data by itself and without extensive adding of data is not sufficient to perform a proper public accessibility analysis. Therefore, for the case studies, the OpenStreetMap data is examined and expanded by on-site mapping. With the semi-automated processing of open data, the framework extends the previously developed approaches to create a contemporary Nolli map and provides a contribution to the debate on the distribution of space in a city.