Abstract. Complexity manifests itself in the physical form of cities through various processes of self-organization and top-down planning. The study of complex street networks has become a popular area of enquiry into urban evolution and dynamics, but such studies tend to struggle with data acquisition and consistency. This talk presents OSMnx, a new Python-based tool to make the collection of data and the creation and analysis of street networks simple, consistent, and automatable for any study site in the world. OSMnx contributes five new capabilities for urban researchers: first, the automated downloading of administrative boundaries and shapefiles; second, the tailored and automated downloading and construction of street networks from OpenStreetMap; third, the algorithmic correction and simplification of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, calculate routes, project and visualize networks, and calculate metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, it presents preliminary research that examines 27,000 street networks at various scales across the U.S.
OSMnx is freely available online at https://github.com/gboeing/osmnx
Bio: Geoff Boeing is a Ph.D. Candidate in City and Regional Planning at the University of California, Berkeley. His research revolves around urban data science, urban form, and complexity. This includes studying street networks and the relationship between the deterministic nature of urban design and the emergent characteristics of urban form that arise out of complex systems. His research has been covered by The Washington Post, The San Francisco Chronicle, Discovery News, Fast Company, CityLab, and various other media outlets. He is also the co-lead instructor for Urban Informatics and Visualization, a Python-based urban data science course at UC Berkeley.