My article, “A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood,” was recently published in Environment and Planning B: Urban Analytics and City Science. This study uses OSMnx to download and analyze 27,000 street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales – namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs.
These 27,000 street networks as well as their measures have been shared in a free public repository at the Harvard Dataverse for anyone to re-purpose. This study’s empirical findings emphasize measures relevant to graph theory, transportation, urban design, and morphology, such as structure, connectedness, density, centrality, and resilience. It uses graph Maximum Betweenness Centrality and Average Node Connectivity to examine how “resilient” a street network is, in terms of how reliant it is on important nodes and how easy it is to disconnect it.