Abstract

OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools.

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The different versions of the original document can be found in:

https://doaj.org/toc/2413-8851 under the license cc-by
https://www.mdpi.com/2413-8851/3/1/28/pdf,
https://escholarship.org/uc/item/9v99703k,
https://academic.microsoft.com/#/detail/2916744195
http://dx.doi.org/10.3390/urbansci3010028
under the license https://creativecommons.org/licenses/by/4.0/


DOIS: 10.3390/urbansci3010028 10.31235/osf.io/7fxjz

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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.3390/urbansci3010028
Licence: Other

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