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Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood

Author

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  • Boeing, Geoff

    (Northeastern University)

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.

Suggested Citation

  • Boeing, Geoff, 2019. "Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood," SocArXiv 7fxjz, Center for Open Science.
    • Handle: RePEc:osf:socarx:7fxjz
    DOI: 10.31219/osf.io/7fxjz
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    References listed on IDEAS

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    Cited by:

    1. Bowater, David & Stefanakis, Emmanuel, 2023. "Extending the Adapted PageRank Algorithm centrality model for urban street networks using non-local random walks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    2. Md Amiruzzaman & Ye Zhao & Stefanie Amiruzzaman & Aryn C. Karpinski & Tsung Heng Wu, 2023. "An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables," Journal of Computational Social Science, Springer, vol. 6(1), pages 315-337, April.
    3. Karolina Dudzic-Gyurkovich, 2023. "Study of Centrality Measures in the Network of Green Spaces in the City of Krakow," Sustainability, MDPI, vol. 15(18), pages 1-30, September.
    4. Nir Kaplan & Itzhak Omer, 2022. "Multiscale Accessibility—A New Perspective of Space Structuration," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    5. Nir Kaplan & David Burg & Itzhak Omer, 2022. "Multiscale accessibility and urban performance," Environment and Planning B, , vol. 49(2), pages 687-703, February.
    6. Boeing, Geoff, 2020. "Street Network Models and Indicators for Every Urban Area in the World," SocArXiv f2dqc, Center for Open Science.

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