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.
Highlights
Urban planners and transportation engineers have examined and modeled street networks for decades to explore household travel behavior, accessibility and equity, urban form and design patterns, connectivity, and centrality [1–26]. Complex networks such as street networks have been explored from the perspective of statistical physics to assess structure and performance [27–44]
Large volumes of cross-sectional street network data—in a format well-suited for graph-theoretic analysis—have often been difficult to come by, especially in an open-source, scalable, automatable way
OSMnx considers two-way “intersections” to be topologically identical to a single street that bends around a curve. To retain these intersections when the incident edges have different OpenStreetMap IDs, we can use OSMnx’s non-strict mode. This cleaning step is critical to this dataset, providing additional value beyond extraction of data from OpenStreetMap itself by producing models more suitable to urban design/morphology and transportation analysis by representing intersections/dead-ends as nodes and linear blocks’ sides as edges
Summary
Urban planners and transportation engineers have examined and modeled street networks for decades to explore household travel behavior, accessibility and equity, urban form and design patterns, connectivity, and centrality [1–26]. It describes a research project that downloaded raw OpenStreetMap street network data for the entire U.S, cleaned these data, constructed graph-theoretic models of these data at multiple scales for fast and rigorous urban analysis It calculated dozens of metric and topological measures of each network, nationwide. These models and measures can save researchers weeks or months of ad hoc data collection and analysis They are even more useful for urban planners and policymakers who often lack the technical capacity to write their own API scripts in custom query languages or implement their own network science algorithms to understand the built form and urban circulation. Urban Sci. 2019, 3, 28 to spatial network analysis—unlike OpenStreetMap’s raw geometry data downloads—out of the box These models all have meaningful spatial extents (municipalities, counties, urbanized areas, census tracts, and neighborhoods) that correspond to administrative boundaries and socio-spatial units in useful ways for urban analysis.
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