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

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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Street Network Models and Indicators for Every Urban Area in the World

10.31235/osf.io/f2dqc ◽

2020 ◽

Author(s):

Geoff Boeing

Keyword(s):

Open Source ◽

Urban Area ◽

Open Data ◽

Network Models ◽

Data Repository ◽

Street Network ◽

Data Repositories ◽

Urban Street ◽

The World ◽

Network Form

Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of each urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open-source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study's reproducible computational workflow, introduces two new open data repositories of ready-to-use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open-source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator dataset has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.

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Global trends toward urban street-network sprawl

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1905232116 ◽

2020 ◽

Vol 117 (4) ◽

pp. 1941-1950 ◽

Cited By ~ 3

Author(s):

Christopher Barrington-Leigh ◽

Adam Millard-Ball

Keyword(s):

Time Series ◽

Sustainable Urban Development ◽

Street Network ◽

High Resolution Data ◽

Global Trends ◽

Urban Street ◽

Graph Theoretic ◽

Time Period ◽

Path Dependent ◽

Changes Over Time

We present a global time series of street-network sprawl—that is, sprawl as measured through the local connectivity of the street network. Using high-resolution data from OpenStreetMap and a satellite-derived time series of urbanization, we compute and validate changes over time in multidimensional street connectivity measures based on graph-theoretic and geographic concepts. We report on global, national, and city-level trends since 1975 in the street-network disconnectedness index (SNDi), based on every mapped node and edge in the world. Streets in new developments in 90% of the 134 most populous countries have become less connected since 1975, while just 29% show an improving trend since 2000. The same period saw a near doubling in the relative frequency of a street-network type characterized by high circuity, typical of gated communities. We identify persistence in street-network sprawl, indicative of path-dependent processes. Specifically, cities and countries with low connectivity in recent years also had relatively low preexisting connectivity in our earliest time period. We discuss implications for policy intervention in road building in new and expanding cities as a top priority for sustainable urban development.

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Global trends towards urban street-network sprawl

10.31219/osf.io/2cp5u ◽

2019 ◽

Author(s):

Christopher Paul Barrington-Leigh ◽

Adam Millard-Ball

Keyword(s):

Time Series ◽

Sustainable Urban Development ◽

Street Network ◽

High Resolution Data ◽

Global Trends ◽

Urban Street ◽

Graph Theoretic ◽

Time Period ◽

Path Dependent ◽

Changes Over Time

We present the first global time series of street-network sprawl — that is, sprawl as measured through the local connectivity of the street network. Using high-resolution data from OpenStreetMap and a satellite-derived time series of urbanization, we compute and validate changes over time in multidimensional street connectivity measures based on graph-theoretic and geographic concepts. We report on global, national, and city-level trends since 1975 in the Street-Network Disconnectedness Index (SNDi), based on every mapped node and edge in the world. Streets in new developments in 90% of the 134 most populous countries have become less connected since 1975, while just 29% show an improving trend since 2000. The same period saw a near doubling in the relative frequency of a street-network type characterized by high circuity, typical of gated communities. We identify persistence in street-network sprawl, indicative of path-dependent processes. Specifically, cities and countries with low connectivity in recent years also had relatively low preexisting connectivity in our earliest time period. We discuss implications for policy intervention in road building in new and expanding cities as a top priority for sustainable urban development.

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A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

Environment and Planning B Urban Analytics and City Science ◽

10.1177/2399808318784595 ◽

2018 ◽

Vol 47 (4) ◽

pp. 590-608 ◽

Cited By ~ 24

Author(s):

Geoff Boeing

Keyword(s):

Urban Form ◽

Ad Hoc ◽

Directed Graphs ◽

Street Network ◽

Network Characteristics ◽

Urban Street ◽

Multi Scale ◽

Street Networks ◽

Urbanized Area ◽

Data Acquisition And Processing

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales—namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study 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 street networks and measures data have been shared in a public repository for other researchers to use.

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A global assessment of street network sprawl

10.31219/osf.io/6vp8j ◽

2019 ◽

Author(s):

Christopher Paul Barrington-Leigh ◽

Adam Millard-Ball

Keyword(s):

Energy Use ◽

Dendritic Tree ◽

The United States ◽

Street Network ◽

Cross Sectional ◽

Standard Deviation Increase ◽

Urban Street ◽

Graph Theoretic ◽

Low Levels ◽

Using Data

Disconnected urban street networks, which we call “street-network sprawl,” are strongly associated with increased vehicle travel, energy use and CO_{2} emissions, as shown by previous research in Europe and North America. In this paper, we provide the first systematic and globally commensurable measures of street-network sprawl based on graph-theoretic and geographic concepts. Using data on all 46 million km of mapped streets worldwide, we compute these measures for the entire Earth at the highest possible resolution. We generate a summary scalar measure for street-network sprawl, the Street-Network Disconnectedness index (SNDi), as well as a data-driven multidimensional classification that identifies eight empirical street-network types that span the spectrum of connectivity, from gridiron to dendritic (tree-like) and circuitous networks. Our qualitative validation shows that both the scalar and multidimensional measures are meaningfully comparable within and across countries, and successfully capture varied dimensions of walkability and urban development. We further show that in select high-income countries, our measures explain cross-sectional variation in household transportation decisions, and a one standard-deviation increase in SNDi is associated with an extra 0.25 cars owned per household. We aggregate our measures to the scale of countries, cities, and smaller geographies and describe patterns in street-network sprawl around the world. Latin America, Japan, South Korea, much of Europe and North Africa stand out for their low levels of street-network sprawl, while the highest levels are found in south-east Asia, the United States and the British Isles.

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A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood

10.31235/osf.io/hmhts ◽

2017 ◽

Cited By ~ 5

Author(s):

Geoff Boeing

Keyword(s):

Urban Form ◽

Ad Hoc ◽

Directed Graphs ◽

Street Network ◽

Network Characteristics ◽

Urban Street ◽

Multi Scale ◽

Street Networks ◽

Urbanized Area ◽

Data Acquisition And Processing

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales - namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study 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 street networks and measures data have been shared in a public repository for other researchers to use.

Download Full-text