A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2021Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networkscited by 8
- 2021Classification of Urban Morphology with Deep Learning: Application on Urban Vitalitycited by 8
- 2022Large-Scale Auto-Regressive Modeling Of Street Networkscited by 2
- 2024Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Networkcited by 0
- 2026Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learningcited by 0
- 2026Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning17 Mar 2026
- 2024Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network23 May 2024
- 2022Large-Scale Auto-Regressive Modeling Of Street Networks1 Sep 2022
- 2021Classification of Urban Morphology with Deep Learning: Application on Urban Vitality7 May 2021
- 2021Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks1 Jan 2021