OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks

25 Pages Posted: 13 Nov 2016 Last revised: 9 Jul 2017

See all articles by Geoff Boeing

Geoff Boeing

Northeastern University - School of Public Policy and Urban Affairs; Northeastern University - Network Science Institute

Date Written: May 7, 2017

Abstract

Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

Keywords: street, network, urban design, urban form, transportation, land use, python, openstreetmap, complexity, routing, graph, GIS, visualization, centrality, complex networks, resilience

JEL Classification: C45, D85, O18, C63, C88

Suggested Citation

Boeing, Geoff, OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks (May 7, 2017). Available at SSRN: https://ssrn.com/abstract=2865501 or http://dx.doi.org/10.2139/ssrn.2865501

Geoff Boeing (Contact Author)

Northeastern University - School of Public Policy and Urban Affairs ( email )

343 Holmes Hall
360 Huntington Avenue
Boston, MA 02115
United States

Northeastern University - Network Science Institute ( email )

177 Huntington Avenue
Boston, MA MA 02115
United States

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