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

Abstract

a b s t r a c t a r t i c l e i n f oUrban 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.

Entropy of city street networks linked to future spatial navigation ability

“…This effect has previously been theorized in the urban design literature [24,25], but the empirical studies on street networks suffer from limitations, mostly due to data availability, gathering, and processing constraints [26,27]. To overcome these limitations, we coupled our global dataset with OSMnx, a new tool allowing to download street networks for anywhere in the world via OpenStreetMap [15,28]. We hypothesized that people who grew up in cities with more complex street networks (e.g.…”

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

“…Ghent is a tourist town with many visitor sites, such as old buildings and museums and, like many old towns, has an entangled network of walkways, making it a suitable example due to its complexity. For our purposes, we used a squared area of N, S, E, and W each 525 m from location point 51.05660 N, 3.721500 E. Figure 4 represents a map with walkways and building footprint of this area, obtained with the OSMnx [47] Python module. Paths (white lines) on this map are similar to a maze, which is a challenge for ANT.…”

Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications

“…C is obtained with a procedure. At first, CrowdSenSim downloads the walkable city graph of OpenStreetMap (OSM) through OSMnx Python package [35]. Unfortunately, OSM street nodes are inconsistent for direct use in CrowdSenSim because they include dead-ends, intersections and all the points in a segment when the streets curve.…”