OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
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Abstract (arXiv)
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.
Key facts. Cited by 109 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2016.
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Citing papers per year: 2021: 16; 2022: 18; 2023: 19; 2024: 20; 2025: 17; 2026: 19.
109 papers.
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- 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problemscited by 14
- 2021NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanismscited by 13
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12 most cited of 103 All 103 (JSON)
- 2026Caption-Mediated Perceived-Safety Estimation for Pedestrian Routing29 Sep 2026
- 2026Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees15 Sep 2026
- 2026Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework10 Sep 2026
- 2026When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions10 Sep 2026
- 2026Emergent Charging Coordination in Electric Delivery Fleets7 Sep 2026
- 2026AERIS: Offline Policy Improvement for Multi-UAV Integrated Sensing and Communication26 Aug 2026
- 2026From Street View Imagery to Street Quality Indicators: Vision Language Inference for the Suburban 15-minute City20 Aug 2026
- 2026Graph Instance Landscapes: When Structural Similarity Does (Not) Reflect Shortest-Path Performance1 Jun 2026
- 2026From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs28 May 2026
- 2026Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction23 Apr 2026
- 2026AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT22 Apr 2026
- 2026Bridge-Centered Metapath Classification Using R-GCN-VGAE for Disaster-Resilient Maintenance Decisions20 Apr 2026
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