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OSMnx
Original authorGeoff Boeing
DeveloperOSMnx contributors
Release2016 (2016)[1]
Stable release
2.1.1[2] Edit this on Wikidata / 21 July 2026
Written inPython
TypeSoftware library
LicenseMIT License
Websiteosmnx.readthedocs.io Edit this on Wikidata
Repository

OSMnx is a free and open-source Python package for downloading, modeling, analyzing, and visualizing street networks and other geospatial features from OpenStreetMap.[3][4] It represents spatial networks as NetworkX graph objects and can convert them to and from GeoPandas data structures.[5]

OSMnx is used in research on urban form, transportation, accessibility analysis, and travel behavior[6][7] and is taught in textbooks on spatial analysis and geographic data science.[8][9]

History

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OSMnx was developed by Geoff Boeing, a professor of urban planning and spatial analysis at the University of Southern California.[10] The project was initially motivated by the difficulty of acquiring consistently defined street network data and converting it into graph models suitable for reproducible analysis.[11] Its development reflected a broader effort to make spatial science software more accessible and to encode geographic theory in reusable research tools.[11] The package has been part of the growing ecosystem of open source software for urban planning and transport analysis,[6][7] and is commonly used for modeling and analyzing real-world networks from geospatial vector data.[12]

OSMnx was first released in November 2016.[1] As of 2026, it had been downloaded over 13 million times.[13] In 2025, OSMnx won the Zephyr Foundation's Exceptional Technical Achievement Award, which recognizes projects with high potential to improve transportation or land-use decision-making.[14] OSMnx has received media coverage from Bloomberg,[15] Forbes,[16][17] FlowingData,[18] Planetizen,[19] Domus,[20] MIT Technology Review,[21] and 99% Invisible.[22]

Features

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OSMnx can retrieve OpenStreetMap data for a named place, polygon, bounding box, or area around a point, and then model a network for walking, driving, cycling, or user-defined sets of OpenStreetMap ways.[3][8] It can also retrieve tagged OpenStreetMap features such as buildings, amenities, and transit stops.[3] It represents spatial networks as NetworkX directed multigraphs, allowing parallel edges and one-way flow constraints.[5][4]

OSMnx offers topological simplification algorithms that can merge nodes or edges such that nodes represent intersections and dead-ends and edges represent the street segments between them, while retaining the full original street geometry.[23] This reduces intersection overcounting and produces accurate measurements of intersection density, street segment length, and node degree.[23] Nodes and edges can be converted to GeoPandas data structures, projected to other coordinate reference systems, visualized, and saved in graph or GIS file formats.[3][24] The package supports shortest-path routing and calculates network statistics including street and intersection density, node degree, circuity, and street orientation.[8][3] OSMnx can also add elevation, street grade, speed, and travel-time attributes to the graph.[3]

Applications

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OSMnx has been used to analyze street networks at neighborhood, city, and multi-city scales. Systematic reviews of open-source urban analysis software have identified its use in street network and built environment research.[6][25] Transportation applications include modeling cyclists' route choices,[26] comparing travel times by car and public transport,[27] studying bicycle network growth,[28] and preparing road network data for traffic assignment models.[29] Beyond transportation, studies have used OSMnx to study spatial accessibility and justice,[30][31] segregation across multimodal transport networks,[32] traffic emissions modeling,[33] urban health indicators,[34] and machine learning research on urban form.[35]

See also

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References

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  1. 1 2 "osmnx release history". Python Package Index. Retrieved 11 July 2026.
  2. ↑ "Release 2.1.1". 21 July 2026. Retrieved 22 July 2026.
  3. 1 2 3 4 5 6 "OSMnx 2.1.0 documentation". Read the Docs. OSMnx contributors. Retrieved 11 July 2026.
  4. 1 2 Boeing, Geoff (2025). "Modeling and Analyzing Urban Networks and Amenities With OSMnx". Geographical Analysis. 57 (4): 567–577. Bibcode:2025GeoAn..57..567B. doi:10.1111/gean.70009.
  5. 1 2 Rey, Sergio J.; Arribas-Bel, Dani; Wolf, Levi John (2023). "Spatial Data". Geographic Data Science with Python. Chapman & Hall/CRC. doi:10.1201/9780429292507. ISBN 978-1-032-44595-3.
  6. 1 2 3 Yap, Winston; Janssen, Patrick; Biljecki, Filip (2022). "Free and open source urbanism: Software for urban planning practice". Computers, Environment and Urban Systems. 96 101825. Bibcode:2022CEUS...9601825Y. doi:10.1016/j.compenvurbsys.2022.101825.
  7. 1 2 Lovelace, Robin (2021). "Open source tools for geographic analysis in transport planning". Journal of Geographical Systems. 23 (4): 547–578. Bibcode:2021JGS....23..547L. doi:10.1007/s10109-020-00342-2.
  8. 1 2 3 McClain, Bonny P. (2022). "OpenStreetMap: Accessing Geospatial Data with OSMnx". Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence. O'Reilly Media. ISBN 978-1-098-10474-0.
  9. ↑ Lawhead, Joel (2023). Learning Geospatial Analysis with Python (4th ed.). Packt. ISBN 978-1-83763-917-5.
  10. ↑ "Geoff Boeing USC Price". USC Sol Price School of Public Policy. University of Southern California. Retrieved 11 July 2026.
  11. 1 2 Boeing, Geoff (2020). "The right tools for the job: The case for spatial science tool-building". Transactions in GIS. 24 (5): 1299–1314. arXiv:2008.05561. Bibcode:2020TrGIS..24.1299B. doi:10.1111/tgis.12678.
  12. ↑ Harish; Mooney, Peter; Galván, Edgar (2023). "A method for creating complex real-world networks using ESRI Shapefiles". MethodsX. 11 102426. doi:10.1016/j.mex.2023.102426. PMC 10587512. PMID 37867915.
  13. ↑ "osmnx · 14.0M downloads on PyPI". PyPI download statistics. pepy.tech. Retrieved 11 July 2026.
  14. ↑ "Exceptional Technical Achievement Award". Zephyr Foundation. 7 January 2025. Archived from the original on 16 November 2025. Retrieved 11 July 2026.
  15. ↑ Bliss, Laura (17 January 2017). "A Digital Window Into Your City's Urban Form". Bloomberg CityLab. Retrieved 11 July 2026.
  16. ↑ Winkless, Laurie (7 February 2017). "Understanding Our Cities, Thanks To Beautiful Maps". Forbes. Retrieved 11 July 2026.
  17. ↑ McCue, TJ (30 December 2019). "See Your City In A New Way". Forbes. Retrieved 11 July 2026.
  18. ↑ "One square mile in different cities". FlowingData. 10 February 2017. Retrieved 11 July 2026.
  19. ↑ Brasuell, James (27 January 2017). "Friday Eye Candy: Comparing a Square Mile of the World's Famous Cities". Planetizen. Retrieved 11 July 2026.
  20. ↑ Domus (23 January 2017). "Do-it-yourself city mapping". Archived from the original on 9 July 2017. Retrieved 11 July 2026.
  21. ↑ Emerging Technology from the arXiv (16 October 2019). "What makes a city great? A new way to look at urban data will give us clues". MIT Technology Review. Retrieved 11 July 2026.
  22. ↑ Kohlstedt, Kurt (20 July 2018). "On the Grid: Visualizing Street Network Orientations Across 50 Global Cities". 99% Invisible. Retrieved 11 July 2026.
  23. 1 2 Boeing, Geoff (2025). "Topological Graph Simplification Solutions to the Street Intersection Miscount Problem". Transactions in GIS. 29 (3) e70037. Bibcode:2025TrGIS..2970037B. doi:10.1111/tgis.70037.
  24. ↑ Abdeldayem, Walid Samir; Geddes, Ilaria; Eldesoky, Ahmed Hazem; Stavroulaki, Ioanna; Simons, Gareth; Berghauser Pont, Meta; Charalambous, Nadia (24 March 2026). "Automated versus hybrid street network modelling for centrality and accessibility analysis". Environment and Planning B: Urban Analytics and City Science 23998083261433647. doi:10.1177/23998083261433647.
  25. ↑ Milovanović, Aleksandra; Šošević, Uroš; Cvetković, Nikola; Pešić, Mladen; Janković, Stefan; Krstić, Verica; Ristić Trajković, Jelena; Milojević, Milica; Nikezić, Ana; Simić, Dejan; Djokić, Vladan (2025). "Mapping Digital Solutions for Multi-Scale Built Environment Observation: A Cluster-Based Systematic Review". Smart Cities. 8 (6): 196. doi:10.3390/smartcities8060196.
  26. ↑ Alattar, Mohammad Anwar; Cottrill, Caitlin; Beecroft, Mark (2021). "Modelling cyclists' route choice using Strava and OSMnx: A case study of the City of Glasgow". Transportation Research Interdisciplinary Perspectives. 9 100301. Bibcode:2021TrRIP...900301A. doi:10.1016/j.trip.2021.100301.
  27. ↑ Liao, Yuan; Gil, Jorge; Pereira, Rafael H. M.; Yeh, Sonia; Verendel, Vilhelm (2020). "Disparities in travel times between car and transit: Spatiotemporal patterns in cities". Scientific Reports. 10 (1) 4056. Bibcode:2020NatSR..10.4056L. doi:10.1038/s41598-020-61077-0. PMC 7055332. PMID 32132647.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  28. ↑ Szell, Michael; Mimar, Sayat; Perlman, Tyler; Ghoshal, Gourab; Sinatra, Roberta (2022). "Growing urban bicycle networks". Scientific Reports. 12 (1) 6765. arXiv:2107.02185. Bibcode:2022NatSR..12.6765S. doi:10.1038/s41598-022-10783-y. PMC 9039277. PMID 35474086.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  29. ↑ Xu, Xiaotong; Zheng, Zhenjie; Hu, Zijian; Feng, Kairui; Ma, Wei (2024). "A unified dataset for the city-scale traffic assignment model in 20 U.S. cities". Scientific Data. 11 (1) 325. Bibcode:2024NatSD..11..325X. doi:10.1038/s41597-024-03149-8. PMC 10980787. PMID 38553541.
  30. ↑ Masuyama, Atsushi (2022). "The potential use of Python in network-distance-based spatial accessibility analysis". Theory and Applications of GIS. 30 (1): 11–18. doi:10.5638/thagis.30.11.
  31. ↑ Nelson, Ruth; Warnier, Martijn; Verma, Trivik (2026). "MAP: Mapping accessibility for ethically informed urban planning". Environment and Planning B: Urban Analytics and City Science. 53 (3): 516–524. Bibcode:2026EnPlB..53..516N. doi:10.1177/23998083251387382.
  32. ↑ Neira, Mateo; Molinero, Carlos; Marshall, Stephen; Arcaute, Elsa (2024). "Urban segregation on multilayered transport networks: a random walk approach". Scientific Reports. 14 (1) 8370. arXiv:2309.11901. Bibcode:2024NatSR..14.8370N. doi:10.1038/s41598-024-58932-9. PMC 11006669. PMID 38600261.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  33. ↑ Hofer, Christian; Jäger, Georg; Füllsack, Manfred (2018). "Large scale simulation of CO2 emissions caused by urban car traffic: An agent-based network approach". Journal of Cleaner Production. 183: 1–10. doi:10.1016/j.jclepro.2018.02.113.
  34. ↑ SALURBAL Group (2019). "Building a Data Platform for Cross-Country Urban Health Studies: the SALURBAL Study". Journal of Urban Health. 96 (2): 311–337. doi:10.1007/s11524-018-00326-0. PMC 6458229. PMID 30465261.
  35. ↑ Law, Stephen; Neira, Mateo (2019). "An unsupervised approach to geographical knowledge discovery using street level and street network images". Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. pp. 56–65. arXiv:1906.11907. doi:10.1145/3356471.3365238.
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