Software:OSMnx

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OSMnx
Original author(s)Geoff Boeing
Developer(s)OSMnx contributors
Initial release2016 (2016)[1]
Written inPython
TypeSoftware library
LicenseMIT License

OSMnx is a free and open-source Python package for downloading, modeling, analyzing, and visualizing street networks and other geospatial features from OpenStreetMap.[2][3] It represents spatial networks as NetworkX graph objects and can convert them to and from GeoPandas data structures.[4]

OSMnx is used in research on urban form, transportation, accessibility analysis, and travel behavior[5][6] and is taught in textbooks on spatial analysis and geographic data science.[7][8]

History

OSMnx was developed by Geoff Boeing, a professor of urban planning and spatial analysis at the University of Southern California.[9] 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.[10] Its development reflected a broader effort to make spatial science software more accessible and to encode geographic theory in reusable research tools.[10] The package has been part of the growing ecosystem of open source software for urban planning and transport analysis,[5][6] and is commonly used for modeling and analyzing real-world networks from geospatial vector data.[11]

OSMnx was first released in November 2016.[1] As of 2026, it had been downloaded over 13 million times.[12] 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.[13] OSMnx has received media coverage from Bloomberg,[14] Forbes,[15][16] FlowingData,[17] Planetizen,[18] Domus,[19] MIT Technology Review,[20] and 99% Invisible.[21]

Features

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.[2][7] It can also retrieve tagged OpenStreetMap features such as buildings, amenities, and transit stops.[2] It represents spatial networks as NetworkX directed multigraphs, allowing parallel edges and one-way flow constraints.[4][3]

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.[22] This reduces intersection overcounting and produces accurate measurements of intersection density, street segment length, and node degree.[22] 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.[2][23] The package supports shortest-path routing and calculates network statistics including street and intersection density, node degree, circuity, and street orientation.[7][2] OSMnx can also add elevation, street grade, speed, and travel-time attributes to the graph.[2]

Applications

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.[5][24] Transportation applications include modeling cyclists' route choices,[25] comparing travel times by car and public transport,[26] studying bicycle network growth,[27] and preparing road network data for traffic assignment models.[28] Beyond transportation, studies have used OSMnx to study spatial accessibility and justice,[29][30] segregation across multimodal transport networks,[31] traffic emissions modeling,[32] urban health indicators,[33] and machine learning research on urban form.[34]

See also

References

  1. ↑ 1.0 1.1 "osmnx release history". https://pypi.org/project/osmnx/#history. 
  2. ↑ 2.0 2.1 2.2 2.3 2.4 2.5 "OSMnx 2.1.0 documentation". OSMnx contributors. https://osmnx.readthedocs.io/en/latest/. 
  3. ↑ 3.0 3.1 Boeing, Geoff (2025). "Modeling and Analyzing Urban Networks and Amenities With OSMnx". Geographical Analysis 57 (4): 567–577. doi:10.1111/gean.70009. Bibcode: 2025GeoAn..57..567B. 
  4. ↑ 4.0 4.1 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. https://geographicdata.science/book/notebooks/03_spatial_data.html. 
  5. ↑ 5.0 5.1 5.2 Yap, Winston; Janssen, Patrick; Biljecki, Filip (2022). "Free and open source urbanism: Software for urban planning practice". Computers, Environment and Urban Systems 96. doi:10.1016/j.compenvurbsys.2022.101825. Bibcode: 2022CEUS...9601825Y. 
  6. ↑ 6.0 6.1 Lovelace, Robin (2021). "Open source tools for geographic analysis in transport planning". Journal of Geographical Systems 23 (4): 547–578. doi:10.1007/s10109-020-00342-2. Bibcode: 2021JGS....23..547L. 
  7. ↑ 7.0 7.1 7.2 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. https://www.oreilly.com/library/view/python-for-geospatial/9781098104788/. 
  8. ↑ Lawhead, Joel (2023). Learning Geospatial Analysis with Python (4th ed.). Packt. ISBN 978-1-83763-917-5. https://www.packtpub.com/en-de/product/learning-geospatial-analysis-with-python-fourth-edition-9781837639175. 
  9. ↑ "Geoff Boeing USC Price". University of Southern California. https://priceschool.usc.edu/faculty/directory/geoff-boeing/. 
  10. ↑ 10.0 10.1 Boeing, Geoff (2020). "The right tools for the job: The case for spatial science tool-building". Transactions in GIS 24 (5): 1299–1314. doi:10.1111/tgis.12678. Bibcode: 2020TrGIS..24.1299B. 
  11. ↑ Harish; Mooney, Peter; Galván, Edgar (2023). "A method for creating complex real-world networks using ESRI Shapefiles". MethodsX 11. doi:10.1016/j.mex.2023.102426. PMID 37867915. 
  12. ↑ "osmnx · 14.0M downloads on PyPI". pepy.tech. https://pepy.tech/projects/osmnx. 
  13. ↑ "Exceptional Technical Achievement Award". Zephyr Foundation. 7 January 2025. https://zephyrtransport.org/technical-achievement-award/. 
  14. ↑ Bliss, Laura (17 January 2017). "A Digital Window Into Your City's Urban Form". Bloomberg CityLab. https://www.bloomberg.com/news/articles/2017-01-17/a-digital-window-into-your-city-s-urban-form. 
  15. ↑ Winkless, Laurie (7 February 2017). "Understanding Our Cities, Thanks To Beautiful Maps". Forbes. https://www.forbes.com/sites/lauriewinkless/2017/02/07/understanding-our-cities-thanks-to-beautiful-maps/. 
  16. ↑ McCue, TJ (30 December 2019). "See Your City In A New Way". Forbes. https://www.forbes.com/sites/tjmccue/2020/12/30/see-your-city-in-a-new-way/. 
  17. ↑ "One square mile in different cities". 10 February 2017. https://flowingdata.com/2017/02/10/one-square-mile/. 
  18. ↑ Brasuell, James (27 January 2017). "Friday Eye Candy: Comparing a Square Mile of the World's Famous Cities". Planetizen. https://www.planetizen.com/node/90903/friday-eye-candy-comparing-square-mile-worlds-famous-cities. 
  19. ↑ Domus (23 January 2017). "Do-it-yourself city mapping". https://loves.domusweb.it/do-it-yourself-city-mapping/. 
  20. ↑ 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. https://www.technologyreview.com/2019/10/16/340/what-makes-a-city-great-a-new-way-to-look-at-urban-data-will-give-us-clues/. 
  21. ↑ Kohlstedt, Kurt (20 July 2018). "On the Grid: Visualizing Street Network Orientations Across 50 Global Cities". https://99percentinvisible.org/article/on-the-grid-visualizing-street-network-orientations-across-50-global-cities/. 
  22. ↑ 22.0 22.1 Boeing, Geoff (2025). "Topological Graph Simplification Solutions to the Street Intersection Miscount Problem". Transactions in GIS 29 (3). doi:10.1111/tgis.70037. Bibcode: 2025TrGIS..2970037B. 
  23. ↑ 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. doi:10.1177/23998083261433647. 
  24. ↑ Milovanović, Aleksandra; Šošević, Uroš; Cvetković, Nikola; Pešić, Mladen; Janković, Stefan; Krstić, Verica; Ristić Trajković, Jelena; Milojević, Milica et al. (2025). "Mapping Digital Solutions for Multi-Scale Built Environment Observation: A Cluster-Based Systematic Review". Smart Cities 8 (6): 196. doi:10.3390/smartcities8060196. 
  25. ↑ 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. doi:10.1016/j.trip.2021.100301. Bibcode: 2021TrRIP...900301A. 
  26. ↑ 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). doi:10.1038/s41598-020-61077-0. PMID 32132647. Bibcode: 2020NatSR..10.4056L. 
  27. ↑ Szell, Michael; Mimar, Sayat; Perlman, Tyler; Ghoshal, Gourab; Sinatra, Roberta (2022). "Growing urban bicycle networks". Scientific Reports 12 (1). doi:10.1038/s41598-022-10783-y. PMID 35474086. Bibcode: 2022NatSR..12.6765S. 
  28. ↑ 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). doi:10.1038/s41597-024-03149-8. PMID 38553541. Bibcode: 2024NatSD..11..325X. 
  29. ↑ 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. 
  30. ↑ 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. doi:10.1177/23998083251387382. Bibcode: 2026EnPlB..53..516N. 
  31. ↑ Neira, Mateo; Molinero, Carlos; Marshall, Stephen; Arcaute, Elsa (2024). "Urban segregation on multilayered transport networks: a random walk approach". Scientific Reports 14 (1). doi:10.1038/s41598-024-58932-9. PMID 38600261. Bibcode: 2024NatSR..14.8370N. 
  32. ↑ 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. 
  33. ↑ 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. PMID 30465261. 
  34. ↑ Law, Stephen; Neira, Mateo (2019). "An unsupervised approach to geographical knowledge discovery using street level and street network images". pp. 56–65. doi:10.1145/3356471.3365238. 



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