Development Status
- 5 - Production/Stable
Intended Audience
- Developers
- Science/Research
License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python
- Python :: 3
- Python :: 3 :: Only
- Python :: 3.9
- Python :: 3.10
- Python :: 3.11
- Python :: 3.12
- Python :: 3.13
Topic
- Scientific/Engineering :: GIS
- Scientific/Engineering :: Information Analysis
- Scientific/Engineering :: Mathematics
- Scientific/Engineering :: Physics
- Scientific/Engineering :: Visualization
OSMnx
OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap. You can download and model walking, driving, or biking networks with a single line of code then analyze and visualize them. You can just as easily work with urban amenities/points of interest, building footprints, transit stops, elevation data, street orientations, speed/travel time, and routing.
OSMnx 2.0 is released: read the migration guide.
Citation
If you use OSMnx in your work, please cite the paper:
Boeing, G. (2025). Modeling and Analyzing Urban Networks and Amenities with OSMnx. Geographical Analysis, published online ahead of print. doi:10.1111/gean.70009
Getting Started
First read the Getting Started guide for an introduction to the package and FAQ.
Then work through the Examples Gallery for step-by-step tutorials and sample code.
Installation
Follow the Installation guide to install OSMnx.
Support
If you have any trouble, consult the User Reference. The OSMnx repository is hosted on GitHub. If you have a "how-to" or usage question, please ask it on StackOverflow, as we reserve the repository's issue tracker for bug tracking and feature development.
License
OSMnx is open source and licensed under the MIT license. OpenStreetMap's open data license requires that derivative works provide proper attribution. Refer to the Getting Started guide for usage limitations.
Wheel compatibility matrix
Files in release
geopandas
(>=1.0.1)
networkx
(>=2.5)
numpy
(>=1.22)
pandas
(>=1.4)
requests
(>=2.27)
shapely
(>=2.0)