OSMnx version 2.0.0 has been released. This has been a massive effort over the past year to streamline the package’s API, re-think its internal organization, and optimize its code. Today OSMnx is faster, more memory efficient, and fully type-annotated for a better user experience.
If you haven’t used it before, OSMnx is a Python package to easily download, model, analyze, and visualize street networks and any other geospatial features from OpenStreetMap. You can download and model walking, driving, or biking networks with a single line of code then quickly 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.
This has now been a labor of love for me for about 9 years. Wow. I initially developed this package to enable the empirical research for my dissertation. Since then, it has powered probably 2/3 of the articles I’ve published over the years. And it has received hundreds of contributions from many other code contributors. Thank you to everyone who helped make this possible.
I hope you find the package as useful as I do. Now I’m looking forward to all of your bug reports.
Geoff Boeing
Zephyr Foundation Award
I am happy to share that I was awarded the Zephyr Foundation’s 2025 Exceptional Technical Achievement Award for my work on OSMnx. This annual award recognizes a project that has had a positive impact on the fields or transportation and/or land use decision-making.
This year will mark the 10th anniversary of my work on the OSMnx project. It recently reached version 2.0 with a slew of new features and enhancements. If you haven’t used it before, OSMnx is a Python package to easily download, model, analyze, and visualize street networks and any other geospatial features from OpenStreetMap. You can download and model walking, driving, or biking networks with a single line of code then quickly 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.
If you’re interested in this tool, you can read more about it here.
Global Healthy and Sustainable City Indicators
I recently co-authored an article, “Global Healthy and Sustainable City Indicators: Collaborative Development of an Open Science Toolkit for Calculating and Reporting on Urban Indicators Internationally,” now published in Environment and Planning B: Urban Analytics and City Science. This was a collaboration with my colleagues at the Global Observatory of Healthy and Sustainable Cities, in which we discuss our spatial software co-development process with collaborators and practitioners around the world.
From the abstract:
For more, check out the article.
Surfacic Networks
I recently coauthored an article titled “Surfacic Networks” in PNAS Nexus with Marc Barthelemy, Alain Chiaradia, and Chris Webster. We propose the concept of surfacic networks to describe a class of spatial networks embedded in non-flat two-dimensional manifolds (e.g., the Earth’s surface), and what this means for distance metrics and lazy path solving when accounting for fluctuations in the manifold’s curvature (e.g., changes in elevation on Earth’s surface).
For more, check out the article.
OSMnx 2.0 Released
OSMnx version 2.0.0 has been released. This has been a massive effort over the past year to streamline the package’s API, re-think its internal organization, and optimize its code. Today OSMnx is faster, more memory efficient, and fully type-annotated for a better user experience.
If you haven’t used it before, OSMnx is a Python package to easily download, model, analyze, and visualize street networks and any other geospatial features from OpenStreetMap. You can download and model walking, driving, or biking networks with a single line of code then quickly 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.
This has now been a labor of love for me for about 9 years. Wow. I initially developed this package to enable the empirical research for my dissertation. Since then, it has powered probably 2/3 of the articles I’ve published over the years. And it has received hundreds of contributions from many other code contributors. Thank you to everyone who helped make this possible.
I hope you find the package as useful as I do. Now I’m looking forward to all of your bug reports.
Access to the Exclusive City
I recently coauthored an article in Urban Studies with Julia Harten titled “Access to the Exclusive City: Home Sharing as an Affordable Housing Strategy.” We examined how shared housing serves increasingly diverse populations as a pathway into otherwise unaffordable housing submarkets.
From the abstract:
For more, check out the article.
A Roadmap for Data-Driven Urban Research
I recently coauthored an article in the journal Cities titled “A Road Map for Future Data-Driven Urban Planning and Environmental Health Research.” This arose from a symposium in Sitges, Spain which I was invited to last year by the Barcelona Institute for Global Health.
From the abstract:
For more, check out the article.
The Structure of Street Networks
I recently coauthored an article titled “A Review of the Structure of Street Networks” with Marc Barthelemy in Transport Findings. On a personal note, Marc has long been a personal hero of mine and was the 2nd most cited author in my dissertation, after Mike Batty (who I also recently had the pleasure of collaborating with).
For more, check out the article.
Urban Form, Transport, Environment and Health
I recently coauthored an article in Environmental Research, titled “Exploring the Nexus of Urban Form, Transport, Environment and Health in Large-Scale Urban Studies: A State-of-the-Art Scoping Review.” This arose from a symposium in Sitges, Spain which I was invited to last year by the Barcelona Institute for Global Health.
From the abstract:
For more, check out the article.
AI and NLP for Urban Mixed Methods Research
One area where urban AI research seems promising is in mixed methods work. For example, it’s hard to use traditional qualitative methods on really large text data sets because of the overwhelming manual labor involved. But if you could train a model to do, say, topic labeling for you, you’d be able to (potentially) analyze nearly unlimited text data nearly instantly after that initial training work. The mixed methods holy grail.
I coauthored an article recently in Computers, Environment and Urban Systems with Madison Lore and Julia Harten which takes up this challenge. Using Los Angeles’s housing crisis and rental market as a case study, we demonstrate how and when modern AI and NLP techniques can generate qualitative insights on par with traditional manual techniques, but at a far larger scale and requiring far less labor.
OSMnx 2.0 Beta
OSMnx v2.0.0 is targeted for release later in 2024. This major release includes some breaking changes (including removing previously deprecated functionality) that are not backwards compatible with v1. See the migration guide and reference paper for details.
The first beta pre-release is out now, and testers are needed. If you use OSMnx, you can help test it by installing the latest pre-release. Create a virtual environment then run:
pip install --pre osmnx
For more, check out the migration guide and reference paper.