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Data

OSMnx Reference Paper Published

The official OSMnx reference paper, titled “Modeling and Analyzing Urban Networks and Amenities With OSMnx,” has just been published open-access by Geographical Analysis. It describes what OSMnx does and why it does it that way. I also discuss some lessons learned over the past decade in geospatial software development.

From the abstract:

OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx’s modern capabilities, usage, and design—in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science’s implications for urban modeling and analysis.

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.

For more, check out the article at Geographical Analysis.

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Data

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.

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Data

Worldwide Street Network Models and Indicators

My article, “Street Network Models and Indicators for Every Urban Area in the World” has been published by Geographical Analysis. This project was a massive undertaking and I’m excited to share it. As you might guess from the title, I modeled and analyzed the street network of each urban area in the world then deposited all the source code and models and indicators in open repositories for public reuse. The article also includes a high-level analysis of urban street network form across the world.

Cities worldwide exhibit a variety of street patterns and configurations that shape human mobility, equity, health, and livelihoods. Using boundaries derived from the Global Human Settlement Layer, I modeled and analyzed the street networks of every urban area in the world using OSMnx and OpenStreetMap raw data. In total, I modeled over 160 million street network nodes and over 320 million edges across 8,914 urban areas in 178 countries. I attached node elevations and street grades to every node/edge in the final models. All the final models were topologically simplified such that nodes represent intersections and dead-ends, and edges represent the street segments linking them.

Street network topology simplification with OSMnx and OpenStreetMap

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Data

GIS and Computational Notebooks

I have a new chapter “GIS and Computational Notebooks,” co-authored with Dani Arribas-Bel, out now in The Geographic Information Science & Technology Body of Knowledge. Want to make your spatial analyses more reproducible, portable, and well-documented? Our chapter is a short, gentle intro to using code and notebooks for modern GIS work.

Science and analytics both struggle with reproducibility, documentation, and portability. But GIS in both research and practice particularly suffers from these problems due to some of its unique characteristics. Our chapter discusses this challenge and its urgency for building better and more actionable knowledge from geospatial data. Then we introduce an emerging solution, the computational notebook, using Jupyter as our central example to illustrate what it does and how it works.

Jupyter notebook JupyterLab user interface