Tag: urban data science

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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.

Tags code, data science, geographic data science, geospatial, gis, github, maps, modeling, networks, openstreetmap, osmnx, pip, python, science, software, spatial analysis, street-network, urban analytics, urban data science

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Worldwide Street Network Models and Indicators

Post author By gboeing
Post date 2021-03-09
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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.

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New Article on Computational Notebooks

Post author By gboeing
Post date 2020-05-08
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I have a new article out in Region: Journal of the European Regional Science Association, “Urban Street Network Analysis in a Computational Notebook.” It reflects on the use of Jupyter notebooks in applied data science research, pedagogy, and practice, and it uses the OSMnx examples repository as an example.

From the abstract:

Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future.

For more, check out the article.

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Defining Urban Data Science

Post author By gboeing
Post date 2019-10-30
1 Comment on Defining Urban Data Science

I’m a co-author on a new article out in Environment and Planning B: Urban Analytics and City Science titled “A Roundtable Discussion: Defining Urban Data Science” (download free PDF). It arises from a panel discussion I participated in at the 2019 AAG Annual Meeting in DC. Vanessa Frias-Martinez, Song Gao, Ate Poorthuis, and Wenfei Xu joined me on the panel, which was organized and moderated by Wei Kang, Taylor Oshan, and Levi Wolf. From the abstract: