Tag: urban analytics

Categories
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.

Categories
Data

Urban Analytics: History, Trajectory and Critique

I have a new chapter titled “Urban Analytics: History, Trajectory and Critique,” co-authored with Mike Batty, Shan Jiang, and Lisa Schweitzer, now published in the Handbook of Spatial Analysis in the Social Sciences, edited by Serge Rey and Rachel Franklin.

From our abstract:

Urban analytics combines spatial analysis, statistics, computer science, and urban planning to understand and shape city futures. While it promises better policymaking insights, concerns exist around its epistemological scope and impacts on privacy, ethics, and social control. This chapter reflects on the history and trajectory of urban analytics as a scholarly and professional discipline. In particular, it considers the direction in which this field is going and whether it improves our collective and individual welfare. It first introduces early theories, models, and deductive methods from which the field originated before shifting toward induction. It then explores urban network analytics that enrich traditional representations of spatial interaction and structure. Next it discusses urban applications of spatiotemporal big data and machine learning. Finally, it argues that privacy and ethical concerns are too often ignored as ubiquitous monitoring and analytics can empower social repression. It concludes with a call for a more critical urban analytics that recognizes its epistemological limits, emphasizes human dignity, and learns from and supports marginalized communities.

For more, check out the chapter.

Categories
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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Categories
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

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Categories
Planning

Off the Grid… and Back Again?

My article “Off the Grid… and Back Again? The Recent Evolution of American Street Network Planning and Design” has been published by the Journal of the American Planning Association and won the 2020 Stough-Johansson Springer Award for best paper. It identifies recent nationwide trends in American street network design, measuring how urban planners abandoned the grid and embraced sprawl over the 20th century, but since 2000 these trends have rebounded, shifting back toward historical design patterns. In this post I discuss these findings and visualizations across the US today as well as over time, then discuss my analysis methods.

Map of where street grids exist today across the US, made with OSMnx and Python

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Categories
Data

New Article on Computational Notebooks

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.

Categories
Academia

AAG Transactions in GIS Plenary

Manhattan, New York City, New York street network, bearing, orientation from OpenStreetMap mapped with OSMnx and PythonI am giving the Transactions in GIS plenary address at the AAG conference this afternoon. I’ll be reflecting on urban science, spatial networks, and tool-building in academia, focusing on OSMnx. A paper will be forthcoming soon, but in the meantime, for any interested plenary session attendees or other folks, here are a few links to more info and related resources:

Getting started

What is OSMnx? What does it do? Here’s a succinct overview.

The easiest way to get started with street network modeling and analysis in OSMnx is with this docker image and these example/tutorial Jupyter notebooks. The OSMnx software documentation is available here and this journal article introduces it more formally.

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