Tag: spatial networks

Categories
Planning

Resilient by Design

I have a new article out now in Transportation Research Part A: Policy and Practice. Here’s a free open-access preprint if you lack institutional access.

We simulate over 2.4 billion trips across every urban area in the world to measure street network vulnerability to disasters, then measure the relationships between street network design and these vulnerability indicators.

First we modeled the street networks of more than 8,000 urban areas in 178 countries. Then, for each urban area, we simulated disasters of 3 different types (representing floods, earthquakes, and targeted attacks) and 10 different extents. Then we simulated over 2.4 billion trips on these networks to measure how certain trips become more circuitous or even impossible to complete as parts of the network fail after a disaster. Finally we built a model to predict how much a disaster would impact trips.

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Categories
Urban

Scaling Urban Indicators

I have a new article out now in the journal of Urban Policy and Research coauthored with a team comprising many of the folks from our recent series in The Lancet Global Health. The article is titled “Policy-Relevant Spatial Indicators of Urban Liveability and Sustainability: Scaling from Local to Global” and discusses measuring urban indicators and scaling the software to calculate them from a local case study to a worldwide effort.

From the abstract:

Urban liveability is a global priority for creating healthy, sustainable cities. Measurement of policy-relevant spatial indicators of the built and natural environment supports city planning at all levels of government. Analysis of their spatial distribution within cities, and impacts on individuals and communities, is crucial to ensure planning decisions are effective and equitable. This paper outlines challenges and lessons from a 5-year collaborative research program, scaling up a software workflow for calculating a composite indicator of urban liveability for residential address points across Melbourne, to Australia’s 21 largest cities, and further extension to 25 global cities in diverse contexts.

For more, check out the article itself. And you may also be interested in our recent The Lancet Global Health series of articles that developed similar themes in great depth.

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

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
Urban

Urban Street Network Orientation

My new article, Urban Spatial Order: Street Network Orientation, Configuration, and Entropy, has just been published in one of my favorite journals: Applied Network Science (download free PDF). This study explores the spatial signatures of urban evolution and central planning. It examines street network orientation, connectivity, granularity, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx for modeling and visualization:

City street network grid orientations, order, disorder, entropy, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib.

So, who’s got a grid and who doesn’t? Each of the cities above is represented by a polar histogram (aka rose diagram) depicting how its streets orient. Each bar’s direction represents the compass bearings of the streets (in that histogram bin) and its length represents the relative frequency of streets with those bearings. The cities above are in alphabetical order. Here they are again, re-sorted from most-ordered/gridded city (Chicago) to most-disordered (Charlotte):

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