My article, “Urban Science Beyond Samples: Up-to-Date Street Network Models and Indicators for Every Urban Area in the World,” has just been published in Environment and Planning B: Urban Analytics and City Science. You can download it open-access from EP-B.

My own research goal over the past few years has been to try to conduct urban network science “beyond samples” – that is, what can we measure for every urban area in the world? Alternatively, what can we not measure consistently and accurately? And what urban theory can we build from comprehensive worldwide studies?

This article presents up-to-date street network models and indicators for every urban area in the world: 10,351 urban areas across 189 countries, built from 2025 Global Human Settlement Layer boundaries and 2025 OpenStreetMap data. The workflow ingests 180 million OSM nodes and 360 million OSM edges, then produces reusable network models, GIS files, node/edge lists, metadata, and indicators such as intersection densities, street lengths, circuity, orientation, elevation, grade, and betweenness centrality.

The article expands my past work modeling and analyzing the street networks of every urban area in the world. It is an update that adds 1,400 more urban areas, 11 more countries, and approximately 20 million more street network nodes and 40 million more edges. It also now includes betweenness centrality calculations for every node in every urban area, enabling analyses of network importance, concentration, and chokepoints without requiring users to perform these (very) computationally expensive calculations themselves. These new models allow us to study more (and more-recently urbanized) cities than before. And they allow us to link these street network models and indicators to hundreds of new, up-to-date Global Human Settlement Layer attributes on urban climate, land use, economic conditions, etc.

The purpose of this update is practical (for me, and hopefully for others): if we want to compare cities, model accessibility, study resilience, or evaluate street network design, we need models that are current, consistent, and readily available across places. OpenStreetMap gives us raw data worldwide, but turning it into scientifically useful graph models and indicators still takes coding, computation, and lots of methodological decisions that pose barriers for many studies.

I hope this reduces duplicated effort in our discipline: rather than requiring every researcher to independently download, clean, model, and analyze street network data, the repository provides reusable models, indicators, and code in a fully reproducible workflow. These data can support studies of accessibility, active transport, network resilience, street connectivity, sprawl, emissions, and local planning decisions. They should be especially useful where ready-to-use urban network data are harder to come by, particularly in under-studied or less-resourced regions.

The data repository is available on the Harvard Dataverse and the source code is on GitHub.

From the abstract:

Urban planners need up-to-date, global, and consistent street network models and indicators to measure resilience and performance, model accessibility, and target local quality-of-life interventions. This article presents up-to-date street network models and indicators for every urban area in the world. It uses 2025 urban area boundaries from the Global Human Settlement Layer, allowing users to join these data to hundreds of other urban attributes. Its workflow ingests 180 million OpenStreetMap nodes and 360 million OpenStreetMap edges across 10,351 urban areas in 189 countries. The code, models, and indicators are publicly available for reuse. These resources unlock worldwide urban street network science beyond samples as well as local analyses in under-resourced regions where models and indicators are otherwise less-accessible.

For more, check out the free open-access article at EP-B.