Urban science beyond samples: Up-to-date street network models and indicators for every urban area in the world

Introduction

Scholars and practitioners use spatial graphs to model street networks to understand or predict urban phenomena including traffic dynamics, accessibility to daily living needs, and the resilience and sustainability of the urban form (Barthelemy, 2022). These networks are defined by both their topology—for example, connections and configuration—and geometry—for example, positions, lengths, areas, and angles (Barthelemy, 2011). Accordingly, various topological and geometric indicators exist throughout the literature to measure important street network characteristics. For instance, node degrees reveal streets’ connectedness (Barrington-Leigh and Millard-Ball, 2020), weighted betweenness centralities identify relatively important parts of the network (Barthelemy and Boeing, 2025), and circuity indicates its efficiency or lack thereof (Giacomin and Levinson, 2015). Such indicators can then inform downstream urban analytics to target planning interventions or benchmark and monitor cities’ progress toward stated sustainability goals (Higgs et al., 2025).

Up-to-date, global, consistent urban street network models and indicators are needed now more than ever before as planners face intertwined sustainability and public health crises in cities around the world (Giles-Corti et al., 2022). Meanwhile, urban science seeks to expand beyond the limits of traditional sampling to build universal theory and better understand under-studied regions, such as the Global South (Lobo et al., 2020). Yet the limitations of traditional data sources and methods present headwinds to these efforts. Data on urban streets are often digitized inconsistently from place to place, thwarting apples-to-apples global comparisons and making analyses particularly difficult in under-resourced regions (Liu et al., 2022). Popular data sources such as OpenStreetMap offer reasonably high-quality data around the world but do not package it in graph-theoretic form nor provide indicators (Boeing, 2025a). Tools like OSMnx help, but still require coding knowledge to conduct the analysis and potentially extensive computational resources for someone trying to conduct global urban science.

This article presents a resource to fill this gap: street network models and indicators worldwide—plus reproducible code to regenerate them—for scholars and practitioners to easily reuse without reinventing the wheel. Using data from OpenStreetMap and boundaries from the 2025 Global Human Settlement Layer (GHSL), this study models and analyzes the street networks of every urban area in the world, ingesting 180 million OpenStreetMap nodes and 360 million OpenStreetMap edges across 10,351 urban areas in 189 countries. The rest of this article describes this open data repository of street network models and indicators, as well as the open-source software repository containing the code to generate them.

Reproducible methods

The following computational workflow, written in the Python programming language, generates the models and calculates the indicators.

Indicator calculation

For each graph, we calculate the street network indicators described in Table 1. These include geometric and topological measures common in transport planning, urban design, and network science (Barthelemy, 2011, 2022; Boeing, 2022). We report node counts, intersection counts (i.e., non-dead-end nodes), and both geometrically and topologically consolidated intersection counts, as explained in Boeing (2025b). We also calculate betweenness centrality for every node in every graph. A node’s betweenness centrality measures the share of all possible shortest paths in a graph that use that node. High centrality values indicate “important” nodes relied on by many shortest paths (Boeing et al., 2024). The maximum betweenness centrality represents the highest relative value in a graph (and thus identifies the most important node), and their Gini coefficient measures the concentration of “importance” in a network, indicating the presence and severity of chokepoints (Barthelemy and Boeing, 2025).

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Table 1.

The indicators dataset contents. Variables carried over from GHSL are noted.

Variable	Type	Description
area_km2	Integer	Area within urban center boundary polygon, km2 (GHSL)
bc_gini	Decimal	Gini coefficient of normalized distance-weighted node betweenness centralities
bc_max	Decimal	Max normalized distance-weighted node betweenness centrality
built_up_area_m2	Integer	Built-up surface area, square meters (GHSL)
cc_avg_dir	Decimal	Average clustering coefficient (unweighted/directed)
cc_avg_undir	Decimal	Average clustering coefficient (unweighted/undirected)
cc_wt_avg_dir	Decimal	Average clustering coefficient (weighted/directed)
cc_wt_avg_undir	Decimal	Average clustering coefficient (weighted/undirected)
circuity	Decimal	Ratio of street lengths to straightline distances
core_city	String	Urban center core city name
country	String	Primary country name
country_iso	String	Primary country ISO 3166—1 alpha—3 code
elev_iqr	Decimal	Interquartile range of node elevations, meters
elev_mean	Decimal	Mean node elevation, meters
elev_median	Decimal	Median node elevation, meters
elev_range	Decimal	Range of node elevations, meters
elev_std	Decimal	Standard deviation of node elevations, meters
grade_mean	Decimal	Mean absolute street grade (incline)
grade_median	Decimal	Median absolute street grade (incline)
intersect_count	Integer	Count of (undirected) edge intersections
intersect_count_clean	Integer	Count of street intersections (merged within 10 m geometrically)
intersect_count_clean_topo	Integer	Count of street intersections (merged within 10 m topologically)
k_avg	Decimal	Average node degree (undirected)
length_mean	Decimal	Mean street segment length (undirected edges), meters
length_median	Decimal	Median street segment length (undirected edges), meters
length_total	Decimal	Total street length (undirected edges), meters
node_count	Integer	Count of nodes
orientation_entropy	Decimal	Entropy of street network bearings
pagerank_max	Decimal	The maximum PageRank value of any node
prop_4way	Decimal	Proportion of nodes that represent 4-way street intersections
prop_3way	Decimal	Proportion of nodes that represent 3-way street intersections
prop_deadend	Decimal	Proportion of nodes that represent dead-ends
resident_pop	Integer	Total resident population (GHSL)
self_loop_proportion	Decimal	Proportion of edges that are self-loops
Straightness	Decimal	Inverse of circuity
street_segment_count	Integer	Count of streets (undirected edges)
uc_id	Integer	Urban center unique ID (GHSL)
uc_names	String	List of city names within this urban center (GHSL)
world_region	String	UN SDG geographic region

Code and data products

Practical applications

In terms of downstream applications, these models and indicators can be used to estimate relationships between network topology and travel patterns or land use (Wang et al., 2018, 2020), identify relatively important parts of a network via empirical distributions of within-city betweenness centrality (Barthelemy and Boeing, 2025), measure sprawl (Gervasoni et al., 2017), simulate disasters’ effects on street network resilience around the world (Boeing and Ha, 2024), improve logistics’ last-mile trip predictions (Merchán et al., 2020), and estimate relationships between greenhouse gas emissions and street network design (Boeing et al., 2024). To date, this repository’s models and indicator data have been downloaded over 70,000 times by users.

Dyer et al. (2024) argue that urban scholars and practitioners need models and indicators that keep up with the pace of transformational change in an era of rapid urbanization. This project seeks to address this by building on prior work initially conducted in 2019–2020 that generated a preliminary version of the data repository (Boeing, 2022). That initial version was based on the 2015 version of the GHSL UCD and 2020 OpenStreetMap data. This new version uses the 2025 GHSL UCD and 2025 OpenStreetMap data to make several new contributions.

First, it includes over 1400 more urban areas and 11 more countries than the earlier version. This entails significantly more worldwide coverage in an era of rapid urban expansion. That is, these new models allow us to study more—and more-recently urbanized—cities than before.

Second, these new models incorporate 10 years of recent urbanization into their updated urban area boundaries and 5 years of new community additions to OpenStreetMap. As such, this workflow’s modeling includes approximately 20 million more street network nodes and 40 million more edges than the earlier version. The new urban boundaries allow users to link these street network models and indicators to hundreds of new, up-to-date GHSL attributes on urban climate, land use, economic conditions, etc. For instance, this allows us to simulate urban travel and access in the context of extreme heat or cold, generate integrated models of urban networks and land uses, or estimate relationships between economic activity and underlying transport infrastructure.

Third, it adds new attributes and indicators to the repository (most consequentially the betweenness centrality of every node in every urban area’s street network, which is time- and resource-intensive to calculate, yet unlocks useful analyses of network structure and resilience). For example, betweenness centrality can provide us with indicators of chokepoints when simulating network disruptions and resilience (Boeing and Ha, 2024) and indicators of the relative importance of network elements (Barthelemy and Boeing, 2025). Fourth, it uses finer-grained SRTM data (30 m instead of the previous 90 m resolution) for more precise elevation attribute values. This is especially useful when modeling active travel behavior, which is sensitive to arduous elevation changes (Barthelemy et al., 2024).

Fifth, from a code product perspective, the workflow’s code base has been wholly refactored and rewritten from the ground-up to significantly reduce its cyclomatic complexity, memory use, and runtime. This makes the workflow more maintainable, sustainable, and easier to re-run in the future to periodically update the data repository whenever new GHSL data are released. This also makes it easier for downstream users to re-run the code to model any network types they wish (including pedestrian networks or cycling networks).

Finally, these models and indicators themselves unlock other researchers’ work, such as through the example applications discussed above. This project provides a global dataset to conduct both worldwide urban street network science beyond samples as well as local analyses particularly in less-resourced regions where such models and indicators are most needed, yet most scarce (Giles-Corti et al., 2022).