A high-resolution global time series of street-network sprawl.

Abstract

Systems of street networks form a backbone for many aspects of human life and, once laid down, urban streets represent a nearly immutable influence on future urban form and concomitant travel, energy, and social outcomes. Moreover, as humanity is currently passing through its peak urbanization rate, decisions about how to design such networks at the local scale are being made faster than ever before. In this work, we quantify local street connectivity and provide a global, high-resolution time series of our Street Network Disconnectedness Index (SNDi) as an open data set. We derive a stylized version of the actual geographic road network from the 2023 vintage of OpenStreetMap by simplifying complex intersections, divided roads, and offset intersections. Using this functional representation of the network corrects systematic biases in derived properties of the network. We couple this simplified network with a newly available time series of urbanization in order to compute SNDi and provide a dynamic analysis to the year 2019 and a cross-sectional analysis for 2023. We release our data as the raw network of edges and nodes and as aggregates to a 1 km grid, to countries, and to five subnational administrative levels. We also provide interactive visualizations at sprawlmap.org. Overall, our findings present a picture of rapidly worsening street-network connectivity in many regions of the world.