A high-resolution global time series of street-network sprawl
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".