Street network connectivity leads to denser urban form in Canadian cities
Bibliographic record
Abstract
The layout of streets forms a skeleton of cities that shapes the long-term development of urban form and land use. The enduring nature of street connectivity implies that any later modifications to other elements of urban infrastructure cannot escape the constraints imposed by the initial street connectivity. Hence, this study examines whether initial street network connectivity leads to subsequent densification. Using Canadian urban neighborhoods as an empirical context, we provide a causal estimation of the effect of the Street-Network Disconnectedness index (SNDi)—a measure of how disconnected a street network is—on densification. Our estimation shows that a 10 % increase in SNDi leads to a considerable 17 % decrease in population density change. The study also provides strong evidence of the spillover effect of surrounding SNDi on the subsequent evolution of density. These results underscore the necessary function of street network connectivity in densification, reinforcing its need in planning policies aimed at reducing transport emissions through densification. • Offers causal evidence of low street connectivity being ‘density-proof’. • Transitioning from a cul-de-sac to a grid results in a neighborhood that is 190 % denser. • Influence of the street network on the urban form transcends both time and space.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".