Urban growth: Modelling street network growth in Manhattan (1642–2008) and Barcelona (1260–2008)
Bibliographic record
Abstract
In this paper, we argue for the case that cities are self-organised complex systems by presenting evidence on positive and reinforcing feedback mechanisms and robust global trends that characterise historical growth patterns. In two case studies; Manhattan and Barcelona, historical stages of urban growth were mapped and analysed. The analyses revealed regularities that may help define the local and global processes that characterise urban growth marked by alternating periods of expansion and pruning in street networks. The global trend marked by a lognormal distribution of segmental integration (closeness) in street networks was consistently restored following planning interventions. The overall street network growth trend appeared to fit an exponential or power law distribution, along with a linear change in fractal dimension. Underlying these global trends, we found evidence for local positive and reinforcing feedback mechanisms; explained by preferential attachment to well-connected street structures, and pruning of weakly integrated local street structures. The findings are likely to improve our understanding of urban growth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".