The impact of urban proximity, transport accessibility and policy on urban growth: A longitudinal analysis over five decades
Bibliographic record
Abstract
Transport accessibility is assumed to be a main driver of urbanisation. Like many other metropolitan regions, the Randstad, the population and economic core of the Netherlands has experienced significant urbanisation, transport network expansion and spatial policies aimed to channel urban growth. This paper investigates the long-term relationships between the development of railway and motorway networks, urbanisation, and spatial policies, by using a panel dataset consisting of grid cells measured at six time points from 1960 to 2010. Generalised Estimating Equations analysis was applied to model the built-up area. Predictors include proximity to and accessibility by transport infrastructure, vicinity of urban areas, and spatial policies. Results indicate that road and rail accessibility alike, stably influenced urbanisation, but less than proximity to urban areas. Spatial policies played a significant role in channelling new urbanisation, while preserving the centrally located green and mainly rural area. Remarkably, the legacy of earlier policies is still significant despite shifts in predominant Dutch spatial policies. The findings are expected to be relevant for comparable poly-nuclear areas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".