A Novel Approach to Measure the Pattern of Urban Agglomeration Based on the Road Network
Bibliographic record
Abstract
The configuration of urban areas depicts the pattern of urban agglomeration that drives a country's economy.Since the era of the early-industrial revolution, many scholars have been continuingly attempted to recognize the pattern of urban agglomeration in cities and regions.Nevertheless, such efforts had been in completed in capturing the complex nature of the agglomerations in modern cities.In such context, this study proposes a novel modeling approach to capture the pattern of urban agglomeration of a given region.In the proposed model, which is named as the intersection based clustered network model (iCN Model), the centrality of the road network is considered as the primary indicator in capturing the urban agglomeration pattern.The model was developed on the basis of the percolation theory and fractal geometry.The model distinguishes the agglomerated urban clusters and measures the self-similarity of the clusters of urban agglomeration.The results revealed that the urban agglomeration pattern derived from the iCN model corresponds with the satellite imagery derived the urban agglomeration pattern with an acceptable level of accuracy (> 71% of KAPPA) and it is proposed to be applied in spatial planning and transport planning practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".