Spatial-Temporal Characteristics in Urban Morphology of Majior Cities in China during 1990-2010
Bibliographic record
Abstract
In this paper, a total of 62 major cities in Chinese mainland are selected as the research object. By constructing SVBI index, central built-up areas are extracted from multi-temporal Landsat TM/ETM+ remote sensing satellite imagery with the help of Arc GIS and Erdas software. Spatial-temporal characteristics of urban spatial morphologic evolution from1990 to 2010 are analyzed by using the index of expanding area, expansion rate, compactness indices, Boyce-Clark shape indices, fractal dimension, trend analysis and so on. The results show that expansion speed of Chinese major cities is proportional to those of urban level. Expansion speed of the eastern cities is higher than that of the western and central cities during the period from 1990 to 2010. The shape of the 62 cities tended to be stable, mostly in between the square and the rectangle. Overall, urban spatial compactness is increased, and the fractal dimension is declined. Major way of urban morphology evolution of Chinese major cities is the intension-type development instead of extensive transit during the period of 1990-2010. Out of 62 cities, 39 cities show an unreasonable speed at urban land expand. H-shape or starshape is the best urban morphology in eliminating air pollution. The factors influencing the urban morphology evolution of Chinese major cities include urbanization, traffic location, new-style spatial elements and government regulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".