Understanding Urban Land Growth through a Social-Spatial Perspective
Bibliographic record
Abstract
To understand the urbanization process, it is essential to detect urban spatial growth and to study relations with social development. In this study, we take Wuhan as a case to examine urban land growth patterns and how social factors relate to the urban land evolution between 1990, 2000, and 2010. We first classify land cover using Landsat images and examine the urban growth patterns during various stages based on landscape metrics regarding the area, density, and shape. Afterwards, principal component analysis and census data are used to extract key social factors. Thirdly, we apply geographically weighted regression (GWR) to depict the link between urban land metrics and social factors. The results indicate that the urban land coalescence and diffusion simultaneously exist, for which redevelopment, infilling, and edge expansion dominate the city center, and diffusion dominates the peripheral areas. The social factors have global regression relationships with urban land areas while local spatial non-stationarity presents in the relationships with the urban land patch shape irregularities. Industrial upgrading, educational levelling up, and population aging show significant with local heterogeneities in the relationships. The simulation of the relationship provides a social-spatial perspective to understand urban land growth. The authors conclude that sustainable urban management should consider the coexistence of different urban spatial growth models and underline social transitions when examining the urban growth process. This works for cities in rapidly urbanizing countries or regions.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".