Quantitative analysis of the impact of urban sprawl on land resources: algorithm-based spatio-temporal modeling
Bibliographic record
Abstract
The global urbanization process is rapidly increasing, and a reasonable and scientific analysis of the relationship between urban land expansion and land resources plays an important role in the rational allocation and coordinated development of land resources.This paper constructs a spatio-temporal geographic weighted regression model coupled with geospatial and temporal coordinates, and incorporates temporal and spatial non-stationarity into the model.Then, using the method of hypothesis testing, the temporal non-stationarity and spatial non-stationarity of the spatio-temporal geographic weighted regression model are examined, and at the same time, the multiple covariance test and the variance expansion factor method are proposed to carry out further statistical inference of the model.As the degree of urban sprawl increases, the land resources weaken year by year from the center to the surrounding area.The global Moran's I for the three periods from 2003 to 2023 are 0.6289, 0.7159, and 0.7368, respectively, which show a trend of increasing year by year.It shows that land resources are strongly influenced by urban expansion, and the spatial distribution of land resources shows spatial aggregation.Several variables, such as building volume rate, population size, regional economic development, regional cultural level, infrastructure construction and urban fallow area, have significant effects on the spatial differentiation of land resources.The above differentiation characteristics provide insights into the rationalization of urban expansion and the scientific allocation of land resources.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".