The co- integration analysis of relationship between urban infrastructure and growth of urban space——A case of Lanzhou
Bibliographic record
Abstract
Urban infrastructure and urban space growth influenced each other. Quantitative analysis of the relationship between them is of great significance to promote urban social and economic development. Based on the related date of Lanzhou city infrastructure and construction area from 1988 to 2012,the relationship of between the Lanzhou city infrastructure and urban spatial growth was analyzed by adopting the methods of the econometric analysis of co- integration test,error correction model and Granger causality test method. The results showed that: 1) The Lanzhou urban infrastructure played a positive role in relationship between the urban space growth and the intensive use of urban space. 2) When the short- term fluctuation deviated from the long- run equilibrium,the system would adjust the non- equilibrium state with intensity of 0. 342206 back to the equilibrium state.And in the short term the road infrastructure was an important variable to guide the growth of urban space and played a leading role. 3) There was no Granger causality between Lanzhou city social infrastructure and urban spatial growth. In the physical infrastructure,the road infrastructure and urban spatial growth showed an individual Granger causality relationship,the water supply infrastructure and urban spatial growth influenced each other.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".