The Spatial Effect of Building New Housing in Zhengzhou City——Based on the Spatial Econometrics Model
Bibliographic record
Abstract
This article, which is based on micro data of building new housing in Zhengzhou city in nearly years, analyzes the spatial correlation model and spatial clustering and finds that there is a clear spatial dependent in the housing prices and spatial correlation patterns have space heterogeneity; the paper judges that the spatial lagged effect shows more apparent by diagnostics for spatial effect, while judges that the spatial Durbin model is the optimum fitting in the 4 models by goodness of fit and maximum likelihood; at the same time, the research analyses shows that spatial lagged effect is very significant for the housing prices in the spatial dimension and is the most important factor by analyzing all the influencing factors, while the paper concluded that the spatial spillover effect and the transportation accessibility should not be ignored. At last, the study suggests that the government department must make a liberal allowance for spatial interaction mechanism has a spatial heterogeneity effect on new building prices when choosing the real estate policy and making controls on the prices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".