The In‐Depth Analysis on the Influencing Factors of Urban Vitality in China’s HSR Station Area
Bibliographic record
Abstract
High‐speed railway (HSR) station area is the key focus of urban construction, and the development of HSR station area has significant regional differences. This study adopts the association rule mining model and ordinary least squares (OLS) regression method to explore the relationships among the development level of the station‐setting cities, the development level of the HSR station area, and the urban vitality of the HSR station area. It further investigates how each factor influences the urban vitality in the HSR station area. Findings reveal a prominent multicenter clustering pattern in the urban vitality of the HSR station area. The association rule mining analysis reveals a clear and complex link between the urban vitality in the station area and the influencing factors of the development level of the station‐setting cities and the development level of the HSR station area. OLS regression analysis results indicate that the proportion of the tertiary industry in GDP and the intensity of intracity travel are significantly positively correlated with urban vitality in the HSR station area, directly contributing to the growth of the urban vitality. The study’s innovation mainly lies in utilizing multisource data to analyze the spatial pattern characteristics and influencing mechanism of the urban comprehensive vitality in the HSR station area from multiple perspectives, as well as applying association rule mining to explore the correlations between urban vitality in the HSR station area and its determinants. From the perspective of urban vitality, gaining deeper insight into the overall development status of the HSR station area and identifying the factors that affect the urban vitality of the HSR station area can support efforts to enhance the vitality of the broader urban environment.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".