An Empirical Spatial Accessibility Analysis of Qingdao City Based on Multisource Data
Bibliographic record
Abstract
Accessibility could reflect people’s travel convenience, cities’ livability and sustainability, and reduce the negative impacts on the environment and safety. It is therefore a key concept in city sustainable development policies across the world. Based on the multi-source data, this paper tries to have an empirical analysis on the urban accessibility of Qingdao city from the angles of spatial linkage strength, job-hosting ratio and distributions of large-scale hospitals and schools. The multisource data includes navigation data, location based service (LBS) data, Point of Interest (POI) data and the census data. The inter-city linkage strength and internal-external linkage strength are used to evaluate the spatial linkages of the urban agglomeration. Results show that the spatial connectivity and public facilities have a very strong positive correlation with the inter-city accessibility. Meanwhile, providing transport infrastructure and services could greatly strengthen the accessibility between two areas. Job-housing ratio is used to analyze the distribution of residents and jobs. Results show that the job-housing ratio declines gradually from the central urban areas to the surrounding areas. Distributions of large-scale hospitals and schools are used to estimate the service capacity of public facilities. Results show that public service facilities are mainly concentrated in the developed areas. On the contrary, the other areas developed slowly due to lack of supporting service facilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".