GPS Trajectory-Based Spatio-Temporal Variations of Traffic Accessibility under Public Health Emergency Consideration
Bibliographic record
Abstract
Medical services are essential to public concerns and living qualities. Facing new public health events, the spatio-temporal variation of healthcare accessibility can be different, which is ignored in the previous accessibility studies. In this paper, we study the spatio-temporal variation of healthcare accessibility and residents’ accessibility to the designated hospitals under public health emergencies such as COVID-19. Metropolitan Beijing is chosen as the study area. Then, we analyze the spatial disparity and the temporal variation and measure the matching degree between healthcare accessibility and population density. From the perspective of epidemic prevention, we evaluate the medical capacity of the designated hospitals. The autocorrelation method is used to analyze the spatial correlation of residents’ accessibility to designated hospitals in the study area. A conclusion can be drawn that 74.14% grids in the study area have proportionate population density and healthcare accessibility. We find that the 5th Medical Center has sufficient medical resources, and Puren hospital is less affected by time. Moreover, the result of residents’ accessibility to the designated hospitals presents a pattern of high-value aggregation in the arterial road neighborhood. At the same time, the peripheral areas show a trend of low-value aggregation. The research in healthcare accessibility can provide a reference for policymakers in medical service development and public emergency management.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".