Spatial-temporal Dynamics of Population Aggregation during the Spring Festival based on Baidu Heat Map in Central Area of Chengdu City, China
Bibliographic record
Abstract
The application of location-aware devices and location-based services enables big data to provide a convenient and efficient way to study the dynamics of urban population distribution. Based on the Baidu heat map data, the spatial-temporal population aggregation in the main urban areas of Chengdu City was explored in the context of the Spring Festival. The results suggested that population aggregation showed regular fluctuation within a day, consistent with the commuting activities. Also, population mobility showed difference before, during and after the festival; population density during the holiday was significantly lower than that on the other two working days, meanwhile the heat value on the working day before the festival was slightly higher than that after the festival. The conclusion showed that the Spring Festival affected the population distribution density. Chinese government’s emergent measures taken to suppress the nationwide spread of COVID_19 at early 2020 also had great influence on the low population aggregation during and after the Spring Festival, indicating the effectiveness of emergency control of human interactions. Better understanding of Spatial-temporal dynamics of population aggregation during the Spring Festival is of great value for optimizing the city expansion and structure planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".