HOW AIR QUALITY AFFECTS HUMAN MOBILITY PATTERNS: AN EXPLORATORY ANALYSIS
Bibliographic record
Abstract
Abstract. Air quality acts as an important factor that human may consider as they make decisions on when and where they would go. In order to access how much the air quality affects human mobility patterns, the air quality was measured using air quality index (AQI) and human mobility patterns were measured by travel volume and travel distance of shared bikes. Their correlation that presents on weekdays and weekends as well as in different administrative districts were investigated using Spearman correlation analysis method. A case study was conducted in Beijing, China using bike sharing data and air quality data ranging from May 10 to 16, 2017. The results show that travel distance is more sensitive to air quality on weekdays such as Changping District (−0.20), Haidian District (−0.13), Shunyi District (−0.12). The travel volume on weekdays is less sensitive to air quality due to commuting. The travel volume has a negative relationship with AQI on weekends. Fengtai District, Huairou District, Pinggu District are more susceptible to severe air quality, leading to a reduction in bike traveling distance. This work sheds light on understanding human-environment coupling mechanism and promoting urban sustainable development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".