Spatiotemporal Relationship Between Human Activities and Urban Heat in Chinese Megacities Based on Multisource Remote Sensing
Bibliographic record
Abstract
Human activities have significantly reshaped urban thermal environments and intensified heat-related risks in rapidly urbanizing areas. Owing to its broad coverage and long-term monitoring capabilities, satellite remote sensing has become essential for tracking land surface temperature (LST) dynamics. In this study, MODIS LST data and WorldPop population density (PD) data retrieved from thermal infrared and multi-source remote sensing were used. Employing correlation analysis, standard deviational ellipse, and a coordination model, we comprehensively assessed the spatiotemporal relationship between human activities and urban heat in six Chinese megacities from 2005 to 2020. The main results revealed that: (1) although both PD and LST exhibited consistent upward trends in all cities during 2005–2020, the correlation between the two decreased while remaining significantly positive, suggesting that governance measures in megacities have helped improve urban thermal environments; (2) the correlation displayed periodic fluctuations. Dominant coordination types alternated between “heating advance” and “coordinated enhancement,” with “heating hysteresis” tending to occur alongside “coordinated enhancement,” reflecting dynamic policy adaptations to different stages of urban development; (3) significant intercity variation in the PD–LST relationship was attributed to differences in geographical conditions, development models, and industrial structures. For example, Guangzhou exhibited high PD and LST but the lowest PD–LST correlation (0.31) and the fastest decline (−1.12%/a), whereas Beijing exhibited lower PD and LST but the highest correlation (0.50) and lowest change rate (−0.60%/a). Based on these findings, we propose targeted heat environment regulation strategies tailored to each city's characteristics, providing practical guidance for enhancing heat environment governance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".