Mapping local urban climate and ventilation corridors using clustering approach
Bibliographic record
Abstract
The urban microclimate model, urbanMicroclimateFoam developed by the authors, is used to simulate thermal comfort in an urban park and adjacent neighborhood in tropical Singapore during a hot humid period. The thermal comfort is analyzed using the Universal Thermal Climate Index (UTCI) which depends on air temperature, relative humidity, wind speed and mean radiant temperature.To better understand the spatial variability of urban microclimate, we apply clustering approach to classify different local climate zones using scaled values of comfort variables as input. In the case study, six clusters are identified, each representing areas with similar local climate characteristics. One key cluster corresponds to the zone shadowed by trees in the park, where UTCI is significantly lower due to tree coverage. However, unshaded zone in between the park trees is also represented in a distinct cluster, where UTCI are higher since they experience lower wind speed and higher relative humidity, due to the wind blocking and transpiration by trees. This effect, in contrast to the local shading by trees, is referred to as nonlocal heating effect by trees. Interestingly, the proposed approach identifies different clusters with different thermal conditions in between the building blocks, with wind speed emerging as the primary differentiating factor. This highlights the importance of wind on thermal comfort in hot-humid context.Using a clustering approach with six variables as input, we were able to detect different wind corridors with a wind speed higher than average. Some of these clusters indicate hot and dry air, or cool and wet air ventilation corridors, which are not easily distinguishable using conventional methods and also unfavorable for thermal comfort improvement. One cluster indicates cool and dry air ventilation, which favors thermal comfort improvement.In conclusion, clustering approach allows to map different urban microclimate patterns and analyze the underlying reasons for the observed thermal comfort.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".