Clustering-based characterization of urban microclimate zones using CFD derived time-series data
Bibliographic record
Abstract
Identifying microclimate zones in cities is needed for planning adequate heat mitigation strategies, ensuring more comfortable and resilient urban environments in the face of climate change. The spatial and temporal variability of thermal comfort poses challenges for analyzing the urban microclimate. This study introduces a clustering-based methodology to analyze the spatial and temporal variability of urban microclimate and assess thermal comfort at neighborhood scale. The approach relies on high-resolution Computational Fluid Dynamics (CFD) simulations used in a time-series clustering to identify patterns driving heat stress. Microclimate data are obtained using the urbanMicroclimateFoam (UMF) model based on OpenFOAM. The Universal Thermal Climate Index (UTCI) is used to assess thermal comfort, while the clustering algorithm integrates normalized time series of air temperature, relative humidity, wind speed, and mean radiant temperature, i.e. the variables influencing UTCI. The methodology is evaluated using two distinct climatic contexts: a hot-humid climate (Singapore) and a continental climate (Montreal). A six-cluster classification is identified as a balance between accuracy and interpretability. The results reveal distinct microclimatic zones. Tree-shaded areas form clusters with significantly improved thermal comfort due to canopy shading. In contrast, unshaded zones form separate clusters characterized by higher UTCI values. These are driven by reduced wind speed, increased relative humidity and high mean radiant temperature. Ventilation corridors are also identified using the clustering approach. Their effect on comfort depends on whether they transport cool, dry air or hot, humid air.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".