Modelling the microscale spatial distribution of urban air temperature in suburban sprawl
Bibliographic record
Abstract
Mobile monitoring for urban air temperature at a microscale in Mississauga, Ontario, Canada, a unique region dominated by suburban sprawl, was completed via cycling. We sampled seven pre-determined routes across Mississauga, resulting in 3144 min of air temperature measurements between July and August 2022. We developed land use regression models to determine if stopping for 5-min periods every 20 min was beneficial compared to continuous collection. The model generated from the data captured while moving demonstrated the best performance, explaining 80 % of the spatial variability of air temperature in Mississauga. Regression kriging addressed issues of spatial autocorrelation in linear models, improving predictive performance (CV R2 = 0.83, CV RMSE = 0.95 °C, CV MAE = 0.74 °C). We used the regression kriging model from the data captured while moving to predict average, maximum, and 95th percentile air temperature at a 20 m-by-20 m spatial resolution across Mississauga. We also conducted one-way analysis of variance (ANOVA) tests between air temperature and marginalization and found that areas with higher levels of marginalization experience different air temperatures compared to areas with lower levels of marginalization. Our study supports mobile monitoring to access urban air temperature and improve predictive performance by integrating regression kriging. • Mississauga, Ontario is a unique region dominated by suburban sprawl. • Mobile monitoring using a bicycle platform can model urban air temperature. • Stopping periods during mobile monitoring does not provide any benefit. • We created a high spatial resolution urban air temperature surface across the city. • Integrating regression kriging improves predictive performance.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".