Investigating Surface Urban Heat Island Patterns and Green Space Interventions in Waterloo, Ontario
Bibliographic record
Abstract
The impact of urbanization on local atmospheric conditions presents a growing challenge to sustainable development. The surface urban heat island (sUHI), driven by anthropogenic infrastructure such as asphalts, bricks, concrete pavements, and buildings increases the land surface temperatures in cities compared to surrounding rural areas. The infrastructural density of cities influences the sUHI and has implications for the heat exposure of residents and the cooling demand for buildings during the warm season. While most research focuses on larger metropolitan cities, mid-sized cities like Waterloo (Ontario, Canada) remain understudied despite their vulnerabilities and growth trajectories. Most of these mid-sized cities lack the infrastructure of larger urban centers, making them particularly vulnerable to the impacts of sUHI, such as heat exposure for residents and increased cooling demands during warmer seasons. This impact has become particularly prescient in Canada after the 2021 BC Heat Dome, exacerbating the increasing need to address changing urban-atmospheric interactions. The city of Waterloo is working to find ways to increase community greening while simultaneously addressing the sUHI of Waterloo. This research addresses this gap by investigating the spatial and temporal patterns of the surface urban heat island of Waterloo over the past two decades, identifying the intensity of the sUHI for summer and winter seasons from recent decades, where land surface changes have resulted in an intensifying sUHI over time. The sUHI is then integrated with remotely sensed vegetation data of the city to investigate the impact of urban green spaces on sUHI. These analyses will identify potential areas of high impact in community greening interventions by the city that may be most effective. The findings will be used in collaboration with the city to direct municipal resources to improve the sUHI and support community greening efforts among the most vulnerable in the city.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".