Context-Dependent Relationships between Street-level Heat Island Effect, the Sky View Factor, and Landscape Pattern: Examples from the Coastal City of Vancouver, Canada
Bibliographic record
Abstract
This research focuses on street-level Sky View Factor (SVF), exploring which areas in coastal city, Vancouver, might see street-level SVF play the most significant role in influencing UHI. Additionally, it explores other indicators of landscape patterns outside of streets that impact UHI intensity. This study establishes multiple-ring buffers and two grid scales, namely 150m×150m and 900m×900m fishnet to analyze spatial heterogeneity of the research area. Using OLS, geographically weighted regression (GWR), and multiscale GWR (MGWR), it analyzes the influence of various indicators of urban morphology on UHI intensity. Our study found a positive correlation between SVF and UHI intensity, consistent with previous research. However, in areas closer to the coast, SVF is insignificant, while distance to the sea has a greater impact on UHI intensity. Additionally, green space provides stronger heat island mitigation at small scales, but landscape patterns of urban grassland are less effective than dense forest areas both at city-wide and more local scales. Finally, based on the research conclusions, some location-specific planning and design recommendations were proposed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".