Modeling the Shading Effect of Vancouver’s Urban Tree Canopy in Relation to Neighborhood Variations
Bibliographic record
Abstract
Abstract Background: Cities consume a disproportionate amount of energy for internal temperature regulation. Being able to reduce cities’ cooling load on hot summer days can decrease energy consumption while improving occupants’ thermal comfort. The urban canopy is an effective shading agent, adding cooling benefits to existing buildings and streets while providing other ecological and physiological values. Yet the building and street shading dynamic is a highly complex system that involves micro-level building components and macro-level variables. Introducing urban canopy to such a complex system creates another challenge, as urban canopy variables can also interact with buildings at both micro- and macro-levels. In order to accurately represent the urban canopy shading effect, it is necessary to account for the interactions among buildings, streets, and urban canopies. Methods: This study simulates the shading effect of urban canopy measured by aerial laser scanning (ALS) in the City of Vancouver, Canada, through the integration of a Radiance daylight simulation engine and geographic information system (GIS) data. All trees detected by ALS were included in the analysis. Results: The results indicate that street surfaces receive more solar irradiance reduction than building roofs and façades (i.e., exterior walls). Neighborhoods with less density and lower buildings were shaded noticeably better than areas with higher density and taller buildings. Among Vancouver’s 22 neighborhoods, 2 neighborhoods, Kitsilano and the West End, demonstrated a promising sign where both building density/height and urban canopies are maintained. There was evidence of high canopy shading and high-density urban morphologies. Conclusion: Overall, this work provided an authentic canopy assessment from single building to city scale, creating opportunities to investigate intracity urban canopy variations, equality, and the balance between urban greening and urban densification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".