Shade Mapping for UBC Vancouver Neighbourhood Climate Adaptation and Community Wellbeing
Bibliographic record
Abstract
Shade is important to urban environments as they provide comfort, reduce heat-related stress, and enhance overall wellbeing. This report presents a comprehensive study on shade mapping for Neighbourhood climate adaptation and community wellbeing within the University of British Columbia Vancouver (UBCV) campus. The primary objectives are to develop methodologies for shade mapping, identify areas with insufficient shade coverage, and provide actionable recommendations for improving shade distribution. Using high-resolution LiDAR data and sun position data, a Digital Surface Model (DSM) was created to represent campus elevation, and hillshade analysis was employed to simulate shade coverage at 15-minute intervals. Findings reveal that pedestrian areas have the highest mean shade coverage (0.69507), while concrete areas such as buildings and structures have the lowest (0.434512). Significant variations exist across Neighbourhoods, with East Campus and Hampton Place showing high, consistent shade, while Stadium and UBlvd require improvement. Bus stations also exhibit variability in shade, with UBC Exchange Bay 8 having the lowest coverage (0.160035). Recommendations include enhancing shade consistency in pedestrian areas, providing shelters in open concrete spaces, and increasing shade in Neighbourhoods like Wesbrook Place and UBlvd. Limitations of the study include the hillshade method's inability to account for shaded areas underneath trees or structures and the need for ground-truth validation. Future work should explore 3D multipatch analysis, incorporate detailed tree inventory data, and integrate shade analysis into broader urban planning efforts. This methodology-driven research aims to inspire further enhancements to the campus environment, ensuring optimized shade coverage and contributing to a more comfortable and sustainable urban landscape.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".