Mapping Equity Access to Green Spaces Across The University of British Columbia Vancouver Campus
Bibliographic record
Abstract
Green spaces play an important role in providing ecosystem services such as air purification, temperature regulation, and recreational opportunities; however, their unequal distribution often highlights environmental inequities. This study examines the spatial distribution of green space coverage across the University of British Columbia (UBC) Vancouver campus to identify areas with limited access, termed Equity Initiative Zones (EIZs). A Geographic Information System (GIS)-based methodology was employed, integrating Light Detection and Ranging (LiDAR) data, green space data, a pedestrian walkway dataset, and a hexagonal grid framework to analyze pedestrian accessibility to green spaces using service area analysis, and to identify green space diversity and spatial equity disparities through statistical analysis. Results revealed that while the UBC campus has an average of 37.8% green space and 46.7% tree canopy coverage, approximately 6% of the campus falls within EIZs, primarily in northeast areas near academic buildings and parking lots—with mean green space coverage of approximately 1.8%. A moderate negative correlation between distance to green spaces and canopy coverage (r = −0.25, p = 1.96 × 10⁻⁷) suggested reduced canopy coverage in areas further away from green spaces. Additionally, green space diversity analysis showed that high-traffic areas like the academic zones are dominated by homogenized lawns, whereas peripheral areas such as those near the UBC Farm exhibit greater diversity. These findings highlight environmental inequities where EIZs may experience diminished access to ecosystem benefits, emphasizing the need for targeted interventions such as tree planting or the creation of pocket parks to promote a sustainable and inclusive campus environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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) | 0.002 | 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".