Visualize Six Years of Architectural Changes at the University of British Columbia Vancouver Campus: Impacts on Tree Growth, Green Connectivity, and Coyote Footprints
Bibliographic record
Abstract
Urban development, such as campus expansion, alters land cover and disrupts ecological processes, but the full extent of these impacts is often underexamined at fine spatial and temporal scales. This study investigates how building expansion on the University of British Columbia (UBC) Vancouver campus from 2015 to 2021 has influenced tree dynamics and coyote movement by leveraging high-resolution Light Detection and Ranging (LiDAR) data and spatial analysis. A campus-wide analysis revealed an increase in building density from 0.91 to 1.03 buildings per hectare, with total building coverage expanding from 16.62% to 18.39%. Eight campus neighborhoods were analyzed in detail, with Wesbrook Place experiencing the most substantial change, adding 22 new primarily residential buildings. Beyond direct tree removal, construction activities indirectly hindered vegetation recovery by damaging root systems and compacting soil. Neighborhoods with lower construction intensity, such as East Campus and Hawthorn Place, showed more stable canopy structure and tree growth. Wildlife patterns were also affected; fragmentation of greenspace and reduction in canopy cover disrupted ecological corridors, influencing coyote movement across the campus. These findings highlight the critical role of remote sensing in tracking and visualizing land-use change, offering valuable insights for sustainable campus development and contributing to urban ecological planning in rapidly expanding metropolitan areas.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".