Exploring the spatial distribution of urban greenery in small and medium-sized Canadian cities through a spatial equality lens
Bibliographic record
Abstract
Urban greenery (UG) supports healthier and more sustainable cities. Therefore, it is important to study how UG is distributed among different population groups. Although much progress has been made, key gaps remain. Most studies focus on large cities, leaving small and medium-sized cities understudied. Many also rely on a single spatial scale and treat UG as an aggregated variable, despite different UG types offering varying benefits. Additionally, access to UG has received less attention than availability. To address these gaps, I used the St. John’s Census Metropolitan Area, NL, Canada, as a case study. Data were collected from various sources, including Sentinel-2 imagery and the Canadian Census. A combination of spatial and aspatial methods were used to examine how tree and grass cover were distributed among population groups at two spatial scales: Census Tract and Dissemination Area. Accessibility to urban parks was also assessed using two methods—one considering road network and one not. The results demonstrated notable inequalities in UG distribution and park accessibility. These patterns varied by UG type, spatial scale, and assessment method. The findings advance knowledge of UG distribution and offer practical guidance for urban planning and policymaking towards creating more sustainable, resilient, and equitable cities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".