Inequality and allergenic cover of urban greenspaces surrounding public elementary schools in Vancouver, British Columbia, Canada
Bibliographic record
Abstract
Inequality in the spatial distribution of urban greenspaces occurs globally, with greater greenspaces in neighbourhoods with higher socioeconomic status. This is problematic, as greenspaces provide numerous ecosystem services, including benefits to human health. However, greenspaces can also trigger allergenic responses, inducing negative economic, medical, and social costs. Using geospatial information, we investigated 91 elementary schools in Vancouver, British Columbia, Canada to answer: (1) Does the amount and type of greenspaces and greyspaces surrounding schools vary with median household income? and (2) Does the surface area of allergenic greenspace surrounding schools vary with median household income? We characterized landcover within a 300 m radius of public elementary schools using a high spatial resolution urban landcover map of Vancouver derived from a combination of RapidEye imagery from 2014 and airborne laser scanning. Beta regression and analysis of variance models were used to explore associations between household incomes and greenspaces, as well as allergenic vegetation near schools. Schools in areas with higher median annual household incomes (>$80,000 CAD) were surrounded by an average of 14% more greenspaces and 16% less greyspaces than schools located in areas with lower household incomes (<$50,000 CAD). Schools in higher income areas were also surrounded by an average of 81% more cover of allergenic vegetation than schools in lower income areas. Greenspaces are a valuable source of ecosystem services for urban residents and should be distributed equally to optimize their benefits; however, they must be planned carefully to avoid the introduction of disservices from allergenic vegetation.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".