Inequitable Spatial and Temporal Patterns in the Distribution of Multiple Environmental Risks and Benefits in Metro Vancouver
Bibliographic record
Abstract
Abstract The urban environment impacts residents' health and well‐being in many ways. Environmental benefits and risks may be interactively and inequitably distributed across different populations in cities, and these patterns may change over time. Here, we assess the spatial distribution of environmental risks and benefits in pairs, considering synergies and trade‐offs, in an illustrative metropolitan area (Metro Vancouver) in Canada in the years 2006 and 2016. We classify census dissemination areas as sweet, sour, risky, or medium spots based on relative exposures for six environmental combinations: Walkability and NO2; heat stress and NO2; vegetation coverage and NO2; vegetation coverage and heat stress; walkability and accessibility to natural recreational areas; and heat stress and accessibility to natural recreational areas. We evaluate whether different population groups are disproportionately exposed to lower environmental quality based on linear regressions and other metrics. We find that while performance for individual environmental variables improved over the decade, considering their combinations, sweet spots became sweeter and sour spots became sourer. Residents with high material and social deprivation and visible minorities were disproportionately exposed to lower environmental quality in both years for most of the environmental combinations. Further, we find that these inequities were not improving over time for all groups: for instance, South Asian residents in the region faced higher disproportionate burdens or diminished access to benefits in 2016, as compared to 2006. Given these findings, we suggest considerations of cumulative exposure in prioritizing areas for intervention, targeting the sour and risky spots persistently experienced by overburdened populations.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".