Spatial Analysis in Environmental Justice Research: Neighbourhood Exposure to Traffic Emissions in Toronto, Canada
Bibliographic record
Abstract
ISEE-526 Objective: As the environmental health equity/justice literature broadens, one area of continuing development concerns the methods used to assess health hazard exposures and effects. The objectives of this article are to: (1) trace the development of spatial analysis in environmental justice research and (2) demonstrate recent developments of air pollution exposure analysis in a case study in Toronto, Canada. Materials and Methods: The case study is of traffic-generated NO2 in Toronto. GIS and spatial analysis are used to (1) allocate 95 fixed site passive samplers for measurements in the field in Spring 2002, (2) use these measurements to calibrate a land use regression (LUR) model of annual average ambient NO2, (3) use this calibrated LUR model to estimate a continuous surface of long-term NO2 values for the entire city, and (4) analyze the relationship between measured/modeled NO2 and neighborhood markers of socioeconomic status. Results: (1) Standard regulatory air monitoring significantly underestimates criterion air pollutants at fine spatial scales (neighborhoods), including short-term air quality standards and guidelines, (2) in models with neighborhood SES markers, some expected positive associations with air pollution are found (low income and low education) whereas other unexpected associations are also found (dwelling value and high-status occupations). Conclusions: GIS and spatial analysis afford the opportunity to analyze justice and equity relationships at fine spatial scales although some methods rely on intensive sampling campaigns. Accordingly this article concludes with a discussion of the future potential, pitfalls, and prospects of research on the microgeographies of environmental justice and health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".