Access to green space and median household income in metro Vancouver cities
Bibliographic record
Abstract
Background: Greenspace is a very important component of a healthy built environment. It can provide many benefits which include mitigating the effects of climate change, improving air quality, and enhancing mental and physical health. However, it has been shown that health promoting resources such as green spaces are often unequally distributed among different socioeconomic classes. The objective of this study was to identify disparities in proportional greenspace access between different income categories among the residents of three cities in Metro Vancouver, British Columbia. The mapping tool ArcGIS was used to visualize patterns of greenspace distribution and median household income. Methods: Green space was classified as recreational parks within the cities of New Westminster, Vancouver and Burnaby in Metro Vancouver. Income and green space data were gathered from Statistics Canada and the Municipalities’ websites for mapping in ArcGIS respectively. This data was then exported, and a correlation analysis was performed to identify any relationship between green space and median household income of census tract divisions. Results: Out of 248 data points, 90 census tracts were analyzed in Burnaby, 145 in Vancouver and 13 in New Westminster. Patterns in the maps indicated that higher income census tracts had lower proportional access to green space. Statistical results demonstrated that a negative correlation exists between greenspace and median household income. Higher income households have less access to green space across all three Metro Vancouver cities; New Westminster (p = 0.33), Vancouver (p = 0.02) and Burnaby (p = 0.03) a negative correlation was also found in a combined analysis across all three cities (p=0.0013). Conclusion: Green space is undeniably important to all individuals within a city as it can provide recreational opportunities, improve physical and mental health, temper climate change, improve air quality and provide cooling effects. There may be substitutes to recreational activities that green space can provide, but there are none for the overall benefits that it can provide. This study calls for policy makers and planners to consider greater investments in green space and recreational parks in all census tracts including wealthier neighbourhoods, where smaller proportions of greenspace were identified. Programs such as the City of Vancouver’s “Greenest City Action Plan” whose goal is to encourage green initiatives including situating all residents within a five minute distance of greenspace should be implemented across all three 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".