Quantification of green and walkable neighbourhoods across the distribution of social and material deprivation in Metro Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND & AIM: Along with other environmental exposures, both neighbourhood walkability, and greenspace exposure and access have been linked to many diverse health benefits. Previous work has examined the spatial relationship of built and natural environments according to levels of neighbourhood deprivation; however, less is known about how these relationships may vary when different greenspace metrics are used. This work examines how normalized differences vegetation index (NDVI), tree canopy cover, green land cover, and park count metrics relate to walkability, and how these neighbourhood characteristics associate with both social and material deprivation. METHODS: Greenspace exposure was measured using NDVI, tree canopy cover, and green land cover, while park access was quantified by the number of designated public parks within 1000m and 400m network buffers for each six-digit postal-code centroid in Metro Vancouver, Canada. Local area deprivation was measured using the 2016 Material and Social Deprivation Index (MSDI). Pearson’s correlation coefficients were calculated to compare these neighbourhood characteristics. RESULTS: Walkability was positively associated with social deprivation (1000m r = 0.48; 400m r = 0.48, p < .01), while walkability has a weak inverse relationship with material deprivation (1000m r = -0.21, p < .01). Tree canopy was negatively related to both social (1000m r = -0.23, 400m r = -0.23, p < .01) and material deprivation (1000m r = -0.24, 400m r = -0.23, p < .01). In contrast, the relationship between park count and both deprivation measures was weak. CONCLUSION: In this study, areas with greater social and material deprivation tend to have less greenspace, but not necessarily less park access. The identification of neighbourhoods with higher material deprivation, low walkability, and low greenspace, may be prioritized by urban planners and decision makers as possible locations for additional greenspace allocation. KEYWORDS: greenspace, walkability, social deprivation, material deprivation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".