A comparison of greenspace metrics and measurement methods, walkability, and social and material deprivation in Metro Vancouver
Bibliographic record
Abstract
There is extensive literature examining the impacts of the built and natural environment on human health. Along with other environmental exposures, neighbourhood walkability, and greenspace exposure have been linked to many health behaviours and health outcomes. There are several different metrics and methods commonly used to quantify neighbourhood exposure to greenspace. This thesis compares the results of four greenspace metrics (total green land cover, tree canopy cover, normalized difference vegetation index, and park count), as well as the relationship between results calculated using two different methods (circular and network buffers) using 6-digit postal code level data. When comparing the results for the circular and network buffer methods applied to estimating greenspace exposure and access, the results range from moderately to highly correlated. These findings may support environmental health researchers to be intentional about the choice of greenspace metric and buffering methods used to address their specific research question. This thesis also examines the relationship between neighbourhood greenspace, walkability, social deprivation, and material deprivation in the Metro Vancouver Regional District. Consistent with previous work neighbourhood walkability was not highly positively correlated with measures of greenspace, indicating that the most walkable neighbourhoods tend to have less greenspace. Additionally, local area material deprivation was associated with less walkable neighbourhoods and less greenspace. These areas may be sites to prioritize future greenspace allocation and implement land use changes to improve walkability.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".