Quantifying Residential Access and Exposure to Greenspace Using High-Resolution Remotely Sensed Data: A Case Study of Metro Vancouver, British Columbia
Bibliographic record
Abstract
Despite growing evidence demonstrating the health benefits of greenspaces, the potential differences in effect depending on type of greenspace exposure remain relatively unexplored. The present research aims to develop a methodology to (1) identify greenspace using remotely sensed data and (2) model residential access and exposure to greenspace and the relative difference in vegetation type.Methods:2014 RapidEye satellite imagery and LiDAR datasets from 2008-2015 were combined in eCognition for object-based segmentation, and then classified using Random Forest in R to produce a high-resolution (5m) land cover map of Metro Vancouver, Canada. The land cover map includes 14 classes – covering, for example, coniferous, deciduous, shrub, and grass-herb vegetation. Using the land cover map, greenspace access was calculated as the presence of a public park, recreation area or reserve (≥ 1 hectare) within 300m of residential postal codes and greenspace exposure was calculated as the proportion of greenspace and each land cover type, within several buffer zones of residential postal codes.Results:The land cover map has an overall accuracy of 89% with a kappa of 0.88. Compared to traditional greenness metrics, such as NDVI, the land cover map provides a more detailed model of the variety and distribution of greenspace. Initial analyses suggest that more urbanized areas have greater access to public greenspace and higher exposure to built-up classes and broadleaf vegetation, with grass-herb vegetation increasingly dominant in rural and agricultural areas.Conclusions:This research presents a method of identifying and quantifying greenspace that can be applied widely. Differentiating greenspace access and exposure metrics, including relative distribution of land cover type, will help define which aspects and qualities of greenspaces may provide the most benefits. Such information will provide important guidance for prioritization in urban planning and public health policy.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".