Access for whom? Inequality and inequity in multi-modal accessibility to large parks
Bibliographic record
Abstract
Large parks provide vital health, social, and environmental benefits, especially for low-income populations who face disproportionate exposure to environmental stressors and health challenges. While research has explored park access inequalities through walking and driving, less is known about access variations across different transport modes considering common travel sequences and shared mobility options. This study examines multi-modal accessibility to 58 large parks in Metro Vancouver, Canada, focusing on both spatial patterns of accessibility and underlying socioeconomic inequities. Using data from 3,590 neighborhoods, we assess park accessibility through minimum distance, cumulative opportunities, and gravity models. Our findings reveal that driving provides the most equitable access distribution, while alternative modes, particularly shared mobility, show higher inequality and favor wealthier populations. The advanced gravity models accounting for travel time and park quality exposes greater disparities in shared mobility access compared to traditional approaches. These findings highlight the need for urban planners and policymakers to consider multimodal and equity-based approaches in green space planning. Ensuring that new and emerging transport options support rather than hinder equitable park access is critical for promoting inclusive urban environments and advancing environmental justice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".