Socio-spatial disparities in urban green space accessibility: The existing challenge for Toronto in its aspiration to be a liveable city
Bibliographic record
Abstract
Toronto, a thriving multicultural metropolis, aspires to create an inclusive and livable urban environment meeting diverse resident needs. However, challenges arise due to the uneven distribution of urban green spaces. This study employs a gravity model and Gaussian-based 2SFCA model to assess green space accessibility in Toronto’s dissemination areas. A Gini index and local bivariate Moran’s I illuminate socio-spatial disparities, while Geographically Weighted Regression unveils economic inequalities by correlating green space accessibility with housing prices and their five-year growth. Findings expose stark environmental inequity, with the bottom 20% accessing a mere 7% of spaces and the top 20% enjoying 40%. City center and low-income peri-central areas exhibit pronounced disparities, driven by limited green spaces and intense competition. In flourishing, dense areas, residents pay more for increased green space share, while less-dense areas with ample green spaces see higher housing prices where accessibility prevails. Neighborhoods with abundant green spaces and amenities, notably special school programs, attract families, correlating housing price growth with green space accessibility. Considering diverse district development phases and priorities and potential conflicts, tailored strategies for equitable green space systems are recommended citywide.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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".