Using a GIS Approach to Evaluate Spatial Accessibility to Neighbourhood Amenities and Natural Environmental Features: Implications for Older Adult Health in the Toronto CMA
Bibliographic record
Abstract
Older adults (i.e., adults aged 65+) have become the fastest-growing age group in Canada. This demographic shift has resulted in accelerated demand for health-promoting resources as older adults age in place. This project analyzes potential spatial accessibility of health-promoting neighbourhood amenities (NAs) and natural environmental features (NEFs) among Census tracts and Census subdivisions in the Toronto Census Metropolitan Area. Spatial accessibility of these resources is analyzed for older adults residing in different neighbourhoods and communities. Geographic information systems are used to implement methods including the enhanced two-step floating catchment area accessibility model, location quotient, and bivariate local indicators of spatial association. Two travel scenarios (i.e., walking and driving) represent common modes of transportation. Results indicate significant variation in spatial accessibility between NAs and NEFs and spatial mismatch between older adult concentration and spatial accessibility. This project provides a modest basis for improvement of service provision, supportive infrastructure, and health promotion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".