Exploring Spatial Patterns of Pedestrian Injury by Age and Severity in the City of Toronto, Canada
Bibliographic record
Abstract
The City of Toronto experienced a ten-year high in pedestrian fatalities last year and has the highest pedestrian collision rate of Canadian cities. Walking is one of the most accessible forms of physical activity for all ages. Promoting increased walking for transport may carry forward into reduced air pollution, noise and traffic congestion. Understanding the geography of pedestrian motor vehicle collisions (PMVCs) can provide evidence to inform policy and planning targeting increased walking while reducing pedestrian injury risk. Age related differences in the geography of injury are expected given age-related differences in activity patterns and physical and cognitive abilities. Spatial patterns of PMVCs by age and injury type are studied across the City of Toronto’s, urban and inner suburban neighborhoods. Geographical variation in PMVCs and injuries by age (seniors and children) and severity are explored using indirect standardized rates. Moran’s I statistics are estimated for the standardized rates, to test for the spatial clustering of PMVCs across Toronto’s urban and inner suburban neighborhoods. Distinct spatial patterns of PMVCs and injuries emerged between children and seniors. While evidence of spatial clustering is indicated for both age groups, children’s injuries revealed the strongest level of clustering, while PMVCs involving seniors’ were more dispersed. Fatal and major injury events appear to be more concentrated toward and within Toronto’s inner suburbs. Additional attention, on the policy and planning front, should be given to pedestrian safety in Toronto’s inner suburban neighbourhoods. Intervention planning and implementation should acknowledge spatial differences in PMVCs by age and severity.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".