Spatial distribution of roadway environment features related to child pedestrian safety by census tract income in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: Investments in traffic calming infrastructure and other street design features can enhance pedestrian safety as well as contribute to the 'walkability' of neighbourhoods. Pedestrian-motor vehicle collisions (PMVCs) in urban areas, however, remain common and occur more frequently in lower income neighbourhoods. While risk and protective features of roadways related to PMVC have been identified, little research exists examining the distribution of roadway environment features. This study examined the relationship between roadway environment features related to child pedestrian safety and census tract income status in Toronto. METHODS: Spatial cluster detection based on 2006 census tract data identified low-income and high-income census tract clusters in Toronto. Police-reported PMVC data involving children between the ages of 5 and 14 years were mapped using geographical information system. Also mapped were roadway environment features (densities of speed humps, crossing guards, local roads, one-way streets and missing sidewalks). Multivariate logistic regression was used to examine the relationship between roadway environment features (independent variables) and cluster income status (dependent variable), controlling for child census tract population. RESULTS: There were significantly fewer speed humps and local roads in low-income versus high-income clusters. Child PMVC rates were 5.4 times higher in low-income versus high-income clusters. CONCLUSION: Socioeconomic inequities in the distribution of roadway environment features related to child pedestrian safety have policy and process implications related to the safety of child pedestrians in urban neighbourhoods.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".