PW 0318 Child pedestrian risk and social equity: spatial distribution of roadway safety features in toronto, canada
Bibliographic record
Abstract
Investments in road design features are made to improve pedestrian safety in urban areas. Pedestrian motor vehicle collisions (PMVC); however, remain common, and occur at higher frequency in lower income neighborhoods. The objective of this study was to compare child PMVC rates and the distribution of roadway environment features related to child pedestrian safety in low versus high income clusters, in Toronto, Canada. Spatial cluster detection by census tract identified low and high income clusters using Canadian census data. Police-reported data of 2185 PMVCs involving children ages 5–14 from 2001–2010 were mapped with speed humps, crossing guards, missing sidewalks, one-way streets and local roads. Relationships between roadway features and low versus high income clusters were examined using multiple logistic regression. Of 524 census tracts, fifty eight (11%) were in high and 44 (8%) were in low income clusters. Collision rates were almost 6 times higher in low income clusters. For every km/10 km road increase of speed humps there was a 65% lower odds, for every km/10 km road increase in local roads there was a 38% lower odds and for every additional crossing guard/10 km road there was a 43% greater odds of being in a lower income cluster. Fewer lower speed local roads and speed humps in lower income areas may put children at increased risk. The inequity in spatial distribution of speed humps may due to process of request which is initiated by the community and may favour higher income communities. More school crossing guards may reflect more children walking in lower income areas, as well as attempts to ameliorate more dangerous road environments with higher PMVC rates. Policy implications relate to the equitable distribution of roadway features to provide safe pedestrian environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".