Pedestrian injury and the built environment: an environmental scan of hotspots
Bibliographic record
Abstract
BACKGROUND: Pedestrian injury frequently results in devastating and costly injuries and accounts for 11% of all road user fatalities. In the United States in 2006 there were 4,784 fatalities and 61,000 injuries from pedestrian injury, and in 2007 there were 4,654 fatalities and 70,000 injuries. In Canada, injury is the leading cause of death for those under 45 years of age and the fourth most common cause of death for all ages Traumatic pedestrian injury results in nearly 4000 hospitalizations in Canada annually. These injuries result from the interplay of modifiable environmental factors. The objective of this study was to determine links between the built environment and pedestrian injury hotspots in Vancouver. METHODS: Data were obtained from the Insurance Corporation of British Columbia (ICBC) for the 6 year period from 2000 to 2005 and combined with pedestrian injury data extracted from the British Columbia Trauma Registry (BCTR) for the same period. High incident locations (hotspots) for pedestrian injury in the City of Vancouver were identified and mapped using geographic information systems (GIS), and the characteristics of the built environment at each of the hotspot locations were examined by a team of researchers. RESULTS: The analysis highlighted 32 pedestrian injury hotspot locations in Vancouver. 31 of 32 hotspots were situated on major roads. Likewise, the majority of hotspots were located on downtown streets. The 'downtown eastside' was identified as an area with multiple high-incident locations, including the 2 highest ranked pedestrian injury hotspots. Bars were present at 21 of the hotspot locations, with 11 of these locations being judged to have high alcohol establishment density. CONCLUSION: This study highlighted the disproportionate burden of pedestrian injury centred on the downtown eastside area of Vancouver. The environmental scan revealed that important passive pedestrian safety countermeasures were only present at a minority of high-incident locations. More importantly, bars were highly associated with risk of pedestrian injury. This study is the basis for potential public health intervention by clearly indicating optimal locations for signalized pedestrian crosswalks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".