Spatial Analysis of Characteristics and Influencing Factors of Killed or Seriously Injured Persons from Motor Vehicle Collisions within the City of Toronto
Bibliographic record
Abstract
This research is intended to support policy makers, infrastructure designers, road safety planners, and law enforcement in the identification of the underlying characteristics of Killed or Seriously Injured (KSI) between 2006 and 2019, as a result of Motor Vehicle Collisions (MVCs) within the City of Toronto. A combination of global & local spatial autocorrelation testing approaches with Moran’s I and Getis-Ord followed by statistical modelling were leveraged. Results determined KSIs within the City of Toronto were not random in nature and spatial interaction was driven by underlying factors. Global autocorrelation was only present in the Downtown Toronto Area. Stepwise Regression Modelling (SRM) revealed a multitude of explanatory factors including: land use, infrastructure density, and demographics to explain the variation within the rate of KSI occurrences with statistical significance. Data for this research was acquired through open data and academic repositories from Toronto Police Service, City of Toronto, and Statistics Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".