A GIS-based spatiotemporal analysis of violent trauma hotspots in Vancouver, Canada: identification, contextualisation and intervention
Bibliographic record
Abstract
BACKGROUND: In 2002, the WHO declared interpersonal violence to be a leading public health problem. Previous research demonstrates that urban spaces with a high incidence of violent trauma (hotspots) correlate with features of built environment and social determinants. However, there are few studies that analyse injury data across the axes of both space and time to characterise injury-environment relationships. This paper describes a spatiotemporal analysis of violent injuries in Vancouver, Canada, from 2001 to 2008. METHODS: Using geographic information systems, 575 violent trauma incidents were mapped and analysed using kernel density estimation to identify hotspot locations. Patterns between space, time, victim age and sex and mechanism of injury were investigated with an exploratory approach. RESULTS: Several patterns in space and time were identified and described, corresponding to distinct neighbourhood characteristics. Violent trauma hotspots were most prevalent in Vancouver's nightclub district on Friday and Saturday nights, with higher rates in the most socioeconomically deprived neighbourhoods. Victim sex, age and mechanism of injury also formed strong patterns. Three neighbourhood profiles are presented using the dual axis of space/time to describe the hotspot environments. CONCLUSIONS: This work posits the value of exploratory spatial data analysis using geographic information systems in trauma epidemiology studies and further suggests that using both space and time concurrently to understand urban environmental correlates of injury provides a more granular or higher resolution picture of risk. We discuss implications for injury prevention and control, focusing on education, regulation, the built environment and injury surveillance.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".