Comparisons Between Robbery and Break‐And‐Enter: Area‐Specific Trends, Socioeconomic Risk Factors, and Hotspots Analysis Using a Bayesian Spatial and Spatiotemporal Approach
Bibliographic record
Abstract
ABSTRACT Robbery and break‐and‐enter (BE) crimes require investigations into how these contrasting crimes co‐occur. Utilizing robbery and BE data from the City of Toronto in Canada, this study analyzed the mean and area‐specific crime trends, their risk factors, and the shared and crime‐specific risk and hotspot areas. Results suggest an increase in robbery (0.23, 95% credible interval (CI): 0.17–0.29) and BE (0.08, 95% CI: 0.04–0.12) crimes during 2021–2022, revealing the most prominent area‐specific trends in northwest and northeastern Toronto. The findings suggest that spatially lagged variables can offer deeper insights into complex spatial interactions of real‐life factors that influence crime. Robberies were positively associated with the household and dwellings indicator (2021 Ontario Marginalization Index) but not its spatial lag, while BE crimes had no direct association with it but showed a positive association with its spatial lag. Neighborhoods in northwestern, northeastern, and southcentral parts of Toronto were hotspots of robberies, while southcentral and northwestern parts were at elevated risk due to BE. The findings demonstrate the complexities associated with the co‐occurrence of multiple crime types and highlight the need for more unified and integrated theories to contextualize neighborhood effects of crime determinants and their impact on crimes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".