Comparisons Between Robbery and Break‐And‐Enter
Area‐Specific Trends, Socioeconomic Risk Factors, and Hotspots Analysis Using a Bayesian Spatial and Spatiotemporal Approach
Bibliographic Data
| ID | 21321739 |
|---|---|
| Authors | Jane Law (0000-0001-6259-9037, School of Planning University of Waterloo Canada, corresponding author), Abu Yousuf Md Abdullah (0000-0001-8641-990X, School of Planning University of Waterloo Canada) |
| Year | 2025 |
| Volume | 57 |
| Issue | 3 |
| Pages | 463-477 |
| Publication date | 2025-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Geographical Analysis (JOURNAL) |
| Journal identifiers | ISSN: 0016-7363 • E-ISSN: 1538-4632 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/gean.12421 |
| OpenAlex | W4407383820 |
| Language | EN |
| References cited | 43 |
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
Bayesian probability · Geography · Population · Socioeconomic status · Sociology · Statistics · Crime Patterns and Interventions · Demography · Homelessness and Social Issues · Mathematics · Substance Abuse Treatment and Outcomes
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| Highly cited | No |
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