Are There Non-Business Days for Crime? A Small-Area Bayesian Spatiotemporal Analysis of Crime Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Criminal behavior may be different on weekends and holidays compared to business days. Understanding the distinctive crime patterns on non-business days is useful for crime research and crime control. This study contributes to the literature by explicitly investigating the small-area spatiotemporal variation in five types of major crimes between business days and non-business days using a Bayesian modeling approach in Old Toronto, Canada. The results show that criminal activity varies between business days and non-business days, influenced by the types of crimes, geographic locations, and local neighborhood characteristics. Compared to business days, on non-business days, southern areas with high business and entertainment activity exhibit increased assault and robbery levels, while northern residential areas experience reduced activity of break and enter, auto theft, and theft over $5,000. Nonetheless, spatial crime hot spots generally remain consistent between the two date categories, with some hot spots presenting an exacerbation of criminal activity during non-business days. A few sociodemographic variables and built environment features are associated with the spatiotemporal variation in crime. These findings demonstrate the spatiotemporal variation in criminal behavior and crime patterns between business days and non-business days and highlight the need for customized crime control measures at the small area level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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 it