A Bayesian spatial shared component model for identifying crime-general and crime-specific hotspots
Bibliographic record
Abstract
The spatial patterning of crime hotspots provides place-based information for the design, allocation, and implementation of crime prevention policies and programmes. However, most spatial hotspot identification methods are univariate, analyse a single crime type, and do not consider if hotspots are shared amongst multiple crime types. This study applies a Bayesian spatial shared component model to identify crime-general and crime-specific hotspots for violent crime and property crime at the small-area scale. The spatial shared component model jointly analyzes both violent crime and property crime and separates the area-specific risks of each crime type into one shared component, which captures the underlying crime-general spatial pattern common to both crime types, and one type-specific component, which captures the crime-specific spatial pattern that diverges from the shared pattern. Crime-general and crime-specific hotspots are classified based on the posterior probability estimates of the shared and type-specific components, respectively. Results show that the crime-general pattern explains approximately 81% of the total variation of violent crime and 70% of the total variation of property crime. Crime-general hotspots are found to be more frequent than crime-specific hotspots, and property crime-specific hotspots are more frequent than violent crime-specific hotspots. Crime-general and crime-specific hotspots are areas that may be targeted with comprehensive initiatives designed for multiple crime types or specialized initiatives designed for a single crime type, respectively.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".