Spatial Patterns of Offender Groups
Bibliographic record
Abstract
A co-offending network, the network of offenders who have committed crimes together, is a prime source for crime investigation. Analyzing co-offending networks contributes to crime reduction and prevention strategies and tactics at different levels by extracting meaningful patterns and relationships. On a different track, spatial analysis of crime has recently enriched understanding of criminal activity extensively. This study integrates spatial and social network analysis to understand the role of spatial distance in forming criminal collaborations. First, we extract co-offending networks from a police-reported database and present a comprehensive study of the spatial properties of co-offending networks. Then, using community detection approaches, we detect offender groups as denser sub graphs of some co-offending network. Finally, we study the geography of offender groups as an important characteristic of such groups. Recognizing if offender groups are geographically dispersed or geographically concentrated can help law enforcement and intelligence agencies to prioritize their preventative deployments and proactive investigations in combating crime. For the experimental evaluation, we use a real-world crime dataset comprising crime incidents in the time period 2001-2006 in the regions of British Columbia, Canada policed by the RCMP.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".