SPORS: A Suspect Recommendation System Based on Offenders' Reconstructed Spatial Profile
Bibliographic record
Abstract
According to Crime Pattern Theory, individuals all have routine daily activities which require frequent travel between several nodes, with each used for various purposes, such as their home, work, or shopping location. As people move about, their familiarity with the spatial areas around, and in between, the nodes increases, eventually forming their Activity Space. Offenders have similar spatial movement patterns and Activity Spaces as non-offenders, hence, according to theory, an offender will commit the crimes in their own Activity Space. Previous work in this research area determined the Activity Nodes in a city using clustering techniques based on the directionality of crime locations of repeat offenders. This paper extends that research by proposing a top-k recommendation system, called SPORS, which reconstructs the entire Activity Space for offenders and, for any new crime, recommends the top-k likely suspects for that crime. This algorithm is evaluated using information about 322 repeat offenders within the City of Surrey. Using only the spatial location of previous crimes and the home location of the offenders, SPORS is found to accurately predict the correct offender 22.30% of the time when predicting the top-10 suspects, a 718% improvement over naïve random selection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".