Finding Criminal Attractors Based on Offenders' Directionality of Crimes
Bibliographic record
Abstract
According to Crime Pattern Theory, individuals all have routine daily activities which require frequent travel between several nodes, with each being used for a different purpose, such as home, work or shopping. As people move between these nodes, their familiarity with the spatial area around the nodes, as well as between nodes, increases. Offenders have the same spatial movement patterns and Awareness Spaces as regular people, hence according to theory an offender will commit the crimes in their own Awareness Space. This idea is used to predict the location of the nodes within the Awareness Space of offenders. The activities of 57,962 offenders who were charged or charges were recommended against them were used to test this idea by mapping their offense locations with respect to their home locations to determine the directions they move. Once directionality to crime was established for each offender, a unique clustering technique, based on K-Means, was used to calculate their Cardinal Directions through which the awareness nodes for all offenders were calculated. It was found that, by looking at the results of various clustering parameters, offenders tend to move towards central shopping areas in a city, and commit crimes along the way. Almost all cluster centers were within one kilometer of a shopping center. This technique of finding Criminal Attractors allows for the reconstruction of the spatial profile of offenders, which allows for narrowing the possible suspects for new crimes.
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How this classification was reachedexpand
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.026 | 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 teacher head, 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".