More than an offender location tool: Geographic profiling and body deposition
Bibliographic record
Abstract
In homicide cases, it is difficult to provide resolution for the bereaved or to obtain a successful criminal conviction of the guilty party when no body is found. Since the mid-nineteenth century, geographic and environmental patterns have been used to better understand the relationship between crime and its environment. Now known as geographic profiling, practitioners in this field amalgamate criminological, psychological, and geographical knowledge, as well as aspects of mathematics, statistics, and physics to identify spatial patterns associated with criminal behaviour as a means of locating anchor points of an offender (where they live, or work). The same techniques can also be used to locate the covert body deposition sites of their victims. This paper aims to (1) provide a brief summary of criminal behaviour and the environment and how understanding their relationship can be helpful to geographic profiling, (2) amalgamate the available literature on the application of geographic profiling in locating clandestine graves (as most documented uses are to locate offender residences), and (3) include a geographic profile of Ivan Milat, an Australian serial killer (officially) active from 1989 to 1992, demonstrating how geographic profiling techniques can help to identify additional victims and potential body deposition sites. The information in this review will be helpful to law enforcement and practitioners to improve missing persons investigations and searches for clandestine graves.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".