Uncovering the Spatial Patterning of Crimes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: The main objective of this study was to see if the characteristics of offenders’ crimes exhibit spatial patterning in crime neutral areas by examining the relationship between simulated travel routes of offenders along the physical road network and the actual locations of their crimes in the same geographic space. Method: This study introduced a Criminal Movement model (CriMM) that simulates travel patterns of known offenders. Using offenders’ home locations, locations of major attractors (e.g., shopping centers), and variations of Dijkstra’s shortest path algorithm we modeled the routes that offenders are likely to take when traveling from their home to an attractor. We then compare the locations of offenders’ crimes to these paths and analyze their proximity characteristics. This process was carried out using data on 7,807 property offenders from five municipalities in the Greater Vancouver Regional District (GVRD) in British Columbia, Canada. Results: The results show that a great proportion of crimes tend to be located geographically proximal to the simulated travel paths with a distance decay pattern characterizing the distribution of distance measures. Conclusion: These results lend support to Crime Pattern Theory and the idea that there is an underlying pattern to crimes in crime neutral areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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) | 0.001 | 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 it