Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet
Bibliographic record
Abstract
Abstract Purpose/background A new body of research that focuses on crime harm scores rather than counts of crime incidents has emerged. Specifically in the context of spatial analysis of crime, focusing on crime harm suggests that harm is more concentrated than counts, at the level of crime hot spots. It remains presently unclear what drives the concentration distributions, and whether the count-based model should be abandoned. Methods Cross-sectional and longitudinal analysis of 6 year of spatiotemporal crime data in Toronto, Canada, to compare patterns and concentration of crime harm (measured in terms of the Crime Severity Index (CSI) against crime counts. Conditional probabilities, trajectory analyses, power few concentrations, and spatial Global Moran’s I are used to infer generalised trends from the data. Findings Overall CSI and crime counts tend to exhibit similar concentrations at the spatial micro levels, except against-the-body crimes such as violence which seems to drive nearly all the variations between the two measurement types. Violence harm spots tend to be more dispersed citywide and often do not remain constant year-to-year, whereas overall crime hotspots are more stable over time. Nevertheless, variations in disproportionally high crime hot spots are associated with total variations in crime, with as little as 1% increase in crime levels in these hot spots translating into substantial overall gains in recorded crime citywide. Conclusions Abandoning count-based models in spatial analysis of crime can lead to an incomplete picture of crime concentrations. Both models are needed not just for understanding spatial crime distributions but also for cost-effective allocation of policing resources.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".