Crime dynamics in Edmonton’s train stations: analysing hot spots, harm spots and offender patterns
Bibliographic record
Abstract
Purpose The objectives of this study are to (a) identify spatial and temporal crime concentrations, (b) supplement the traditional place-based analysis that defines hot spots based on counted incidents with an analysis of crime severity and (c) add to the research of hot spots with an analysis of offender data. Design/methodology/approach This study explores crime concentration in mass transit settings, focusing on Edmonton’s Light Rail Transit (LRT) stations in 2017–2022. Pareto curves are used to observe the degree of concentration of crime in certain locations using multiple estimates; trajectory analysis is then used to observe crime patterns in the data on both places and offenders. Findings A total of 16.3% of stations accounted for 50% of recorded incidents. Train stations with high or low crime counts and severity remained as such consistently over time. Additionally, 3.6% of offenders accounted for 50% of incident count, while 5% accounted for 50% of harm. We did not observe differences in the patterns and distributions of crime concentrations when comparing crime counts and harm. Research limitations/implications Hot spots and harm spots are synonymous in low-crime-harm environments: high-harm incidents are outliers, and their weight in the average crime severity score is limited. More sensitive severity measures are needed for high-frequenty, low-harm enviornments. Practical implications The findings underscore the benefits of integrating offender data in place-based applied research. Originality/value The findings provide additional evidence on the utility of place-based criminology and potentially cost-effective interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".