Fear and loathing on public transportation: Applying a spatial framework to crime patterns on Vancouver's Canada Line SkyTrain System
Bibliographic record
Abstract
The expansion of mass forms of public transportation systems have often been resisted due to fears and concerns over an increased level of crime. The following study seeks to determine whether the SkyTrain’s Canada Line has increased levels of reported crime in six criminal offence categories: commercial burglary, residential burglary, mischief, theft, theft from vehicle, and theft of vehicle between January 2003 and December 2015 in Vancouver, British Columbia. Time series regression, panel data analysis, and spatial point pattern tests are applied to determine whether such concerns should be merited or disregarded in the study of crime and transportation. Results demonstrate that census tracts that host a Canada Line SkyTrain station do not increase levels of crime. Rather, census tracts that host multiple SkyTrain stations and/or are situated in socially disorganized neighbourhoods are at a higher level of risk for criminal victimization. These findings are critical in removing the negative stigma surrounding mass forms of public transportation systems. Additionally, these results assist local police, transit authorities, and urban planners to create appropriate crime prevention strategies to prevent crime while restructuring public discourse about the potential criminogenic effects from public transportation systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".