Opportunity in Transit: Bus Stop Crowding and Crime
Bibliographic record
Abstract
Fifty years after the introduction of the Crime as Opportunity theory, this paper applies its core insights to a micro-temporal empirical setting to examine how short-lived fluctuations in urban mobility shape street crime. While transit nodes are recognized as crime generators, previous research has remained largely descriptive and constrained by coarse temporal resolutions that overlook short-term opportunity fluctuations. We address this gap by assessing whether brief periods of crowding at bus stops causally increase police reports of non-violent property street crimes-offenses highly dependent on opportunity structures. Leveraging high-resolution spatial data from Montevideo, Uruguay, we implement an imputation difference-indifferences estimator to isolate intra-day variation in passenger flows and test opportunity theory at a micro-temporal scale. Our findings reveal offense-specific selectivity: short-term crowding peaks significantly elevate theft risk, whereas no significant effects are detected for robberies. These results indicate that non-violent property crimes are more sensitive to the presence of opportunity, showing that routine and transient mobility fluctuations exert immediate effects on street crime by reshaping the distribution of opportunities in urban environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".