Crime Fluctuations in Response to Hurricane Evacuations: Understanding the Time-Course of Crime Opportunities during Hurricane Harvey
Bibliographic record
Abstract
Research regarding how natural disasters impact crime is largely mixed. Most studies focus on whether aggregate postdisaster crime levels differ from predisaster ones and pay less attention to how emergency procedures impact the timing of crime fluctuations. A recent study of Hurricane Rita in Houston, Texas, uncovered a surge in burglary prior to the storm, suggesting that the prestorm evacuation increased the opportunities for burglary by reducing guardianship. This suggests that researchers should examine crime fluctuations that may occur before, during, and after natural disasters. Using nonparametric kernel regression models, we examined crime trends surrounding Hurricane Harvey that occurred in Houston 12 years later where no prestorm evacuation was ordered. We observed no crime surge prior to the storm. Instead, we observed substantial increases for some crime types after the hurricane made landfall that coincided with poststorm evacuations. This supports previous findings that evacuations may create certain crime opportunities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".