Does a Large Crime Decline Mean That Hot Spots of Crime Are No Longer ‘Hot’?: Evidence from a Study of New York City Street Segments
Bibliographic record
Abstract
Abstract The law of crime concentration at places predicts that hot spot streets in a city will maintain very high crime levels even when there are strong crime drops in a city overall. We use New York City as a case study focusing on crime at street segments to illustrate this outcome. New York City experienced very large crime declines over the last quarter-century. Nonetheless, looking at the hot spot street segments that produce 25% and 50% of crime in 2010, 2015, and 2020, we find that many New York City streets continue to have very high levels of crime. In 2020, for example, over 1,100 street segments in the city evidenced more than 39 crime reports. These data suggest that the argument that a city can disengage from policing when overall crime rates are low, belies the reality that hot spots of crime are likely to continue to be ‘hot’ during such periods.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".