The Prediction Error Bound in Urban Crime: Temporal Predictability
Bibliographic record
Abstract
Predicting when and where crime occurs is essential and a significant task to support preventive policing. This subsequently has important outcomes for economic benefits, urban planning and human safety—predictability, which is a theoretical bound for the prediction of performance in human behavior based on limited data. Current approaches to predictability in human behavior usually measure prediction accuracy which is aimed at classification issues such as the next location of prediction and the lack of measurement for regression problems. To further research in this area, this study proposes a new method based on differential entropy to compute the prediction error as a form of mean square error to derive the minimum level of error referred to as temporal predictability. Special emphasis is placed on investigating the sensitivity of the predictability methods with regard to changing the data lengths. The method was evaluated using public crime datasets from four cities (Washington DC, Denver, New York, and Vancouver) collected between 2016 and 2022. The results from the study support the hypothesis of correlation between the minimum amount of data and the level of temporal predictability, which can guide the prediction of the regression issue.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".