A New Methodological Framework for Crime Spatial Analysis Using Local Entropy Map
Bibliographic record
Abstract
The highest crime rate in major cities has been always a challenge for managers and urban planners. In order to control and reduce the crime rate, different methods have been proposed in recent decades. Considering the relationship between land use and crime, in this study, potential spatial dependency between commercial land uses, banks, bus and subway stations and pickpocketing was investigated in Tehran. To analyze the spatial dependency, local entropy models and nonparametric approach were used. Using ArcGIS ESRI product we created the local entropy maps to show the significance level of each local region, which allows interactive examination of significant local multivariate relationships. The results show a high spatial autocorrelation between mentioned land uses and specified crimes. The parameters indicate a significant cluster distribution. Furthermore, pickpocketing density at the bus stops is at the high Bonferroni level. The results specify that spatial patterns of pickpocketing are related to land use in the study area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".