Exploring ‘near’: Characterizing the spatial extent of drinking place influence on crime
Bibliographic record
Abstract
The important role of facilities in understanding crime patterns is widely recognized. Studies have demonstrated a connection between the presence of facilities such as bars, parks, schools and fast food restaurants and higher crime rates. Typically, these studies use a single distance threshold. Areas within the threshold are assumed to be related to the facility and those outside the threshold unrelated. But the choice of threshold in each study is usually an ad hoc decision based on the expertise of the researcher. Until recently, there has been no systematic evaluation of the methodology used to define those thresholds. This paper evaluates two methods for determining an empirically-based answer to the question ‘How close is “near”?’ The results of an example analysis testing the association of drinking places and crime in Seattle, Washington are reported. The two most common facility-based measures, Euclidean distance buffers and Street distance buffers are compared across two levels of aggregation and 18 separate distances. Findings indicate the geographic extent of increased crime around drinking places varies based on the type of buffer (Euclidean vs. Street distance) and the width of the distance bands (street block vs. 402 meter (quarter mile) increments). The geographic extent of the influence of drinking places on crime is best captured by street distance measures across street block distances (122 meter bands). Implications of these findings for theory and practice are discussed.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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 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".