Pathways of crime: Measuring crime concentration along urban roadways
Bibliographic record
Abstract
Some urban spaces are associated with disproportionate numbers of criminal events, while other areas are relatively free from disorder and crime. The relationship between urban space and crime concentration has received increased attention in recent years, with the location quotient frequently presented as a tool to identify and quantify such concentration. This measure has several limitations, with one significant concern surrounding the choice of denominator with which to standardize local and global rate calculations. In response, we present a new methodological adaptation to the location quotient, improving the measurement of crime concentration along linear features. To test this adaptation, we measure how crime concentrates by road classification at both a macro and micro level within two Canadian suburban municipalities. Using transportation network data, we identify the road types that are associated with a disproportionate concentration of criminal events, and illustrate how these relationships change alongside the level of aggregation. Results support the use of the adapted location quotient, finding that criminal events concentrate along specific road types, and emphasize the importance of spatial scale in understanding local relationships between crime and the built urban landscape.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".