Exploring Hotspots of Drug Offences in Toronto: A Comparison of Four Local Spatial Cluster Detection Methods
Bibliographic record
Abstract
Spatial cluster detection is an exploratory spatial data analysis technique that identifies areas or groups of areas with disproportionately high risk. Several local cluster detection methods have been developed; yet no research has critiqued these methods as they contribute to spatial studies of crime. This study aims to identify the locations of drug offence hotspots in Toronto and compare the clusters detected through four methods: (1) spatial scan statistic – Euclidean distance, (2) spatial scan statistic – non-Euclidean contiguity, (3) flexibly shaped scan statistic, and (4) local Moran's I. It was found that all methods detected clusters in the downtown, with fewer methods detecting clusters in the west and east of Toronto. It was observed that the spatial scan statistic detected the largest and most circular clusters, making it a suitable tool to inform general policing initiatives and highlight possible variables to be included in confirmatory research. The local Moran's I method, in contrast, found the smallest and most compact clusters, indicating that it is an appropriate test for identifying areas where resource intensive crime prevention and policing efforts should be targeted.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".