Identifying Optimal Scales for Spatio-temporal Crime Clusters
Bibliographic record
Abstract
The spatial and temporal scales are not only two essential parameters for the spatio-temporal \nclustering algorithm to generate the crime clusters but are significantly helpful for determining the \ninterventive distance at space and time in place-based crime prevention. This study presents the issue \nof identifying the optimal spatial-temporal scale when examining the micro-level crime clusters \napproached by density-based spatio-temporal clustering methods. The approach comprises adopting a \nclustering evaluation index to examine the performance of different clustering results from a range of \nspace and time values iteration. For this purpose, two types of density-based clustering algorithms \ncalled ST-DBSCAN and ST-OPTICS are compared to determine the optimal scales for space-time \ncrime clusters. A case study is demonstrated using individual crime records of burglary from \nVancouver, Canada in 2010. Several derived results are significant. First, appropriate scales – 500m \nand 3 days can be distinctively determined by clustering algorithm ST-OPTICS from our tested \nparameters. Second, the narrowed scales were found in this study significantly for spatio-temporal \ncrime clusters, which can help to develop a more focused and specific policing tactics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".