‘Domestic abuse hot spots’: A longitudinal, place-based analysis of 13 years of initial reports to the police
Bibliographic record
Abstract
Abstract A rich body of literature suggests that crime is concentrated in hotspots, some consistently ‘hot’ over long periods. However, whether there are spatial and temporal concentrations of domestic abuse (DA) is presently unknown. While it is plausible that DA data follow similar Pareto curves as general crime, it is equally reasonable to assume stochasticity, especially regarding year-to-year consistency. We conducted a retrospective longitudinal analysis of 1.7 million DA initial reports to the police (as opposed to ‘crime incidents’) over 13 years (2007–19) in London, UK. We also examine crime harm patterns, which provide a more nuanced risk estimate for victims based on a crime harm index. We utilize a combination of spatial statistics and trajectory modelling approaches. We find that a small percentage of addresses are responsible for an outsized proportion of DA counts but half the bandwidth for crime harm generated. Year-to-year repeat victimization at specific addresses is 69.9%, and the mean probability of receiving another DA report from the same address in the following month is 41%. For both crime count and harm models, locations with either low or high DA reportage remained as such throughout the study. Changes in less than 1% of locations will drive DA trends in London. We conclude that concentrating on place-based emergency-calls-for-service data rather than crime reports unmasks a substantially greater likelihood of repeat DA victimization than previously assumed. The discovery of a spatiotemporal DA hotspot allows law enforcement to ‘zero in’ prevention efforts on a small number of premises relative to the overall scale of the capital. Future DA research should place greater weight on micro-place factors associated with DA to calibrate prevention efforts’ accuracy and efficiency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".