The use of geographic information systems to study repeat victims of domestic abuse in the Waterloo region of Ontario, Canada
Bibliographic record
Abstract
The importance of repeat victims as an effective target for crime prevention measures has been widely recognised. Many have studied repeat victims of property crimes, but few have studied repeat victims of personal crimes and their spatial distribution. This study used 1996 and 1997 crime data from the Waterloo Regional Police Service (WRPS) in Ontario, Canada and focused specifically on domestic-abuse-related crimes and repeat victims of those crimes. Various methods of mapping and analysis were used to better understand the spatial distribution of those crimes and vulnerability of the victims. Standardised crime rates and crime density were calculated. Thematic and dasymetric mapping were used to visualize the crime data. A hot spot analysis was performed as well as correlation analyses of the crime data with selected socio-economic characteristics. Problems relating to the geocoding, denominators used in calculations, small area of some spatial units, data representation, and ecological fallacy were documented. The different mapping and analysis methods used identified similar anticipated trends in the data. The various techniques consistently highlighted the city centres as pockets of high domestic-abuse-related crime activity. There were also pockets outside those areas, anomalies that resulted from data and denominator problems, and likely other problems that should be further explored in future research. The trends uncovered are however more reflective of the police data than they are of the reality of domestic abuse. The bias found in the data limits the type of analysis it can support. Nonetheless significant findings resulted from this study, and recommendations were made on how to minimize the bias in the data and still be able to extract valuable information. Domestic abuse is a widespread problem, even more so than it appears as available data is not complete. Prevention efforts should therefore be geared to the general population to raise awareness of the problem and of the resources available to victims for help.
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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 | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".