Spatial pattern analysis of robbery and spousal assault in Vancouver between 1989 and 2000 utilizing Geographic Information Systems
Bibliographic record
Abstract
It has been repeatedly shown that there are temporal and spatial concentrations of crime. Various research indicates that a motivated offender has a greater chance of committing a crime near his or her home base, and may also travel to familiar places where more potential targets exist. Thus, the areas where motivated offenders live and spend time, or pass by frequently in their daily activities will tend to have more occurrences of criminal events influencing patterns in crime. Traditionally, it has been thought that spatial patterns of crime are more related to stranger-to-stranger property and / or violent crimes than crimes occurring between known-to-known people. In this research, it is argued that patterns of crime exist whether it is crime occurring between stranger-to-stranger or known-to-known. Therefore, the purpose of this research was to examine whether spatial concentrations of crime exist in two types of crime: robbery and spousal assault. The present research explored spatial patterns of spousal assault and robbery by mapping out the Vancouver Police Department's data of calls for service in selected years between 1989 and 2000 utilizing Geographic Information Systems (GIS). It was found that in both crimes, spatial patterns exist and these patterns were stable during the observed time periods. By examining repeat victimization of locations, it was supported that repeatedly victimized locations disproportionately contribute to both spatial crime patterns and crime rates. As a last step, Location Quotients of Crime (LQC) were calculated for both crimes to assess the relative risks and centres of both crimes. As expected from the theoretical frame of environmental criminology, spatial patterns of robbery and spousal assault were different. As this research demonstrates, analyses utilizing GIS can offer useful information regarding crime hot spots and the extent of crime concentration in a given geographical area. In the future, findings from such research need to be employed in an effort to improve crime prevention, detection, and forecasting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".