Bibliographic record
Abstract
The Geography of Crime has a history in criminology that repeatedly finds a clustering of crime in time and space. Research in this field explores spatio-temporal patterning by studying who commits crimes, and why and when they commit crimes more in some parts of a city. Research finds a level of stability for many crimes. This thesis uses reported assault and break-and-enter crimes in Regina to explore in more depth the spatial-temporal patterns of crime and to develop and use hazard-risk modelling to improve methods of predicting future crime concentrations. Specifically, in order to explore and improve current hazard-risk models in criminology, new software was created for this thesis (Crime Risk Assessment Software) in order to generate a geostatistical risk model of two crime types, assault and break-and-enter (dynamic and static, respectively), to determine whether geostatistics, specifically Kriging techniques, could create strong predictive risk surfaces of these crimes. Through this exploratory spatio-temporal research, it was believed that after buildling and testing the model, statistics would reveal that the Kriging model would more accurately predict static crime than it would dynamic crime owing to the mobility issue of the crimes chosen (e.g., assault can happen anywhere spatially whereas break-and-enter can only occur at a static location such as a residence). Using examples provided by the Regina Police Service (RPS) in Saskatchewan, Canada, from assault and break-and-enter data gathered over the period from January 1, 2005, to December 31, 2005, both the Kriging risk models and the Crime Risk Assessment software demonstrated successful application in depicting spatially clustered data consistent with Geography of Crime Geography of Crime research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".