DATA QUALITY AND LINKAGES: AN APPLICATION OF CRIME DATA IN THE CΓΓY OF LONDON, ONTARIO
Bibliographic record
Abstract
The aim ofthis thesis is to present analyses ofthe quality of crime related spatial data and the linkages to that data used in research on spatial patterns ofresidential burglaries in London, Ontario. Data of differing quality are often combined for research with little discussion about potential errors in quality, leading to spurious conclusions. The buffer overlay statistics and stochastic methods are employed to establish the quality metrics of positional accuracy, completeness, and miscodings for the data sets. An empirical investigation ofthe data used in mapping residential burglaries is undertaken. A new census dissemination area data set is created to strengthen the linkages ofthe residential burglary locations with the socio-economic census data. Local indicators ofspatial autocorrelation, in the form of crime clusters, are mapped by using local Moran’s I and Getis Ord G; statistics. The findings based on spatial statistical analysis indicate that there is approximately 10 percent variability in significant spatial crime clusters.
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.027 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".