Exploratory spatial data analysis of missing & found persons in Vancouver
Bibliographic record
Abstract
This thesis explored methods that can be used to improve the spatial analysis of crime.The emphasis was on exploratory spatial data analysis (ESDA) in the study of missing and found persons calls for service in Vancouver in 1996.Traditionally, spatial crime analysis has been limited to the cartographic display of clustering or hot spots of criminal events.Research in spatial clustering typically uses limited statistical analysis.This thesis addressed the value of spatial statistics in understanding crime patterns by using global and local methods of analysis on non-standardized and standardized missing and found persons data.Local analysis indicated that clustering exists within both the missing and found persons calls for service.The found persons point data indicate stronger clustering and less dispersion; whereas the missing persons point data illustrate a more dispersed pattern highlighting multiple clusters existing within Vancouver.Findings on a global level indicate that the non-standardized data demonstrate spatial autocorrelation and association within both the missing and found persons datasets; whereas analysis conducted on the standardized data indicate no spatial autocorrelation within the missing persons data.Spatial autocorrelation was present when analysis was conducted on found persons data, and high concentrations (clusters) were present specifically in the downtown east side of Vancouver.iii DEDICATION I dedicate this work to my son, Damyn Thompson, who is the inspiration driving my success in life.Without his love and support I would not be where I am today.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".