Crime and individual and neighbourhood sociodemographic characteristics in the City of Toronto
Bibliographic record
Abstract
The overall objective of this study is to determine what neighbourhood and offender-related demographic characteristics impact crime rates in the City of Toronto. By doing so, quantitative and qualitative approaches were implemented in this study. This study includes both property and violent crime datasets from 2014-2016 and census related information from the 2011 Canadian Census. The advancing techniques of Geographical Information System (GIS) has been explored and applied to achieve a thorough understanding of crime occurrences and patterns in the city. Hotspot and Kernel Density mapping were applied to analyze the spatial distribution of crime occurrences and account for spatial autocorrelation. Findings revealed that property and violent crimes across the three years of study showed similar distribution of significant hotspots in the core, Northwest, and East end of the city. An Ordinary Least Square (OLS) regression was conducted to examine the ways in which individual and neighbourhood demographic characteristics predict the effects of crime occurrences. The OLS model was a good predictor for offender-related demographics as opposed to neighbourhood level demographics at the 0.05 significant level. These findings revealed that social disadvantaged neighbourhood characteristics such as low income, unemployment, low education, female lone parent were poor predictors of property crimes but good predictors for violent crimes. However, individual characteristics were.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".