Spatial Patterns of Immigration and Property Crime in Vancouver: A Spatial Point Pattern Test
Bibliographic record
Abstract
We empirically evaluate the distribution of spatial patterns at the census tract (CT) level for various immigration and property crime measures in Vancouver, British Columbia, 2003 and 2016, using a spatial point pattern test that identifies significant similarities, or otherwise, in the spatial patterns of (a) multiple measures of immigration, (b) various property crime classifications, and (c) immigration and crime patterns together. Results show local-level variations in the spatial concentration of immigration in Vancouver CTs. The use of multiple measures of immigration showed substantive variations of immigrant settlement at the local level. Moreover, results reveal that while immigrant concentration patterns are stable over time and, thus, demonstrate ecological stability, property crime patterns shift from year to year. The spatial analytic approach utilized in this study provides support for the use of local-level spatial models and the multidimensional operationalization of the immigration variable even when their correlations are high. There is heterogeneity among immigrant groups, an important yet often overlooked aspect in assessments of immigration effects on crime.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".