Principal Components and Assortativity-based Assessment of the Similarity of Crime Metrics across Coterminous Wards in the City of Chicago
Bibliographic record
Abstract
The City of Chicago (with 50 wards) has one of the highest crime rates in the US. We seek to quantitatively assess the similarity of crime metrics across coterminous wards using a combination of Principal Component Analysis (PCA) and Assortativity analysis. We first build a ward network (nodes are the wards and edges connect coterminous wards) of the city using the ward map. We parse through the 2022 crime dataset for the city and build a matrix whose entries correspond to the number of occurrences of a crime type in a ward. We conduct PCA of this ward-crime type matrix and determine a weighted average PC_crime_score (using the entries in the high-variance principal components and their variances as weights) for each ward. We observe the coterminous wards to exhibit moderate-strong assortativity with respect to three different crime metrics: hotspot classification, PC_crime_scores and the crime counts of the individual crime types.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".