On the Measurement and Analysis of Safety in a Large City
Bibliographic record
Abstract
Sharing city's operational data with public is a recent trend that can help in identifying deficiencies and bringing improvements in city operations. Analyzing such kinds of data can have a strong impact on problem solving as it connects public and private sectors with their city. Los Angeles (LA) is a good example of the ongoing index on the US open datasets. Since last several years, LA authorities worked hard to engage with its residents by launching several datasets, these datasets were represented by dashboards showing the progress of the city's performance and services. Any resident can review and assess progress of the city authorities in many aspects (e.g. building information, safety, water, streets conditions). Since open datasets can provide us with the ability to analyze and discover their values, we decided to analyze a dataset on crime statistics in Los Angeles. In this paper, we present a comprehensive analysis for the LA crime data from 2010 to present. This dataset was created by the Los Angeles Police Department (LAPD) and it is updated on a regular basis. The dataset contains approximately 1.5 million records, where each record represents a crime incident. We analyze multiple features including the activity of crimes (i.e. number of crimes) in terms of year, month, weekdays, time of the day, area, victim sex, victim age and victim descent. In addition, we analyze the reporting period of a crime incident by calculating the average reporting days (i.e. number of days the victim took to report a crime incident) in terms of multiple factors. Our analysis uncovers the unique characteristics and insights of safety measures and crime prevention in the city.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".