Predicting Short Time-to-Crime Guns: a Machine Learning Analysis of California Transaction Records (2010-2021).

ABSTRACT: Gun-related crime continues to be an urgent public health and safety problem in cities across the US. A key question is: how are firearms diverted from the legal retail market into the hands of gun offenders? With close to 8 million legal firearm transaction records in California (2010-2020) linked to over 380,000 records of recovered crime guns (2010-2021), we employ supervised machine learning to predict which firearms are used in crimes shortly after purchase. Specifically, using random forest (RF) with stratified under-sampling, we predict any crime gun recovery within a year (0.2% of transactions) and violent crime gun recovery within a year (0.03% of transactions). We also identify the purchaser, firearm, and dealer characteristics most predictive of this short time-to-crime gun reco

SUBMITTER: Laqueur HS 

PROVIDER: S-EPMC11461422 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature