Multivariate Temporal Modeling of Crime with Dynamic Linear Models

• Activity Number: 531 - Urban Analytics: Modeling and Analysis of High Resolution Urban Data
• Type: Invited
• Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
• Sponsor: Social Statistics Section
• Author(s): Jarad Niemi* and Nate Garton
• Companies: Iowa State University and Iowa State University
• Keywords: Bayesian statistics; Time series; Seasonality; Point process; Seemingly unrelated time series equations; FBI Uniform Crime Report

Abstract

Interest in modeling contemporary crime trends, a task that has historically been considered valuable to the public, researchers, and policymakers, is resurging. Advancements in criminology have made it clear that understanding crime trends necessarily involves understanding trends in how likely individuals are to report crimes to the police, as well as how likely the police are to accurately record those crimes. In this paper, we use dynamic linear models to simultaneously model the time series for several crime types in order to gain insight into trends in crime and crime reporting. We analyze crime data from Chicago spanning 2007 through 2016 and show how correlations in the way crime trends evolve may contain information about drivers of crime and crime reporting. We provide evidence of substantial differences in the relationships between the trends of crimes of different types depending on whether crimes are violent or nonviolent and whether or not crimes are tracked in the FBI’s Uniform Crime Report.