This repository hosts the code and data used to perform the analyses of the paper entitled "Disentangling Community-level Changes in Crime Trends During the COVID-19 Pandemic in Chicago", written by me (Gian Maria Campedelli), Serena Favarin, Alberto Aziani and Alex R. Piquero. Link to the paper (published open access in "Crime Science"): https://crimesciencejournal.biomedcentral.com/articles/10.1186/s40163-020-00131-8
Recent studies exploiting city-level time series have shown that, around the world, several crimes declined after COVID-19 containment policies have been put in place. Using data at the community-level in Chicago, this work aims to advance our understanding on how public interventions affected criminal activities at a finer spatial scale. The analysis relies on a two-step methodology. First, it estimates the community-wise causal impact of social distancing and shelter-in-place policies adopted in Chicago via Structural Bayesian Time-Series across four crime categories (i.e., burglary, assault, narcotics-related offenses, and robbery). Once the models detected the direction, magnitude and significance of the trend changes, Firth’s Logistic Regression is used to investigate the factors associated to the statistically significant crime reduction found in the first step of the analyses. Statistical results first show that changes in crime trends differ across communities and crime types. This suggests that beyond the results of aggregate models lies a complex picture characterized by diverging patterns. Second, regression models provide mixed findings regarding the correlates associated with significant crime reduction: several relations have opposite directions across crimes with population being the only factor that is stably and positively associated with significant crime reduction.
To investigate the impact of COVID-19 on crime, we focused on the city of Chicago and specifically on four crime categories, considering daily time series. The methodology is two-fold: we first investigate the causal impact (RCE) of the Covid-19 containment policies on these crimes by employing Structural Bayesian Time Series models (via CausalImpact) at the community level. Second, we assess what factors predict significant reductions in crime, addressing four conceptual dimensions: crime-related, socio-economic, health-demographic and joint dynamics (parallel reductions of more than one crime).