This work investigates whether and how COVID-19 containment policies had an immediate impact on crime trends in Los Angeles. The analysis is conducted using Bayesian structural time-series and focuses on nine crime categories and on the overall crime count, daily monitored from January 1st 2017 to March 28th 2020. We concentrate on two post-intervention time windows—from March 4th to March 16th and from March 4th to March 28th 2020—to dynamically assess the short-term effects of mild and strict policies. In Los Angeles, overall crime has significantly decreased, as well as robbery, shoplifting, theft, and battery. No significant effect has been detected for vehicle theft, burglary, assault with a deadly weapon, intimate partner assault, and homicide. Results suggest that, in the first weeks after the interventions are put in place, social distancing impacts more directly on instrumental and less serious crimes. Policy implications are also discussed.

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.

The global spread of 2019-nCoV, a new virus belonging to the coronavirus family, forced national and local governments to apply different sets of measures aimed at containing the outbreak. Los Angeles has been one of the first cities in the United States to declare the state of emergency on March 4 th , progressively issuing stronger policies involving (among the others) social distancing, the prohibition of crowded private and public gatherings and closure of leisure premises. These interventions highly disrupt and modify daily activities and habits, urban mobility and micro-level interactions between citizens. One of the many social phenomena that could be influenced by such measures is crime. Exploiting public data on crime in Los Angeles, and relying on routine activity and pattern theories of crime, this work investigates whether and how new coronavirus containment policies have an impact on crime trends in a metropolis. The article specifically focuses on eight urban crime categories, daily monitored from January 1 st 2017 to March 16 th 2020. The analyses will be updated bi-weekly to dynamically assess the shortand medium-term effects of these interventions to shed light on how crime adapts to such structural modification of the environment. Finally, policy implications are also discussed.

The dense distribution of crime in a small number of micro places led to the formulation of a law of crime concentration applicable across cities and stable over time. This law has rarely been tested in Europe and has never been tested in Italy. In addition, there is a lack of extensive knowledge about its determinants. Therefore, the main objectives of this study are to test the presence and the stability of crime concentration in a different urban context and to explain this concentration. A street segment analysis and a group-based trajectory analysis were conducted to test the presence and the stability of crime concentration in the city of Milan (Italy), and negative binomial regression models were run to understand the main determinants of this concentration. The findings confirm the presence of crime concentration at street segment level, but only a few segments can be considered to be highly criminogenic over time. Social disorganization factors play an important role in explaining crime concentration, even though opportunity factors also coincide in this explanation. Despite their differences, cities around the world share the same crime concentration. The generalization of these findings is an important step in the development of common knowledge. Nevertheless, in Milan only a few segments are chronic hot spots. The stability pattern in the city needs to be further analysed using different methods. A theoretical integration approach considering both situational and social disorganization factors is promising in understanding why crime occurs in urban areas.

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This article is a further elaboration of the results of a study conducted by the authors as part of the project BlockWaste "Blocking the Loopholes for Illicit Waste Trafficking", co-funded by the Internal Security Fund of the European Union.