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.

中文翻译：

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理清芝加哥 COVID-19 大流行期间社区层面犯罪趋势的变化

最近利用城市级时间序列进行的研究表明，在世界各地，在实施 COVID-19 遏制政策后，一些犯罪活动有所下降。这项工作利用芝加哥社区层面的数据，旨在加深我们对公共干预如何在更精细的空间尺度上影响犯罪活动的理解。该分析依赖于两步方法。首先，它通过结构贝叶斯时间序列估计了芝加哥采取的社会疏远和就地避难政策对四种犯罪类别（即入室盗窃、袭击、毒品相关犯罪和抢劫）的社区因果影响。一旦模型检测到趋势变化的方向、幅度和显着性，Firth 的 Logistic 回归将用于调查与第一步分析中发现的具有统计显着性的犯罪减少相关的因素。统计结果首先表明，犯罪趋势的变化因社区和犯罪类型而异。这表明，除了聚合模型的结果之外，还有一幅以不同模式为特征的复杂图景。其次，回归模型提供了关于与显着犯罪减少相关的相关性的混合结果：一些关系在犯罪之间具有相反的方向，而人口是与显着犯罪减少稳定且正相关的唯一因素。