在芝加哥COVID-19大流行期间解开社区层面的犯罪趋势变化
Disentangling Community-level Changes in Crime Trends During the COVID-19 Pandemic in Chicago
摘要：
最近利用城市一级时间序列进行的研究表明，在全球范围内，COVID-19遏制政策实施后，几起犯罪案件有所下降。利用芝加哥社区一级的数据，这项工作旨在促进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。分析采用两步走的方法。首先，它通过结构贝叶斯时间序列估计了芝加哥通过四种犯罪类别（即，入室盗窃、袭击、与毒品有关的犯罪和抢劫）。一旦模型检测到趋势变化的方向，幅度和重要性，Firth的逻辑回归用于调查与分析第一步中发现的统计上显着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，除了总量模型的结果之外，还有一个以不同模式为特征的复杂情况。第二，回归模型提供了混合的调查结果与显着减少犯罪的相关性：几个关系有相反的方向与人口的犯罪是唯一的因素，是稳定和积极的显着减少犯罪。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
