论文标题
推断分析区域中的隐藏潜力：发现麦德林的犯罪嫌疑人社区
Inferring hidden potentials in analytical regions: uncovering crime suspect communities in Medellín
论文作者
论文摘要
本文提出了一种使用报告的统计数据对分析区域中隐藏人群的大小进行推断的贝叶斯方法。为此，我们建议在面板数据结构中考虑单方面的误差组件和空间效应。我们的模拟练习表明样本表现良好。我们分析了梅德林（哥伦比亚）与四个犯罪活动相关的犯罪嫌疑人的犯罪嫌疑人。我们的提议似乎确定了热点或“犯罪社区”，潜在的社区，在报道不足的社区更严重，也是犯罪学校的驱动因素。统计证据表明，一方面凶杀与毒品交易之间存在高度的相互作用，另一方面，摩托车和汽车盗窃。
This paper proposes a Bayesian approach to perform inference regarding the size of hidden populations at analytical region using reported statistics. To do so, we propose a specification taking into account one-sided error components and spatial effects within a panel data structure. Our simulation exercises suggest good finite sample performance. We analyze rates of crime suspects living per neighborhood in Medellín (Colombia) associated with four crime activities. Our proposal seems to identify hot spots or "crime communities", potential neighborhoods where under-reporting is more severe, and also drivers of crime schools. Statistical evidence suggests a high level of interaction between homicides and drug dealing in one hand, and motorcycle and car thefts on the other hand.
