Spatio-Temporal Mixed Membership Models for Criminal Activity
提出一种概率方法研究伦敦犯罪数据，通过分层混合模型分析不同时间和犯罪类别下的空间分布，揭示城市犯罪活动的潜在模式。
Abstract We suggest a probabilistic approach to study crime data in London and highlight the benefits of defining a statistical joint crime distribution model which provides insights into urban criminal activity. This is achieved by developing a hierarchical mixture model for observations, crime occurrences over a geographical study area, that are grouped according to multiple time stamps and crime categories. The mixture components correspond to spatial crime distributions over the study area and the goal is to infer, based on the observations, how and to what degree the latent distributions are shared across the groups.