Analyzing crime events is crucial to understand crime dynamics and it is largely helpful for constructing prevention policies. Point processes specified on linear networks can provide a more accurate description of crime incidents by considering the geometry of the city. We propose a spatio-temporal Dirichlet process mixture model on a linear network to analyze crime events in Valencia, Spain. We propose a Bayesian hierarchical model with a Dirichlet process prior to automatically detect space-time clusters of the events and adopt a convolution kernel estimator to account for the network structure in the city. From the fitted model, we provide crime hotspot visualizations that can inform social interventions to prevent crime incidents. Furthermore, we study the relationships between the detected cluster centers and the city's amenities, which provides an intuitive explanation of criminal contagion.

翻译：分析犯罪事件对于理解犯罪动态至关重要，且对构建预防政策具有重要帮助。在线性网络上指定的点过程通过考虑城市几何结构，能够更精确地描述犯罪事件。本文提出一种线性网络上的时空狄利克雷过程混合模型，用于分析西班牙瓦伦西亚的犯罪事件。我们构建了一个采用狄利克雷过程先验的贝叶斯分层模型，以自动检测事件的时空聚类，并采用卷积核估计器来考虑城市中的网络结构。基于拟合模型，我们提供了犯罪热点可视化结果，可为预防犯罪事件的社会干预措施提供信息。此外，我们研究了检测到的聚类中心与城市公共设施之间的关系，这为犯罪传染现象提供了直观解释。