基于非平稳时空点过程的亚特兰大枪支暴力建模
Atlanta Gun Violence Modeling via Nonstationary Spatio-temporal Point Processes
AI总结：
针对现有枪支暴力Hawkes模型平稳核函数的局限，研究提出带神经网络非平稳核的时空点过程模型，基于亚特兰大1.6万起枪击事件验证，兼具良好预测性与可解释性。
摘要：
美国的枪支暴力分析已采用多种基于时空点过程的模型。既往研究已发现枪支暴力存在传染效应，其特征是在城市环境中出现扩散式爆发，这种效应可通过自激时空Hawkes过程（霍克斯过程）有效表征。Hawkes过程及其变体已成功应用于建模自激事件，包括地震、疾病暴发、金融市场波动、神经活动以及社交网络中模因的病毒式传播。然而，现有应用于枪支暴力的Hawkes模型通常依赖简单的平稳核函数，无法解释影响力在时空上复杂、非均匀的传播与影响。为解决这一局限，我们采用一种非平稳时空点过程模型，该模型融合了基于神经网络的核函数，以更好地刻画枪支暴力事件之间的多样关联。本研究分析了2021年至2023年亚特兰大都会区约16000起枪击事件的综合数据集。我们方法的核心是创新性的非平稳核函数，其设计旨在提升模型表达能力的同时保留可解释性。该方法不仅展现出优异的预测性能，还为枪支暴力的时空动力学及其在城市环境中的传播机制提供了见解。
英文摘要：
Analysis of gun violence in the United States has utilized various models based on spatiotemporal point processes. Previous studies have identified a contagion effect in gun violence, characterized by bursts of diffusion across urban environments, which can be effectively represented using the self-excitatory spatiotemporal Hawkes process. The Hawkes process and its variants have been successful in modeling self-excitatory events, including earthquakes, disease outbreaks, financial market movements, neural activity, and the viral spread of memes on social networks. However, existing Hawkes models applied to gun violence often rely on simplistic stationary kernels, which fail to account for the complex, non-homogeneous spread of influence and impact over space and time. To address this limitation, we adopt a non-stationary spatiotemporal point process model that incorporates a neural network-based kernel to better represent the varied correlations among events of gun violence. Our study analyzes a comprehensive dataset of approximately 16,000 gunshot events in the Atlanta metropolitan area from 2021 to 2023. The cornerstone of our approach is the innovative non-stationary kernel, designed to enhance the model's expressiveness while preserving its interpretability. This approach not only demonstrates strong predictive performance but also provides insights into the spatiotemporal dynamics of gun violence and its propagation within urban settings.
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