时空数据的贝叶斯非参数聚类及其在空气污染中的应用
Bayesian nonparametric clustering for spatio-temporal data, with an application to air pollution
AI总结：
本文回顾了贝叶斯非参数聚类方法在时空数据中的应用，重点介绍空间乘积划分模型，并在意大利北部PM10数据上验证其识别污染模式的能力，为环境风险评估提供见解。
摘要：
空气污染是全球主要的健康危害，其中细颗粒物（PM10）与严重的呼吸系统和心血管疾病相关。因此，分析和聚类时空空气质量数据对于理解污染动态和指导政策干预至关重要。本工作回顾了贝叶斯非参数聚类方法，特别关注其在环境科学中无处不在的时空数据上的应用。我们首先介绍了点参考时空数据的关键建模方法，强调了它们在捕捉复杂空间和时间依赖性方面的灵活性。然后回顾了贝叶斯聚类的最新进展，重点关注将空间结构纳入聚类过程的空间乘积划分模型。我们在意大利北部的PM10监测数据上展示了所提方法，证明了其识别有意义污染模式的能力。本综述突出了贝叶斯非参数方法在环境风险评估中的潜力，并为公共卫生和环境科学中时空聚类的未来研究方向提供了见解。
英文摘要：
Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-temporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work provides a review of Bayesian nonparametric clustering methods, with a particular focus on their application to spatio-temporal data, which are ubiquitous in environmental sciences. We first introduce key modeling approaches for point-referenced spatio-temporal data, highlighting their flexibility in capturing complex spatial and temporal dependencies. We then review recent advancements in Bayesian clustering, focusing on spatial product partition models, which incorporate spatial structure into the clustering process. We illustrate the proposed methods on PM10 monitoring data from Northern Italy, demonstrating their ability to identify meaningful pollution patterns. This review highlights the potential of Bayesian nonparametric methods for environmental risk assessment and offers insights into future research directions in spatio-temporal clustering for public health and environmental science.
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