A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
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DOI：
10.1016/j.csda.2023.107810
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发表时间：
2023-08-31
影响因子：
1.8
通讯作者：
Karmakar，Sayar
中科院分区：
文献类型：
--
作者：
Deb，Soudeep;Karmakar，Sayar
提出了一种新的时空数据聚类算法。所提出的方法利用空间半正矢距离矩阵和基于频谱密度的时间距离矩阵的位置之间的加权组合。利用中心点周围划分算法和差距统计量的概念来改进算法和确定最佳聚类数。这种非参数算法是新颖的，因为它结合了空间和时间的距离的单位，它可以工作的时间序列可能不同的长度。为该方法的一致性提供了理论保证。一个精心设计的仿真研究也证明了该算法的有效性。作为一个有趣的真实的生活应用，该算法被实现来分析在美国县级观察到的冠状病毒（COVID-19）发病率的时间序列的时空动态。结果在不同大小的数据集上得到了展示：整个国家、中西部地区和加州州。特别强调的是在最后两个案例中，显示聚类结果如何提供有趣的见解，在这些地区的流行病进展。特别是，它揭示了州强制性的限制是否对整个州产生了类似的影响，或者在COVID-19传播方面是否存在有趣的地方行为。
A new clustering algorithm for spatio-temporal data is developed. The proposed method leverages a weighted combination of a spatial haversine distance matrix and a spectral-density based temporal distance matrix between the locations. Concepts of partition around medoids algorithm and the gap statistic are utilized to develop the algorithm and to determine the optimal number of clusters. Such a non-parametric algorithm is novel as it incorporates both spatial and temporal distances of the units and it can work for time-series of possibly different lengths. Theoretical guarantee of consistency of the proposed method is provided. An elaborate simulation study is also given to demonstrate the efficacy of the algorithm. As an interesting real life application, the proposed algorithm is implemented to analyze the spatio-temporal dynamics of the time series of coronavirus (COVID-19) incidence rates observed at county-level in the United States of America. The results are demonstrated on datasets of different sizes: the entire country, the Midwest region and the state of California. Special emphasis is given on the last two cases to display how the clustering results offer interesting insights into the epidemic progression in these areas. Particularly, it sheds light on whether state-mandated restrictions impacted the entire state similarly or if there are interesting local behaviors in terms of the COVID-19 spread.