D'Urso Pierpaolo, De Giovanni Livia, Vitale Vincenzina
Department of Social end Economic Sciences, Sapienza University of Rome, P.za Aldo Moro, 5 00185 Rome, Italy.
Department of Political Sciences Politiche, LUISS university, Viale Romania, 32 00197 Rome, Italy.
Spat Stat. 2022 Jun;49:100518. doi: 10.1016/j.spasta.2021.100518. Epub 2021 May 15.
The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects.
这项工作的目的是根据与新冠疫情相关的主要变量，为意大利的20个地区确定一种聚类结构。数据是随时间观测的，时间跨度从2020年2月的最后一周到2021年2月的第一周。针对在多个时间点观测到的地理单元，所提出的模糊聚类模型嵌入了空间和时间信息。具体而言，已提出一种基于指数距离的带空间惩罚项的围绕中心点的模糊划分算法，用于对时间轨迹的样条表示进行分类。结果表明，地区间的异质性以及空间邻接性对于理解疫情传播和设计有效政策以减轻影响至关重要。
