Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space-time autoregressive models.
Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space-time autoregressive models.
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DOI：
10.1016/j.spasta.2021.100531
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发表时间：
2022-06
影响因子：
2.3
通讯作者：
Vitale, Vincenzina
中科院分区：
文献类型：
--
作者：
D'Urso, Pierpaolo;Mucciardi, Massimo;Otranto, Edoardo;Vitale, Vincenzina
关键词：
本文提出了一种鲁棒模糊聚类模型，即基于star的带有噪声聚类的模糊c - mediids聚类模型，根据谷歌提供的整个COVID-19大流行时期工作场所的工作场所流动趋势，定义欧洲地区的领土划分（NUTS2）。聚类模型利用STAR模型的时空系数自回归，同时考虑了时空信息。提出的通过噪声聚类的聚类模型能够抵消噪声数据的负面影响。主要的实证结果是社区流动性趋势与封锁期之间存在预期的直接关系，相邻区域之间存在明显的空间互动效应。
In this paper we propose a robust fuzzy clustering model, the STAR-based Fuzzy C-Medoids Clustering model with Noise Cluster, to define territorial partitions of the European regions (NUTS2) according to the workplaces mobility trends for places of work provided by Google with reference to the whole COVID-19 pandemic period. The clustering model takes into account both temporal and spatial information by means of the autoregressive temporal and spatial coefficients of the STAR model. The proposed clustering model through the noise cluster is capable of neutralizing the negative effects of noisy data. The main empirical results regard the expected direct relationship between the Community mobility trend and the lockdown periods, and a clear spatial interaction effect among neighboring regions.
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影响因子：
6.2
作者：
MCBRATNEY, AB;MOORE, AW
通讯作者：
MOORE, AW
影响因子：
4.1
作者：
D'Urso, Pierpaolo;Di Lallo, Dario;Maharaj, Elizabeth Ann
通讯作者：
Maharaj, Elizabeth Ann
影响因子：
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作者：
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