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.

在本文中，我们提出了一个稳健的模糊聚类模型，即基于 STAR 的模糊 C-Medoids 聚类模型和噪声聚类，根据谷歌提供的工作场所的工作场所流动趋势定义欧洲地区的地域划分 (NUTS2)参考整个 COVID-19 大流行期间。聚类模型通过 STAR 模型的自回归时间和空间系数考虑时间和空间信息。所提出的通过噪声簇的聚类模型能够抵消噪声数据的负面影响。主要实证结果关于社区流动趋势与封锁期之间的预期直接关系，以及相邻区域之间明显的空间交互效应。