Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space–time autoregressive models
| Autor: | Massimo Mucciardi, Pierpaolo D'Urso, Edoardo Otranto, Vincenzina Vitale |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Fuzzy clustering COVID-19 outbreak Fuzzy C-medoids clustering Google COVID-19 community mobility report NUTS 2 Robust clustering STAR model Computer science Management Monitoring Policy and Law Star (graph theory) computer.software_genre Fuzzy logic Medoid Autoregressive model Data mining Computers in Earth Sciences Cluster analysis Spatial analysis computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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