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.

Spatial robust fuzzy clustering of COVID 19 time series based on B-splines / D'Urso, P.; De Giovanni, L.; Vitale, V.. - In: SPATIAL STATISTICS. - ISSN 2211-6753. - (2021). [10.1016/j.spasta.2021.100518]

Spatial robust fuzzy clustering of COVID 19 time series based on B-splines

D'Urso P.;Vitale V.
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1567858
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