Work

A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States

Year: 2023

Type: article

Abstract: A new clustering algorithm for spatio-temporal data is developed. The proposed method leverages a weighted combination of a spatial haversine distance matrix and a spectral-density based temporal distance matrix between the locations. Concepts of partition around medoids algorithm and the gap statistic are utilized to develop the algorithm and to determine the optimal number of clusters. Such a non-parametric algorithm is novel as it incorporates both spatial and temporal distances of the units and it can work for time-series of possibly different lengths. Theoretical guarantee of consistency of the proposed method is provided. An elaborate simulation study is also given to demonstrate the efficacy of the algorithm. As an interesting real life application, the proposed algorithm is implemented to analyze the spatio-temporal dynamics of the time series of coronavirus (COVID-19) incidence rates observed at county-level in the United States of America . The results are demonstrated on datasets of different sizes: the entire country, the Midwest region and the state of California. Special emphasis is given on the last two cases to display how the clustering results offer interesting insights into the epidemic progression in these areas. Particularly, it sheds light on whether state-mandated restrictions impacted the entire state similarly or if there are interesting local behaviors in terms of the COVID-19 spread.

Source: Computational Statistics & Data Analysis

DOI: 10.1016/j.csda.2023.107810

Authors: Soudeep Deb, Sayar Karmakar

Institutions: Indian Institute of Management Bangalore, University of Florida

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,340