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

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  • Deb, Soudeep
  • Karmakar, Sayar

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.

Suggested Citation

  • Deb, Soudeep & Karmakar, Sayar, 2023. "A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:csdana:v:188:y:2023:i:c:s0167947323001214
    DOI: 10.1016/j.csda.2023.107810
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    1. Brian Neelon & Alan E. Gelfand & Marie Lynn Miranda, 2014. "A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(5), pages 737-761, November.
    2. Xiaoping Jin & Bradley P. Carlin & Sudipto Banerjee, 2005. "Generalized Hierarchical Multivariate CAR Models for Areal Data," Biometrics, The International Biometric Society, vol. 61(4), pages 950-961, December.
    3. Liu, Xueli & Yang, Mark C.K., 2009. "Simultaneous curve registration and clustering for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1361-1376, February.
    4. Liu, Weidong & Wu, Wei Biao, 2010. "Asymptotics Of Spectral Density Estimates," Econometric Theory, Cambridge University Press, vol. 26(4), pages 1218-1245, August.
    5. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
    6. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    7. Mingjin Yan & Keying Ye, 2007. "Determining the Number of Clusters Using the Weighted Gap Statistic," Biometrics, The International Biometric Society, vol. 63(4), pages 1031-1037, December.
    8. Fruhwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2008. "Model-Based Clustering of Multiple Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 78-89, January.
    9. Tanupriya Choudhury & Rohini Arunachalam & Abhirup Khanna & Elzbieta Jasinska & Vadim Bolshev & Vladimir Panchenko & Zbigniew Leonowicz, 2022. "A Social Network Analysis Approach to COVID-19 Community Detection Techniques," IJERPH, MDPI, vol. 19(7), pages 1-14, March.
    10. Xiansheng Chen & Ruisong Quan, 2021. "A spatiotemporal analysis of urban resilience to the COVID-19 pandemic in the Yangtze River Delta," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 829-854, March.
    11. Carmen Fernández & Peter J. Green, 2002. "Modelling spatially correlated data via mixtures: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 805-826, October.
    12. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    13. Lijiang Geng & Guanyu Hu, 2022. "Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data," Biometrics, The International Biometric Society, vol. 78(2), pages 536-547, June.
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