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Spatial quantile clustering of climate data

Author

Listed:
  • Carlo Gaetan

    (Università Ca’ Foscari)

  • Paolo Girardi

    (Università Ca’ Foscari)

  • Victor Muthama Musau

    (Kirinyaga University)

Abstract

In the era of climate change, the distribution of climate variables evolves with changes not limited to the mean value. Consequently, clustering algorithms based on central tendency could produce misleading results when used to summarize spatial and/or temporal patterns. We present a novel approach to spatial clustering of time series based on quantiles using a Bayesian framework that incorporates a spatial dependence layer based on a Markov random field. A series of simulations tested the proposal, then applied to the sea surface temperature of the Mediterranean Sea, one of the first seas to be affected by the effects of climate change.

Suggested Citation

  • Carlo Gaetan & Paolo Girardi & Victor Muthama Musau, 2025. "Spatial quantile clustering of climate data," 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. 19(1), pages 147-175, March.
    • Handle: RePEc:spr:advdac:v:19:y:2025:i:1:d:10.1007_s11634-024-00580-y
    DOI: 10.1007/s11634-024-00580-y
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    References listed on IDEAS

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