Association rules-based multivariate analysis and visualization of spatiotemporal climate data

Feng Wang, Wenwen Li, Sizhe Wang, Chris R. Johnson

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest. Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics. The Arctic System Reanalysis (ASR) data are used to demonstrate and validate the effectiveness and usefulness of our method through a case study of “The Great Arctic Cyclone of 2012”. The results show that different variables have strong associations near the polar cyclone area. This work also provides techniques for identifying multivariate correlation and for better understanding the driving factors of climate phenomena.

Original languageEnglish (US)
Article number266
JournalISPRS International Journal of Geo-Information
Issue number7
StatePublished - Jul 2018


  • Association analysis
  • Climate visualization
  • Multivariate analysis
  • Polar cyclone

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)


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