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ISPRS Int. J. Geo-Inf. 2018, 7(7), 266; https://doi.org/10.3390/ijgi7070266

Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data

1
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287-5302, USA
2
Scientific Computing and Image Insititute, University of Utah, Salt Lake City, UT 84112, USA
*
Author to whom correspondence should be addressed.
Received: 24 May 2018 / Revised: 29 June 2018 / Accepted: 3 July 2018 / Published: 9 July 2018
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Abstract

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. View Full-Text
Keywords: multivariate analysis; association analysis; polar cyclone; climate visualization multivariate analysis; association analysis; polar cyclone; climate visualization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, F.; Li, W.; Wang, S.; Johnson, C.R. Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data. ISPRS Int. J. Geo-Inf. 2018, 7, 266.

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