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

• Published in: ISPRS International Journal of Geo-Information
• Main Authors: Feng Wang, Wenwen Li, Sizhe Wang, Chris R. Johnson
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2018
• Subjects: multivariate analysis; association analysis; polar cyclone; climate visualization; geo; stat; Arctic
• Online Access: https://doi.org/10.3390/ijgi7070266; https://doaj.org/article/a4ed727c90764c9a914c061d39843377

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.