Exploring High-D Spaces with Multiform Matrices and Small Multiples

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely t...

• Type: Conference
• Year: 2003
• Where: INFOVIS
• Authors: Alan M. MacEachren, Xiping Dai, Frank Hardisty, Diansheng Guo, Eugene Lengerich
• Keywords: Bivariate, INFOVIS 2003, Multivariate Data, Small Multiple