Extending the Heatmap Matrix: Pairwise Analysis of Multivariate Categorical Data

Analysts are often interested in understanding the association between variables within a dataset. This paper describes a set of techniques for augmenting the Heatmap Matrix, which represents pairwise intersections of categorical variables. The proposed extensions include adapting the design and layout of the matrix to enhance its readability, expanding the number of metrics that can be presented, displaying matching records in a coordinated table view, and embedding the Chi-square test of independence. These features are demonstrated on two datasets using the empirical prototype that has been developed.