PCP-Ed : parallel coordinate plots for ensemble data

Abstract

The Parallel Coordinate Plot (PCP) is a complex visual design commonly used for the analysis of high-dimensional data. Increasing data size and complexity may make it challenging to decipher and uncover trends and outliers in a confined space. A dense PCP image resulting from overlapping edges may cause patterns to be covered. We develop techniques aimed at exploring the relationship between data dimensions to uncover trends in dense PCPs. We introduce correlation glyphs in the PCP view to reveal the strength of the correlation between adjacent axis pairs as well as an interactive glyph lens to uncover links between data dimensions by investigating dense areas of edge intersections. We also present a subtraction operator to identify differences between two similar multivariate data sets and relationship-guided dimensionality reduction by collapsing axis pairs. We finally present a case study of our techniques applied to ensemble data and provide feedback from a domain expert in epidemiology.