The Pattern is in the Details: An Evaluation of Interaction Techniques for Locating, Searching, and Contextualizing Details in Multivariate Matrix Visualizations

TL;DR

This paper empirically compares three interaction techniques—focus+context, pan&zoom, and overview+detail—for exploring multivariate matrix visualizations, revealing the fisheye lens's superior performance in locating details and pan&zoom's speed advantage.

Contribution

It provides the first empirical evaluation comparing key interaction techniques for multivariate matrix visualizations, highlighting the fisheye lens's effectiveness and pan&zoom's efficiency.

Findings

Fisheye lens outperforms other focus+context techniques.

Pan&zoom is faster for locating and searching details.

Pan&zoom matches overview+detail in contextualizing details.

Abstract

Matrix visualizations are widely used to display large-scale network, tabular, set, or sequential data. They typically only encode a single value per cell, e.g., through color. However, this can greatly limit the visualizations' utility when exploring multivariate data, where each cell represents a data point with multiple values (referred to as details). Three well-established interaction approaches can be applicable in multivariate matrix visualizations (or MMV): focus+context, pan&zoom, and overview+detail. However, there is little empirical knowledge of how these approaches compare in exploring MMV. We report on two studies comparing them for locating, searching, and contextualizing details in MMV. We first compared four focus+context techniques and found that the fisheye lens overall outperformed the others. We then compared the fisheye lens, to pan&zoom and overview+detail. We found that pan&zoom was faster in locating and searching details, and as good as overview+detail in contextualizing details.