An Empirical Study on the Relationship Between the Number of Coordinated Views and Visual Analysis

TL;DR

This study empirically investigates how the number of coordinated views in visualization tools affects user analysis processes and outcomes, revealing positive correlations and strategic benefits of multiple views.

Contribution

It introduces a CMV tool with visualization duplication and provides empirical evidence on the impact of view quantity on analysis effectiveness and strategies.

Findings

More views lead to better analytic results.

Visualization duplication encourages creating more views.

Multiple views support diverse analysis strategies.

Abstract

Coordinated Multiple views (CMVs) are a visualization technique that simultaneously presents multiple visualizations in separate but linked views. There are many studies that report the advantages (e.g., usefulness for finding hidden relationships) and disadvantages (e.g., cognitive load) of CMVs. But little empirical work exists on the impact of the number of views on visual anlaysis results and processes, which results in uncertainty in the relationship between the view number and visual anlaysis. In this work, we aim at investigating the relationship between the number of coordinated views and users analytic processes and results. To achieve the goal, we implemented a CMV tool for visual anlaysis. We also provided visualization duplication in the tool to help users easily create a desired number of visualization views on-the-fly. We conducted a between-subject study with 44 participants, where we asked participants to solve five analytic problems using the visual tool. Through quantitative and qualitative analysis, we discovered the positive correlation between the number of views and analytic results. We also found that visualization duplication encourages users to create more views and to take various analysis strategies. Based on the results, we provide implications and limitations of our study.