Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings

Abstract

Visual analytics is often based on the intuition that highly interactive and dynamic depictions of complex and multivariate databases amplify human capabilities for inference and decision-making, as they facilitate cognitive tasks such as pattern recognition, association, and analytical reasoning (Thomas and Cook 2005). But how do we know whether visual analytics really works? This article offers a generic evaluation approach combining theory- and data-driven methods based on sequence similarity analysis. The approach systematically studies users' visual interaction strategies when using highly interactive interfaces. We specifically ask whether the efficiency (i.e., speed) of users can be characterized by specific display interaction event sequences, and whether studying user strategies could be employed to improve the (interaction) design of the dynamic displays. We showcase our approach using a very large, fine-grained spatiotemporal dataset of eye movement recordings collected during a controlled human subject experiment with dynamic visual analytics displays. With this methodological approach based on empirical evidence, we hope to contribute to a deeper understanding of how people make inferences and decisions with highly interactive visualization tools and complex displays.

Keywords:

• visual analytics strategies
• eye tracking
• sequence analysis
• map use
• efficiency

Acknowledgments

This study was partially funded by the Swiss National Fund (award numbers 200021_120434/1: Çöltekin and 200021-113745: Fabrikant) and the US Fulbright – Swiss Scholarship Program. The authors thank Benedikt Heil and Simone Garlandini for their substantial contribution in the initial stages of this project. The authors express their gratitude to Andreas Neumann of carto.net and John P. Donnelly of natlas.gov for answering their questions regarding the design considerations of the two stimuli used in this experiment. They are grateful for the helpful comments from four anonymous reviewers who provided feedback that improved the article.

Notes

1. PoP Analyst is licensed under LGPL, that is, GNU lesser general public license, (GNU 2010) and can be downloaded at http://popanalyst.dynalias.org. Please cite this article if you use the software.