Task-Based Effectiveness of Basic Visualizations

TL;DR

This study evaluates how five basic visualization types perform across various data analysis tasks and attribute types, revealing significant context-dependent effectiveness and providing guidelines for visualization choice.

Contribution

It systematically assesses visualization effectiveness across tasks and data types, and demonstrates how to develop a data-driven visualization recommender system.

Findings

Effectiveness varies significantly by task and data attribute type.

Certain visualizations are better suited for specific tasks.

A decision tree can effectively recommend visualizations based on data and task.

Abstract

Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks and data attribute types. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five visualization types --- Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart --- across ten common data analysis tasks and three data attribute types using two real-world datasets. We found the effectiveness of these visualization types significantly varies across task and data attribute types, suggesting that visualization design would benefit from considering context dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.