2017/09/25 by Bahador Saket, Saket, Bahador, Alex Endert +4 · 1 voice
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Image and Video Quality Assessment #cs.HC
paper · pdf · doi:10.48550/arxiv.1709.08546
openalex publication_date 2017/09/25 · arxiv published 2017/09/25 · arxiv updated 2018/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.