Data-Driven Mark Orientation for Trend Estimation in Scatterplots


A common task for scatterplots is communicating trends in bivariate data. However, the ability of people to visually estimate these trends is under-explored, especially when the data violate assumptions required for common statistical models, or visual trend estimates are in conflict with statistical ones. In such cases, designers may need to intervene and de-bias these estimations, or otherwise inform viewers about differences between statistical and visual trend estimations. We propose data-driven mark orientation as a solution in such cases, where the directionality of marks in the scatterplot guide participants when visual estimation is otherwise unclear or ambiguous. Through a set of laboratory studies, we investigate trend estimation across a variety of data distributions and mark directionalities, and find that data-driven mark orientation can help resolve ambiguities in visual trend estimates.

Tingting Liu
School of Computer Science, Qingdao, Shandong, China
Xiaotong Li
School of Computer Science, Qingdao, Shandong, China
Chen Bao
Shandong University, Qingdao, Shandong, China
Michael Correll
Tableau Software, Seattle, Washington, United States
Changehe Tu
Shandong Univ., Qingdao, China
Oliver Deussen
University of Konstanz, Konstanz, Germany
Yunhai Wang
Shandong University, Qingdao, China




会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Novel Visualization Techniques

[A] Paper Room 09, 2021-05-11 17:00:00~2021-05-11 19:00:00 / [B] Paper Room 09, 2021-05-12 01:00:00~2021-05-12 03:00:00 / [C] Paper Room 09, 2021-05-12 09:00:00~2021-05-12 11:00:00
Paper Room 09
15 件の発表
2021-05-11 17:00:00
2021-05-11 19:00:00