Data-Driven Mark Orientation for Trend Estimation in Scatterplots

Description: 

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.

Authors: 
Tingting Liu
Xiaotong Li
Chen Bao
Michael Correll
Changhe Tu
Oliver Deussen
Yunhai Wang
Publication Date: 
Saturday, May 8, 2021
Publication Information: 
CHI 2021, May 8–13, 2020, Yokohama