Data-Induced Groupings and How To Find Them

TL;DR

This paper investigates how data values influence perceived groupings in visualizations like dot plots, revealing that users often rely on data-induced groupings which can mislead interpretation, and proposes a model to predict such perceptions.

Contribution

It introduces a model to predict user-perceived groupings in dot plots, addressing the under-explored interaction between data values and visualization design.

Findings

Users rely on data-induced groupings despite irrelevance in nominal data.

A predictive model can identify perceived groupings in dot plots.

Design interventions can be guided by the model to improve visualization clarity.

Abstract

Making sense of a visualization requires the reader to consider both the visualization design and the underlying data values. Existing work in the visualization community has largely considered affordances driven by visualization design elements, such as color or chart type, but how visual design interacts with data values to impact interpretation and reasoning has remained under-explored. Dot plots and bar graphs are commonly used to help users identify groups of points that form trends and clusters, but are liable to manifest groupings that are artifacts of spatial arrangement rather than inherent patterns in the data itself. These ``Data-induced Groups'' can drive suboptimal data comparisons and potentially lead the user to incorrect conclusions. We conduct two user studies using dot plots as a case study to understand the prevalence of data-induced groupings. We find that users rely on data-induced groupings in both conditions despite the fact that trend-based groupings are irrelevant in nominal data. Based on the study results, we build a model to predict whether users are likely to perceive a given set of dot plot points as a group. We discuss two use cases illustrating how the model can assist visualization designers by both diagnosing potential user-perceived groupings in dot plots and offering redesigns that better accentuate desired groupings through data rearrangement.