IEEE VIS 2025 Content: Anchoring and Alignment: Data Factors in Part-to-Whole Visualization

Anchoring and Alignment: Data Factors in Part-to-Whole Visualization

Connor Bailey -

Michael Gleicher -

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This paper demonstrates the effects of data and design factors on perceptual mechanisms, and the resulting effects on task performance. Our work has three types of ramifications. First, in terms of visualization design, it provides evidence to support a nuanced approach to making design decisions based on data properties. Therefore, our paper should be of interest to chart designers. Second, it shows the need to consider data and design factors and perceptual mechanisms in designing visualization experiments. This should be of interest to data visualization researchers. Third, it contributes to our understanding of the mechanisms of how basic charts are perceived by viewers, showing how understanding of anchoring, and alignment relate to the large variations across different conditions within chart types. This should make our paper applicable in data visualization, psychology, and other areas in which the understanding of perceptual mechanisms is important.
Keywords

part-to-whole, estimation, graphical perception, anchoring, alignment, rounding, perceptual mechanisms.

Abstract

We explore the effects of data and design considerations through the example case of part-to-whole data relationships. Standard part-to-whole representations like pie charts and stacked bar charts make the relationships of parts to the whole explicit. Value estimation in these charts benefits from two perceptual mechanisms: anchoring, where the value is close to a reference value with an easily recognized shape, and alignment where the beginning or end of the shape is aligned with a marker. In an online study, we explore how data and design factors such as value, position, and encoding together impact these effects in making estimations in part-to-whole charts. The results show how salient values and alignment to positions on a scale affect task performance. This demonstrates the need for informed visualization design based around how data properties and design factors affect perceptual mechanisms.