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

TL;DR

This paper investigates how data properties and design choices in part-to-whole visualizations like pie charts influence perceptual mechanisms such as anchoring and alignment, affecting estimation accuracy.

Contribution

It provides empirical insights into how value salience and alignment impact perception in part-to-whole charts, informing better visualization design.

Findings

Salient values improve estimation accuracy.

Alignment to scale positions influences perceptual anchoring.

Design considerations affect task performance in data interpretation.

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.