Shape It Up: An Empirically Grounded Approach for Designing Shape Palettes
Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri, Danielle Albers, Szafir

TL;DR
This paper empirically evaluates 39 shapes for use in multi-class scatterplot palettes, developing a model and tool to optimize shape selection based on perceptual efficiency and task performance.
Contribution
It introduces a data-driven model and design tool for selecting effective shape palettes in visualizations, addressing the lack of general guidelines and the complexity of shape perception.
Findings
Shape effectiveness varies significantly across pairs.
Certain shapes improve perceptual accuracy and efficiency.
Classical shape features do not predict perceptual performance.
Abstract
Shape is commonly used to distinguish between categories in multi-class scatterplots. However, existing guidelines for choosing effective shape palettes rely largely on intuition and do not consider how these needs may change as the number of categories increases. Although shapes can be a finite number compared to colors, they can not be represented by a numerical space, making it difficult to propose a general guideline for shape choices or shed light on the design heuristics of designer-crafted shape palettes. This paper presents a series of four experiments evaluating the efficiency of 39 shapes across three tasks -- relative mean judgment tasks, expert choices, and data correlation estimation. Given how complex and tangled results are, rather than relying on conventional features for modeling, we built a model and introduced a corresponding design tool that offers recommendations…
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Taxonomy
TopicsDesign Education and Practice
