6 papers · 1 filter
CatPAL: Task-Aware Learning for Categorical Palette Recommendation
Chin Tseng, Arran Zeyu Wang, Yunqi Li +1
Designing effective categorical palettes requires balancing a range of factors, including perceptual distinctiveness, category count, and task effectiveness. The effectiveness of c…
Redundant is Not Redundant: Automating Efficient Categorical Palette Design Unifying Color & Shape Encodings with CatPAW
Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri +1
Colors and shapes are commonly used to encode categories in multi-class scatterplots. Designers often combine the two channels to create redundant encodings, aiming to enhance clas…
Graphical Perception of Icon Arrays versus Bar Charts for Value Comparisons in Health Risk Communication
Jade Kandel, Jiayi Liu, Arran Zeyu Wang +2
Visualizations support critical decision making in domains like health risk communication. This is particularly important for those at higher health risks and their care providers,…
Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Arran Zeyu Wang, Ghulam Jilani Quadri, Mengyuan Zhu +2
Understanding how people perceive visualizations is crucial for designing effective visual data representations; however, many heuristic design guidelines are derived from specific…
Shape It Up: An Empirically Grounded Approach for Designing Shape Palettes
Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri +1
Shape is commonly used to distinguish between categories in multi-class scatterplots. However, existing guidelines for choosing effective shape palettes rely largely on intuition a…
Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri +1
Existing guidelines for categorical color selection are heuristic, often grounded in intuition rather than empirical studies of readers' abilities. While design conventions recomme…