37 citations · 81 across the 17 of their papers we have counts for
9 papers · 1 filter
Striking the Right Balance: Systematic Assessment of Evaluation Method Distribution Across Contribution Types
Feng Lin, Arran Zeyu Wang, Md Dilshadur Rahman +2
In the rapidly evolving field of information visualization, rigorous evaluation is essential for validating new techniques, understanding user interactions, and demonstrating the e…
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…
Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis
Arran Zeyu Wang, David Borland, David Gotz
Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualiza…
Causal Priors and Their Influence on Judgements of Causality in Visualized Data
Arran Zeyu Wang, David Borland, Tabitha C. Peck +2
"Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlati…
Our Stories, Our Data: Co-designing Visualizations with People with Intellectual and Developmental Disabilities
Keke Wu, Ghulam Jilani Quadri, Arran Zeyu Wang +4
Individuals with Intellectual and Developmental Disabilities (IDD) have unique needs and challenges when working with data. While visualization aims to make data more accessible to…
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…
Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension
Ghulam Jilani Quadri, Arran Zeyu Wang, Zhehao Wang +3
Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and…
An Empirical Study of Counterfactual Visualization to Support Visual Causal Inference
Arran Zeyu Wang, David Borland, David Gotz
Counterfactuals -- expressing what might have been true under different circumstances -- have been widely applied in statistics and machine learning to help understand causal relat…
Using Counterfactuals to Improve Causal Inferences from Visualizations
David Borland, Arran Zeyu Wang, David Gotz
Traditional approaches to data visualization have often focused on comparing different subsets of data, and this is reflected in the many techniques developed and evaluated over th…