Measuring Categorical Perception in Color-Coded Scatterplots

Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how the number of categories and choice of color encodings used in multiclass scatterplots influences the viewers' abilities to analyze data across classes. Participants estimated relative means in a series of scatterplots with 2 to 10 categories encoded using ten color palettes drawn from popular design tools. Our results show that the number of categories and color discriminability within a color palette notably impact people's perception of categorical data in scatterplots and that the judgments become harder as the number of categories grows. We examine existing palette design heuristics in light of our results to help designers make robust color choices informed by the parameters of their data.Comment: The paper has been accepted to the ACM CHI 2023. 14 pages, 7 figure

Out of the plane: Flower vs. star glyphs to support high-dimensional exploration in two-dimensional embeddings

Exploring high-dimensional data is a common task in many scientific disciplines. To address this task, two-dimensional embeddings, such as tSNE and UMAP, are widely used. While these determine the 2D position of data items, effectively encoding the first two dimensions, suitable visual encodings can be employed to communicate higher-dimensional features. To investigate such encodings, we have evaluated two commonly used glyph types, namely flower glyphs and star glyphs. To evaluate their capabilities for communicating higher-dimensional features in two-dimensional embeddings, we ran a large set of crowd-sourced user studies using real-world data obtained from data.gov. During these studies, participants completed a broad set of relevant tasks derived from related research. This paper describes the evaluated glyph designs, details our tasks, and the quantitative study setup before discussing the results. Finally, we will present insights and provide guidance on the choice of glyph encodings when exploring high-dimensional data.Peer ReviewedPostprint (published version

A Multi-Faceted Approach for Evaluating Visualization Recommendation Algorithms

Data visualizations allow analysts to quickly understand data trends, outliers, and patterns. However, designing the "best" visualizations for a given dataset is complicated. Multiple factors need to be considered, such as the data size, data types, target analysis tasks being supported, and even how the visualization needs to be personalized to the audience. In response, many visualization recommendation algorithms are being proposed to reduce user effort by automatically making some or all of these design decisions for analysts. However, existing visualization recommendation algorithms are evaluated in isolation, or the comparisons do not measure user performance. In other words, existing algorithms are not tested in a way that aligns with how they are used in practice. The lack of evaluation approaches makes it impossible to know how functional an algorithm is compared to another across various analysis tasks, hindering our ability to design new algorithms that provide significantly more benefits than the existing ones.This dissertation contributes a multi-faceted approach for evaluating visualization recommendation algorithms to investigate factors affecting an algorithm's performance and ways to improve it. It first proposes an evaluation-focused framework and then demonstrates how the framework can evaluate strategic behaviors and user performance among a broad range of existing algorithms. The case study results show that newly proposed algorithms might not significantly improve user performance. One way to improve the algorithm performance is by integrating more established theoretical rules or empirical results on how people perceive different visualization designs, i.e., graphical perception guidelines, to guide the recommendation ranking process. Thus, this dissertation next presents a thorough literature review of existing graphical perception literature that can inform visualization recommendation algorithms. It contributes a systematic dataset that collates existing theoretical and experimental visualization comparison results and summarizes key study outcomes. Further, this dissertation conducts an exploratory analysis to investigate the influence of each piece of graphical perception study in changing a visualization recommendation algorithm's behavior and outputs. The analysis results show that some graphical perception studies can alter the behavior of visualization recommendation algorithms dominantly, while others have little influence. Based on the analysis findings, this dissertation opens avenues at the intersection of graphical perception and visualization research, like how to evaluate the effectiveness of new graphical perception work in guiding visualization recommendations