Computational notebooks offer a flexible environment for exploratory data analysis (EDA), but this flexibility often leads to disorganized and iterative execution of notebook cells, making it difficult to track how data states evolve. Consequently, data scientists must devote extra mental effort to staying aware of data states, which is both tedious and prone to overlooking anomalies. To address this challenge, we developed NoteFlow, a notebook extension that leverages charts as ``sight glasses'' to provide a consistent and continuous tracing of data flow. NoteFlow allows users to (1) validate various facets of the current data state using recommended charts provided immediately after each cell execution, and (2) trace the global evolution of selected charts to continuously observe how particular data attributes evolve throughout the EDA process. We evaluated NoteFlow's effectiveness through a controlled study with 12 participants and a one-month field study with 2 data scientists on real-world workflows.
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 class distinctions. However, evidence for the effectiveness of redundancy remains conflicted, and guidelines for constructing effective combinations are limited. This paper presents four crowdsourced experiments evaluating redundant color–shape encodings and identifying high-performing configurations across different category numbers. Results show that redundancy significantly improves accuracy in assessing class-level correlations, with the strongest benefits for 5–8 categories. We also find pronounced interaction effects between colors and shapes, underscoring the need for careful pairing in designing redundant encodings. Drawing on these findings, we introduce a categorical palette design tool that enables designers to construct empirically grounded palettes for effective categorical visualization. Our work advances understanding of categorical perception in data visualization by systematically identifying effective redundant color–shape combinations and embedding these insights into a practical palette design tool.
On a visualization, the position of the marks encoding data is the most expressive and effective visual channel.
It conveys order and quantity without impairing the perception of other visual channels.
In the field of Information Visualization, position is often restricted to two dimensions, because using the third dimension, \emph{depth}, usually affects the perception of size, which is also one of the most effective visual channels.
We propose a new visual channel, D-MO (Depth from Motion and Occlusion), a combination of visual cues, \emph{motion} and \emph{occlusion}, with interactions, that induces a depth perception suitable for combined use with classic visual channels.
We characterize the expressiveness and effectiveness of D-MO and show that it is a magnitude channel with good accuracy, acceptable discriminability, and is separable from size.
Thus, D-MO opens up new areas for visualization design, which is limited by the scarceness of available visual channels.
Multiple forecast visualizations (MFVs) present curated sets of forecasts to support decision-making under uncertainty. However, the research community knows little about how people interpret and integrate competing forecasts. In this study, we investigate the strategies individuals use when predicting hypothetical future events with MFVs across five visualization types (median, 95\% CIs, standard deviation intervals, density plots, and hypothetical outcome plots) and multiple probability distributions in two preregistered experiments (\textit{n} = 500 each). Analysis of 18 participant strategies and open responses shows that whereas many participants attempted to visually average across forecasts, others adopted a winner-takes-all approach (\textit{e.g.,} selecting a single forecast as the most likely outcome), which deviates from rational agent expectations. We also observed reliance on visual artifacts, such as intersection points or end caps. These findings underscore the complexity of interpreting a range of forecasts and help explain why individuals may privilege particular predictions in real-world decision contexts.
Axis truncation in bar charts is widely criticized as misleading, often based on ratio judgments or subjective ratings (i.e., Likert scale comparisons). This perspective, however, overly relies on these tasks and lacks nuance. We conducted three experiments to examine the effects of truncation across seven bar chart tasks. Our results show that truncation increases error for ratio calculations but improves accuracy or speed for tasks such as filtering and value retrieval. We further find that the magnitude of these effects depends on the degree of truncation and that direct data labeling substantially mitigates the negative effects of truncation in our experimental setting. These findings add nuance to bar chart truncation and invite discussion around the inherent "deceptiveness" of design elements.
Thematic maps are widely used to communicate spatial patterns to non-expert audiences. Although uncertainty is inherent in thematic map data, it is rarely visualized, raising questions about how its inclusion affects trust. Prior work offers mixed perspectives: some argue that uncertainty fosters trust through transparency, while others suggest it may reduce trust by introducing confusion. Yet few empirical studies explicitly measure trust in thematic maps. We conducted a between-subjects experiment (N = 161) to evaluate how visualizing uncertainty at varying levels (low, medium, high) influences trust. We find that uncertainty visualization generally reduces trust, with greater reductions observed as uncertainty levels increase. However, maps dominated by low uncertainty do not significantly differ in trust from those with no uncertainty. Moreover, while uncertainty visualization tends to make readers question the accuracy of the data, it appears to have a weaker influence on perceptions of the mapmaker’s integrity.