SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays

Towards Enhancing Low Vision Usability of Data Charts on Smartphones

The importance of data charts is self-evident, given their ability to express complex data in a simple format that facilitates quick and easy comparisons, analysis, and consumption. However, the inherent visual nature of the charts creates barriers for people with visual impairments to reap the associated benefits to the same extent as their sighted peers. While extant research has predominantly focused on understanding and addressing these barriers for blind screen reader users, the needs of low-vision screen magnifier users have been largely overlooked. In an interview study, almost all low-vision participants stated that it was challenging to interact with data charts on small screen devices such as smartphones and tablets, even though they could technically "see" the chart content. They ascribed these challenges mainly to the magnification-induced loss of visual context that connected data points with each other and also with chart annotations, e.g., axis values. In this paper, we present a method that addresses this problem by automatically transforming charts that are typically non-interactive images into personalizable interactive charts which allow selective viewing of desired data points and preserve visual context as much as possible under screen enlargement. We evaluated our method in a usability study with 26 low-vision participants, who all performed a set of representative chart-related tasks under different study conditions. In the study, we observed that our method significantly improved the usability of charts over both the status quo screen magnifier and a state-of-the-art space compaction-based solution.

How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

How Do Viewers Synthesize Conflicting Information from Data Visualizations?

Scientific knowledge develops through cumulative discoveries that build on, contradict, contextualize, or correct prior findings. Scientists and journalists often communicate these incremental findings to lay people through visualizations and text (e.g., the positive and negative effects of caffeine intake). Consequently, readers need to integrate diverse and contrasting evidence from multiple sources to form opinions or make decisions. However, the underlying mechanism for synthesizing information from multiple visualizations remains under-explored. To address this knowledge gap, we conducted a series of four experiments ( N=1166) in which participants synthesized empirical evidence from a pair of line charts presented sequentially. In Experiment 1, we administered a baseline condition with charts depicting no specific context where participants held no strong belief. To test for the generalizability, we introduced real-world scenarios to our visualizations in Experiment 2 and added accompanying text descriptions similar to online news articles or blog posts in Experiment 3. In all three experiments, we varied the relative direction and magnitude of line slopes within the chart pairs. We found that participants tended to weigh the positive slope more when the two charts depicted relationships in the opposite direction (e.g., one positive slope and one negative slope). Participants tended to weigh the less steep slope more when the two charts depicted relationships in the same direction (e.g., both positive). Through these experiments, we characterize participants' synthesis behaviors depending on the relationship between the information they viewed, contribute to theories describing underlying cognitive mechanisms in information synthesis, and describe design implications for data storytelling.