Digital humanity visualization, as an innovative trend that combines historical and cultural studies with visualization, aims to provide the public with intuitive and engaging cultural exploration experiences. However, it is still challenging in this field due to the intrinsic complex and cumbersome textual data, such as how to intuitively and comprehensively present complex relationship networks and spatiotemporal evolution among the data. In this paper, we take the Jinling-related dataset as an example to design a composite visual storytelling tool that utilizes a narrative framework that smoothly changes between macro and micro perspectives. Simultaneously, AI-generated intuitive images are employed to represent the textual data, collectively narrating an engaging cultural story of Jinling. Through user studies, we validate the effectiveness and usability of the tool and demonstrate that the tool possesses excellent storytelling capabilities to improve users' cultural experiences.

We developed an AI-assisted interactive visual system that allow users to explore Journey to the West’s magical artifacts. Although prior studies have explored its narrative and symbolism, the embedded technological metaphors and speculative interaction concepts in the novel remain underexplored. Leveraging ChatGPT-4 for deep semantic analysis, we extracted mappings between principal characters and their signature artifacts and embedded interaction patterns reflecting mythical affordances. These affordances guided the creation of a progressive, narrative-driven interactive visual system built on large displays. In a user study with 20 participants, the system achieved high usability scores and strong preference with facial-matching and multimodal artifact exploration feature. Our work provides a framework for reinterpreting and showcasing mythological narratives. The Appendix is available at our GitHub repository.

Prior work has identified the shortcomings of color‐only encodings for maps with many categories, yet systematic comparisons of hybrid text–color strategies remain scarce. We therefore ran an 80‐participant crowdsourced study on choropleth maps with 8–13 categories—approaching the 10‐hue perceptual limit—to compare four annotation designs (Legend‐Aside, Label‐Fill, Label‐Fit, Colored Label‐Fill) across Count, Identify, Compare, and Rank tasks. Results show that the Label‐Fit Map—with a single, large in‐situ label—yields the highest accuracy and speed and ranks first in readability; Legend‐Aside excels in simple counting and side‐by‐side comparisons. These findings deliver clear, task‐specific guidelines for enhancing multiclass map readability and efficiency, informing the design of more effective map visualizations. All supplementary materials are available at our GitHub repository.

This study examines the effects of two visual guidance techniques, Visual Enhancement and Visual Suppression, on user perception of contextual information in video content. Visual Enhancement introduces explicit visual cues to highlight target content, whereas Visual Suppression attenuates non-target elements, for example, by reducing their brightness. Both approaches aim to isolate specific objects from the background, directing attention to critical information within complex, dynamic scenes. Despite their growing usage, the relative effectiveness of these approaches in guiding attention and their impact on peripheral context awareness remain underexplored. To address this gap, we conducted a controlled user study with 27 participants. The results indicate that Visual Enhancement, through the addition of salient cues, more effectively directs user attention to target information than Visual Suppression. Our findings advance understanding of visual attention in dynamic environments and offer implications for designing visual guidance strategies.

Abstract As smartwatches become increasingly prevalent, their built‐in sensors provide a rich source for gathering various personal data, including physical activity and health metrics. We found that different brands and models use various visualization techniques. However, the effectiveness of these visualizations within the limited display space of smartwatches remains unclear. Therefore, this paper compares four popular visualizations—bar charts, radial bar charts, donut charts and multi‐donut charts—used for displaying activity data on smartwatches. The evaluation focuses on their performance in three common user tasks: counting completed goals, estimating completion percentage and estimating exercise duration. Additionally, the study investigates the impact of the positioning of the target data item, within these visualizations on user performance. Our results indicate that bar charts are superior in terms of task completion time across all tasks. Radial bar charts and multi‐donut charts are most effective in helping users perceive the completion ratio (percentage) of each activity and understand the time taken for each activity metric (in minutes). Interestingly, we found that the positioning of data items within the visualizations significantly influences user performance in many cases. Furthermore, it was noted that the visualizations users favoured the most were generally those that enabled them to achieve the highest accuracy in task completion. These insights provide valuable guidelines for future designs in visualizing exercise data on smartwatches. Supplementary material is available at https://osf.io/5u2ph/ .

Object selection is essential in virtual reality (VR) head-mounted displays (HMDs). Prior work mainly focuses on enhancing and evaluating techniques for selecting a single object in VR, leaving a gap in the techniques for multi-object selection, a more complex but common selection scenario. To enable multi-object selection, the interaction technique should support group selection in addition to the default pointing selection mode for acquiring a single target. This composite interaction could be particularly challenging when using freehand gestural input. In this work, we present an empirical comparison of six freehand techniques, which are comprised of three mode-switching gestures (Finger Segment, Multi-Finger, and Wrist Orientation) and two group selection techniques (Cone-casting Selection and Crossing Selection) derived from prior work. Our results demonstrate the performance, user experience, and preference of each technique. The findings derive three design implications that can guide the design of freehand techniques for multi-object selection in VR HMDs.

The support for multi-focus data exploration is vital in collaborative visualization. In these scenarios, which often involve multiple devices and large displays, users may focus on specific information on their individual screens while also sharing contextual views with others. While many visualization techniques developed for single-user applications can be adapted for use in collaborative settings, little research has been done on how to design adaptive versions of these techniques or how they may impact collaborative tasks involving large datasets. In this work, we perform a comparative study of three collaborative visualization techniques (Zooming, Interactive lenses and Overview+Detail) on large displays in three map-based visualization tasks (Exploration, Comparison and Spatial Memorizing). These three collaborative techniques draw on three different classical visualization techniques in a single-user setting. Our results show that these techniques have different impacts on users’ task performance and preferences. The collaborative Overview+Detail technique benefits users most in supporting Spatial Memorizing. Closely coupled groups prefer collaborative Zooming in Target Exploration. Based on these results, we further discuss the design of collaborative visualization techniques and propose suggestions for adapting classical single-user visualization techniques to a collaborative setting.

In the modern era of hectic lifestyles, individuals often struggle to take a momentary pause from their daily routines to connect with their own physiological changes. Additionally, many people face challenges in recognizing and effectively expressing their emotions, resulting in a lack of self-awareness. This lack of emotional understanding can hinder personal growth and well-being. To tackle this issue, we present a novel visualization approach that combines interactive devices to provide a more intuitive understanding of emotions. Our approach aims to bridge the gap between individuals and their emotional states by translating abstract emotions into visually perceivable physical forms. By leveraging audio-visual equipment, our system creates an environment where individuals can actively engage with and experience their emotions through interactive devices. Through the integration of synesthesia design principles, our visualization approach enables individuals to gain a deeper understanding of their feelings and represent their emotional changes in a more tangible and expressive manner. By facilitating a multisensory experience, individuals can establish a stronger connection with their emotions, promoting self-awareness and emotional well-being. This paper showcases the innovative design and implementation of our visualization, highlighting its potential to empower individuals in understanding and expressing their emotions.

Immersive environments have become increasingly popular for visualizing and exploring large-scale, complex scientific data because of their key features: immersion, engagement, and awareness. Virtual reality offers numerous new interaction possibilities, including tactile and tangible interactions, gestures, and voice commands. However, it is crucial to determine the most effective combination of these techniques for a more natural interaction experience. In this paper, we present MEinVR, a novel multimodal interaction technique for exploring 3D molecular data in virtual reality. MEinVR combines VR controller and voice input to provide a more intuitive way for users to manip- ulate data in immersive environments. By using the VR controller to select locations and regions of interest and voice commands to perform tasks, users can efficiently perform complex data exploration tasks. Our findings provide suggestions for the design of multimodal interaction techniques in 3D data exploration in virtual reality.

This paper investigates how we can display multiple user selections on public multi-user wall-mounted large-display information visualization environments. This is done by looking at a prototype metro navigation system that allows different users to plan their route around a public metro system without relying on access to mobile devices by interacting with a large display. Our results show that user selections can be shown on this type of system can be done by using animation to highlight new selections with marching ants for lines and pulsing for points. Marching ants can merge to form a solid line, and pulsing can slow to stop after the user's selection is highlighted. This allows large numbers of users to use the same interface simultaneously as long as no two user's initial interaction occurs at the same time.

Multi-mobile devices connected to large displays afford collaborative exploration of large data sets and make decisions. However, typical interfaces for the large, such as an overview of the whole data set, do not well designed to support collaboration and promote communication. This paper introduces one innovative multiple view on large displays that accommodate multiple individual viewports in different scales in the shared space to facilitate data exchange, data comparison, and support communication. We conducted a controlled study to evaluate our approach to traditional shared display interfaces. The experiment found our techniques improved collaboration effectiveness, and individual detailed viewports during data exploration play an essential role in collaboration. Our work can potentially improve and extend our understanding of how to visualize information on shared displays.

This paper describes the Smart Survey Tool, a novel multi-device application for museum visitor tracking and tracking data visualization. The application allows museum staff to capture detailed information describing how visitors move around an exhibition and interact with individual exhibits. They can then visualize the results of tracking either on a single mobile device or with multiple mobile devices connected to a large display. The platform uses orthogonal views of the exhibition space for tracking and visualization, with a chess-piece icon to represent visitors during tracking, and curved semi-transparent lines with animated semi-circles to communicate the path and direction of visitor movement. Our visualization is novel in its use of an orthogonal projection for pedestrian tracking and animation to communicate the flow of visitors around the exhibition space, as well as allowing users to dynamically switch between views representing different groups of visitors. The design of our application was informed through an extensive requirements analysis study conducted with Nanjing Museum and evaluated by conducting expert interviews with museum managers who considered that the application allowed for more effective and efficient recording and analysis of visitor tracking data.