Xiaoru Yuan

Visualization in historical research is shifting from isolated attempts to systematic practices. However, data‐driven evidence about how historians actually use visualization remains scarce. We present a corpus‐driven, mixed‐methods study that combines analysis of images from 4,142 research articles across history and digital humanities journals with a collaboratively developed visualization taxonomy and a semi‐automatic labeling pipeline. We construct a corpus of 14,021 images, classify 4,831 visualization instances using a hierarchical, domain‐informed taxonomy, and analyze patterns of visualization adoption across venues, history subfields, and time. To interpret these patterns, we conduct interviews with 11 historians and use Hi‐FigAtlas system as a boundary object to support joint inspection of the corpus. We identify distinct roles for visualizations in historical research: primary‐source, evidence‐synthesis, communicative, confirmative, and exploratory. We further find that while historians pursue diverse goals with figures, persistent epistemological and practical barriers, such as uncertainty, provenance, justification burden, and publication constraints, impede the adoption of visualization. This work contributes a grounded account of visualization use in historical scholarship and points to opportunities to better support domain‐specific needs.

The course of history is inseparable from the actions of historical figures. From the perspective of contemporaneity, an important challenge is how to visualize large numbers of historical figures and their dynamic associations on a unified temporal scale. Yet existing large-scale data solutions often rely on aggregation strategies that diminish the visibility of key figures, while failing to provide smooth contextual transitions between overview and detail. Moreover, current approaches to representing inter-figure associations lack effective integration of the surrounding historical context. To address these challenges, we propose TimeScape, a large-scale biographical data exploration system supporting multi-resolution analysis. Anchored in absolute time, the system highlights the evolving dynamics of historical development. Through multi-level layouts, sampling, and semantic zooming, it enables efficient navigation of large-scale data, allowing users to move seamlessly between overview and detail and to “wander” freely across the historical landscape. The system further allows iterative switching of focal figures to explore concrete inter-personal associations, aligning them on the temporal axis so that these connections are grounded in traceable evidence and explicit context. Case studies and expert interviews demonstrate that TimeScape effectively supports multi-perspective understanding and exploration of large-scale biographical data, offering historians a new visualization pathway for research.

Hiking activities originated during the European Industrial Revolution (Frison-Roche & Jouty, 1996) and developed into a globally popular ecological recreational activity (Moira et al., 2021). Nati...

Chinese calligraphy is a quintessential element of Chinese cultural heritage. Analyzing and comparing calligraphic styles not only enhances the appreciation, learning, and advancement of calligraphy but also provides valuable insights into ancient China. However, such analysis remains challenging due to the limited scalability and possible inconsistencies of qualitative methods, as well as usability and misalignment issues in conventional quantitative approaches. We propose Calli-VA, a visual analytics system, to address these challenges. Calli-VA extracts character images and their corresponding strokes from original works and characterizes each character using systematic criteria. During analysis, the system defines the analysis scope by overview and uncovers relationships between characters. Explanation and recommendation mechanisms are integrated to help users understand patterns and guide further exploration. A documentation feature allows users to record and share their findings. We demonstrate the effectiveness of Calli-VA through three case studies and expert feedback.

Text-to-image generative models can be tremendously valuable in supporting creative tasks by providing inspirations and enabling quick exploration of different design ideas. However, one common challenge is that users may still not be able to find anything useful after many hours and hundreds of images. Without effective help, users can easily get lost in the vast design space, forgetting what has been tried and what has not. In this work, we first propose the Design-Exploration model to formalize the exploration process. Based on this model, we create an interactive visualization system, PromptMap, to support exploratory text-to-image generation. Our system provides a new visual representation that better matches the non-linear nature of such processes, making them easier to understand and follow. It utilizes novel visual representations and intuitive interactions to help users structure the many possibilities that they can explore. We evaluated the system through in-depth interviews with users.

Research has shown that an audiences' age impacts their engagement in digital media. Interactive narrative visualization is an increasingly popular form of digital media that combines data visualization and storytelling to convey important information. However, audience age is often overlooked by interactive narrative visualization authors. Using an established visualization engagement questionnaire, we ran an empirical experiment where we compared end-user engagement to audience age. We found a small difference in engagement scores where older age cohorts were less engaged than the youngest age cohort. Our qualitative analysis revealed that the terminology and overall understanding of interactive narrative patterns integrated into narrative visualization was more apparent in the feedback from younger age cohorts relative to the older age cohorts. We conclude this paper with a series of recommendations for authors of interactive narrative visualization on how to design inclusively for audiences according to their age

Dashboards have arguably been the most used visualizations during the COVID-19 pandemic. They were used to communicate its evolution to national governments for disaster mitigation, to the public domain to inform about its status, and to epidemiologists to comprehend and predict the evolution of the disease. Each design had to be tailored for different tasks and to varying audiences—in many cases set up in a very short time due to the urgent need. In this article, we collect notable examples of dashboards and reflect on their use and design during the pandemic from a user-oriented perspective. We interview a group of researchers with varying visualization expertise who actively used dashboards during the pandemic as part of their daily workflow. We discuss our findings and compile a list of lessons learned to support future visualization researchers and dashboard designers.

Historical visualizations are a rich resource for visualization research. While taxonomy is commonly used to structure and understand the design space of visualizations, existing taxonomies primarily focus on contemporary visualizations and largely overlook historical visualizations. To address this gap, we describe an empirical method for taxonomy development. We introduce a coding protocol and the VisTaxa system for taxonomy labeling and comparison. We demonstrate using our method to develop a historical visualization taxonomy by coding 400 images of historical visualizations. We analyze the coding result and reflect on the coding process. Our work is an initial step toward a systematic investigation of the design space of historical visualizations.

Narrative visualization integrates data visualization and narrative techniques to convey a compelling story. Narrative visualization is notoriously difficult to evaluate. One solution is heuristic evaluation, using a domain-specific set of heuristics. This paper validates a set of heuristics proposed specifically for evaluating narrative visualization. We conducted studies with experienced narrative visualization practitioners in both summative and formative settings. We found that the set of heuristics showed promise in a summative setting, where similar responses evidenced that the set of heuristics could provide reliable evaluation metrics. Furthermore, in a formative setting, implementing the set of heuristics was reported to be useful in the design process; however, due to their limited focus, we recommend that it be implemented in conjunction with other evaluation guidelines. Graphical abstract