Abstract 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.

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.