A. Srinivasan, J. Ellemose, P. W. S. Butcher, P. D. Ritsos, and N. Elmqvist, “Attention-Aware Visualization: Tracking and Responding to User Perception Over Time,” IEEE Transactions on Visualization and Computer Graphics, 2025. We propose the notion of Attention-Aware Visualizations (AAVs) that track the user’s perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make visualizations react appropriately to the user’s attention: for example, by highlighting data the user has not yet seen. We can separate the approach into three components: (1) measuring the user’s gaze on a visualization and its parts; (2) tracking the user’s attention over time; and (3) reactively modifying the visual representation based on the current attention metric. In this paper, we present two separate implementations of AAV: a 2D data-agnostic method for web-based visualizations that can use an embodied eyetracker to capture the user’s gaze, and a 3D data-aware one that uses the stencil buffer to track the visibility of each individual mark in a visualization. Both methods provide similar mechanisms for accumulating attention over time and changing the appearance of marks in response. We also present results from a qualitative evaluation studying visual feedback and triggering mechanisms for capturing and revisualizing attention.
[Abstract]   [Details]   [PDF]   [Preprint]   [doi:10.1109/TVCG.2024.3456300]   [Presented at IEEE VIS 2024]

J. Jackson, P. D. Ritsos, P. W. S. Butcher, and J. C. Roberts, “Path-based Design Model for Constructing and Exploring Alternative Visualisations,” IEEE Transactions on Visualization and Computer Graphics, 2025. We present a path-based design model and system for designing and creating visualisations. Our model represents a systematic approach to constructing visual representations of data or concepts following a predefined sequence of steps. The initial step involves outlining the overall appearance of the visualisation by creating a skeleton structure, referred to as a flowpath. Subsequently, we specify objects, visual marks, properties, and appearance, storing them in a gene. Lastly, we map data onto the flowpath, ensuring suitable morphisms. Alternative designs are created by exchanging values in the gene. For example, designs that share similar traits, are created by making small incremental changes to the gene. Our design methodology fosters the generatiion of diverse creative concepts, space-filling visualisations, and traditional formats like bar charts, circular plots and pie charts. Through our implementation we showcase the model in action. As an example application, we integrate the output visualisations onto a smartwatch and visualisation dashboards. In this article we (1) introduce, define and explain the path model and discuss possibilities for its use, (2) present our implementation, results, and evaluation, and (3) demonstrate and evaluate an application of its use on a mobile watch.
[Abstract]   [Details]   [PDF]   [Preprint]   [doi:10.1109/TVCG.2024.3456323]   [Presented at IEEE VIS 2024]