Wai Tong

Wong Kam-Kwai, Yi-Lin Ye, Wai Tong et al.

Landslides pose a significant threat to public safety, but their dynamic processes are difficult to analyze from post-event observation alone. Computational simulation is therefore essential, but it generates vast, abstract datasets that create a cognitive gap between the analyst and the real-world, physical terrain. While Immersive Analytics (IA) begins to bridge this gap by visualizing data in 3D, we explore how these systems evolve beyond abstract data and integrate data visceralization to enhance Situational Awareness (SA). We present LandSAR, an immersive analytics system that enhances SA for landslide analysis by visceralizing landslide data through integrated simulations and visualizations. LandSAR supports real-time simulations of landslide dynamics, prevention strategies, and climate impacts, enabling multi-perspective what-if analyses. The system uses 3D-printed terrain models as tangible interfaces to facilitate haptic feedback and enable gesture-based exploration, allowing for intuitive geographical perception. Expert interviews and workshops demonstrate that LandSAR effectively improves SA and engagement.

Aoyu Wu, Wai Tong, Tim Dwyer et al.

We contribute MobileVisFixer, a new method to make visualizations more mobile-friendly. Although mobile devices have become the primary means of accessing information on the web, many existing visualizations are not optimized for small screens and can lead to a frustrating user experience. Currently, practitioners and researchers have to engage in a tedious and time-consuming process to ensure that their designs scale to screens of different sizes, and existing toolkits and libraries provide little support in diagnosing and repairing issues. To address this challenge, MobileVisFixer automates a mobile-friendly visualization re-design process with a novel reinforcement learning framework. To inform the design of MobileVisFixer, we first collected and analyzed SVG-based visualizations on the web, and identified five common mobile-friendly issues. MobileVisFixer addresses four of these issues on single-view Cartesian visualizations with linear or discrete scales by a Markov Decision Process model that is both generalizable across various visualizations and fully explainable. MobileVisFixer deconstructs charts into declarative formats, and uses a greedy heuristic based on Policy Gradient methods to find solutions to this difficult, multi-criteria optimization problem in reasonable time. In addition, MobileVisFixer can be easily extended with the incorporation of optimization algorithms for data visualizations. Quantitative evaluation on two real-world datasets demonstrates the effectiveness and generalizability of our method.