A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread - Fei Yang; Jie Shen; Fengzhen Zhu; Junrui Zhang (International Journal of Digital Earth)
The widespread adoption of positioning technology and location-based services has resulted in the continuous generation of substantial volumes of accessible spatiotemporal trajectory data. While many studies focus on 2D trajectory visualization, research on visual overload in 3D space remains limited. Thus, there is a need to balance the presentation of spatiotemporal information and to minimize visual occlusions in the 3D representation of trajectories. To address this gap, we propose a global-local cooperative optimization method based on cognitive load theory, which utilizes cartographic generalization to emphasize local features and clarity, while treating 3D visualization as a global opacity optimization problem to enhance visibility and reduce occlusions. We take the spread trajectories of infectious diseases as our research subject, due to their characteristic spatiotemporal patterns, and employ a space–time cube as the visualization tool. The proposed method incorporates a 3D generalization algorithm that mitigates visual stickiness, while leveraging a 3D line field visualization technique to optimize opacity, thereby minimizing visual occlusion and spatial clutter. The experimental results validate the method's effectiveness in reducing occlusion, resolving visual entanglement, and lowering cognitive load, which in turn improving the clarity and usability of epidemic trajectory visualization.