SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization

Abstract

The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efcient data access, often resulting in cumbersome temporal selection experiences in web-based data portals. Thus, selecting a subset of time steps for prioritized visualization and pre-loading is highly desirable. Addressing this issue, this paper establishes a multifaceted defnition of salient time steps via extensive need-fnding studies with domain experts to understand their workfows. Building on this, we propose a novel approach that leverages autoencoders and dynamic programming to facilitate user-driven temporal selections. Structural features, statistical variations, and distance penalties are incorporated to make more fexible selections. User-specifed priorities, spatial regions, and aggregations are used to combine diferent perspectives. We design and implement a web-based interface to enable efcient and context-aware selection of time steps and evaluate its efcacy and usability through case studies, quantitative evaluations, and expert interviews.