SalienTime: User-driven Selection of Salient Time Steps for Large-Scale
  Geospatial Data Visualization
Juntong Chen Haiwen Huang Huayuan Ye Zhong Peng Chenhui Li Changbo Wang
The voluminous nature of geospatial temporal data from physical monitors and
simulation models poses challenges to efficient 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 definition of salient time steps via extensive need-finding
studies with domain experts to understand their workflows. 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 flexible
selections. User-specified priorities, spatial regions, and aggregations are
used to combine different perspectives. We design and implement a web-based
interface to enable efficient and context-aware selection of time steps and
evaluate its efficacy and usability through case studies, quantitative
evaluations, and expert interviews.