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

• URL: http://arxiv.org/abs/2403.03449v1
• Date: Wed, 6 Mar 2024 04:27:10 GMT
• Title: SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization
• Authors: Juntong Chen, Haiwen Huang, Huayuan Ye, Zhong Peng, Chenhui Li, Changbo Wang
• Abstract summary: We propose a novel approach that leverages autoencoders and dynamic programming to facilitate user-driven temporal selections. We design and implement a web-based interface to enable efficient and context-aware selection of time steps.
• Score: 16.343440057064864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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