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

ArXiv Pub Date : 2024-03-06 DOI:10.1145/3613904.3642944
Juntong Chen, Haiwen Huang, Huayuan Ye, Zhong Peng, Chenhui Li, Changbo Wang
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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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SalienTime:用户驱动的大规模地理空间数据可视化突出时间步骤选择
来自物理监测器和仿真模型的大量地理空间时间数据给高效数据访问带来了挑战,往往导致基于网络的数据门户在时间选择方面的繁琐。因此,选择一个时间步骤子集进行优先可视化和预加载是非常可取的。针对这一问题,本文通过与领域专家进行广泛的需求调查研究,了解他们的工作流程,从而确定了突出时间步骤的多方面定义。在此基础上,我们提出了一种新方法,利用自动编码器和动态编程来促进用户驱动的时间选择。结构特征、统计变化和距离惩罚都被纳入其中,以实现更灵活的选择。用户指定的优先级、空间区域和聚合被用来结合不同的视角。我们设计并实施了一个基于网络的界面,以实现高效和情境感知的时间步骤选择,并通过案例研究、定量评估和专家访谈对其有效性和可用性进行了评估。
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