A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling
A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling
复制标题
基于经验动态建模的生态系统动力学可视化分析方法
DOI：
10.1109/tvcg.2020.3028956
复制
发表时间：
2021
影响因子：
5.2
通讯作者：
Sugihara, George
中科院分区：
文献类型：
--
作者：
Natsukawa, Hiroaki;Deyle, Ethan R.;Pao, Gerald M.;Koyamada, Koji;Sugihara, George
许多学科的科学探究的一个重要方法是使用观测时间序列数据来理解关键变量之间的关系，以获得对管理给定系统的潜在规则的机械见解。在真实的系统中，例如生态学中的系统，时间序列变量之间的关系通常不是静态的;相反，这些关系是动态的，并且以非线性或状态依赖的方式变化。为了进一步理解这样的系统，我们研究了适当表征这些动态的集成方法（即，当交互随时变系统状态而改变时测量交互的方法）与可以帮助分析系统行为的可视化技术。在这里，我们专注于经验动态建模（EDM）作为一个国家的最先进的方法，专门确定因果变量和措施不断变化的时间序列变量之间的状态依赖关系。EDM不是使用以参数方程为中心的方法，而是一种基于动态吸引子研究系统的无方程方法。我们提出了一个可视化的分析系统，以支持系统状态的识别和机械的解释，使用EDM构造的动态图。这项工作，详细介绍了四个分析任务，并与GUI演示，提供了一种新的综合电火花加工和可视化技术，如刷链接可视化和视觉总结，以解释代表生态系统动态的动态图。我们将我们提出的系统应用于生态模拟数据和海洋围隔研究的真实的数据作为两个关键用例。我们的案例研究表明，我们的可视化分析工具支持用户对系统状态的识别和解释，并使我们能够发现生态系统动态中的确认性和新发现。总的来说，我们证明了我们的系统可以促进对系统功能的理解，超越基于特定领域知识的高维信息的直观分析。
An important approach for scientific inquiry across many disciplines involves using observational time series data to understand the relationships between key variables to gain mechanistic insights into the underlying rules that govern the given system. In real systems, such as those found in ecology, the relationships between time series variables are generally not static; instead, these relationships are dynamical and change in a nonlinear or state-dependent manner. To further understand such systems, we investigate integrating methods that appropriately characterize these dynamics (i.e., methods that measure interactions as they change with time-varying system states) with visualization techniques that can help analyze the behavior of the system. Here, we focus on empirical dynamic modeling (EDM) as a state-of-the-art method that specifically identifies causal variables and measures changing state-dependent relationships between time series variables. Instead of using approaches centered on parametric equations, EDM is an equation-free approach that studies systems based on their dynamic attractors. We propose a visual analytics system to support the identification and mechanistic interpretation of system states using an EDM-constructed dynamic graph. This work, as detailed in four analysis tasks and demonstrated with a GUI, provides a novel synthesis of EDM and visualization techniques such as brush-link visualization and visual summarization to interpret dynamic graphs representing ecosystem dynamics. We applied our proposed system to ecological simulation data and real data from a marine mesocosm study as two key use cases. Our case studies show that our visual analytics tools support the identification and interpretation of the system state by the user, and enable us to discover both confirmatory and new findings in ecosystem dynamics. Overall, we demonstrated that our system can facilitate an understanding of how systems function beyond the intuitive analysis of high-dimensional information based on specific domain knowledge.
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DOI：
10.1109/tvcg.2013.198
发表时间：
2013-12
影响因子：
5.2
作者：
S. Hadlak;H. Schumann;C. Cap;Till Wollenberg
通讯作者：
S. Hadlak;H. Schumann;C. Cap;Till Wollenberg
影响因子：
56.9
作者：
Dixon, PA;Milicich, MJ;Sugihara, G
通讯作者：
Sugihara, G
优化动态集合图中的逐步动画
影响因子：
2.5
作者：
Kazuyo Mizuno;Hsiang-Yun Wu;Shigeo Takahashi;and Takeo Igarashi
通讯作者：
and Takeo Igarashi
DOI：
10.1109/tvcg.2019.2934251
发表时间：
2020-01-01
影响因子：
5.2
作者：
Fujiwara, Takanori;Kwon, Oh-Hyun;Ma, Kwan-Liu
通讯作者：
Ma, Kwan-Liu
用于高阶状态转换可视化探索的平滑图
影响因子：
5.2
作者：
Jorik Blaas;C. Botha;Edward Grundy;Mark W. Jones;R. Laramee;F. Post
通讯作者：
F. Post