Downloads: 0

Files in This Item:
This article will be available after a certain embargo period.
Please see the "Rights" information in item metadata display about embargo date.
Title: A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling
Authors: Natsukawa, Hiroaki  kyouindb  KAKEN_id
Deyle, Ethan R.
Pao, Gerald M.
Koyamada, Koji  kyouindb  KAKEN_id  orcid (unconfirmed)
Sugihara, George
Author's alias: 夏川, 浩明
小山田, 耕二
Issue Date: Feb-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Journal title: IEEE Transactions on Visualization and Computer Graphics
Volume: 27
Issue: 2
Start page: 506
End page: 516
Abstract: 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.
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The full-text file will be made open to the public on 7 October 2022 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1109/TVCG.2020.3028956
PubMed ID: 33026998
Appears in Collections:Journal Articles

Show full item record

Export to RefWorks

Export Format: 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.