EVis: Visually Analyzing Environmentally Driven Events
Tinghao Feng, Jing Yang, Martha-Cary Eppes, Zhaocong Yang, Faye Moser
Abstract
Earth scientists are increasingly employing time series data with multiple dimensions and high temporal resolution to study the impacts of climate and environmental changes on Earth's atmosphere, biosphere, hydrosphere, and lithosphere. However, the large number of variables and varying time scales of antecedent conditions contributing to natural phenomena hinder scientists from completing more than the most basic analyses. In this paper, we present EVis (Environmental Visualization), a new visual analytics prototype to help scientists analyze and explore recurring environmental events (e.g. rock fracture, landslides, heat waves, floods) and their relationships with high dimensional time series of continuous numeric environmental variables, such as ambient temperature and precipitation. EVis provides coordinated scatterplots, heatmaps, histograms, and RadViz for foundational analyses. These features allow users to interactively examine relationships between events and one, two, three, or more environmental variables. EVis also provides a novel visual analytics approach to allowing users to discover temporally lagging relationships related to antecedent conditions between events and multiple variables, a critical task in Earth sciences. In particular, this latter approach projects multivariate time series onto trajectories in a 2D space using RadViz, and clusters the trajectories for temporal pattern discovery. Our case studies with rock cracking data and interviews with domain experts from a range of sub-disciplines within Earth sciences illustrate the extensive applicability and usefulness of EVis.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Related papers
- Supporting Guided Exploratory Visual Analysis on Time Series Data with Reinforcement LearningYang Shi, Bingchang Chen, Ying Chen, Zhuochen Jin et al.IEEE VIS 2023 · 8 citations
- Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering ImagesXinyi Huang, Suphanut Jamonnak, Ye Zhao, Boyu Wang et al.IEEE VIS 2020 · 10 citations
- Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic CareersYifang Wang, Tai-Quan Peng, Huihua Lu, Haoren Wang et al.IEEE VIS 2021 · 18 citations
- EventBox: A Novel Visual Encoding for Interactive Analysis of Temporal and Multivariate Attributes in Event SequencesLuis Montana, Jessica Magallanes, Miguel A. Juárez, Suzanne Mason et al.IEEE VIS 2025 · 1 citation
- EVM: Incorporating Model Checking into Exploratory Visual AnalysisAlex Kale, Ziyang Guo, Xiaoli Qiao, Jeffrey Heer et al.IEEE VIS 2023 · 16 citations
