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GPU-based, interactive exploration of large spatiotemporal climate networks.

Stefan Buschmann1, Peter Hoffmann2, Ankit Agarwal3

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Geo-Temporal eXplorer (GTX) is a GPU-based tool for visualizing large climate networks. It addresses challenges in geo-referenced data, enabling interactive analysis of complex, time-dependent, and multi-scale networks.

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Area of Science:

  • Climate Science
  • Data Visualization
  • Network Analysis

Background:

  • Analyzing large, geo-referenced complex networks in climate research presents significant challenges due to data size and heterogeneity.
  • Existing tools often struggle with time-dependent, multi-scale, and multi-layered ensemble climate networks.

Purpose of the Study:

  • Introduce the Graphics Processing Unit (GPU)-based Geo-Temporal eXplorer (GTX) tool for visual analytics of large geo-referenced complex networks.
  • Provide interactive, GPU-based solutions for on-the-fly processing, analysis, and visualization of climate network data.
  • Facilitate the exploration of complex climate information and uncover hidden temporal links.

Main Methods:

  • Development of a GPU-based tool, Geo-Temporal eXplorer (GTX), with interactive visual analytics techniques.
  • Implementation of solutions for processing, analyzing, and visualizing large, complex networks, including time-dependent, multi-scale, and multi-layered types.
  • Application of GTX to use cases involving multi-scale climatic process and climate infection risk networks.

Main Results:

  • GTX enables interactive visual exploration of large geo-referenced complex networks.
  • The tool effectively handles time-dependent, multi-scale, and multi-layered ensemble networks.
  • Demonstrated utility in reducing complexity and revealing hidden temporal links in climate data.

Conclusions:

  • GTX offers a powerful, interactive solution for visual analytics in climate research.
  • The tool enhances the understanding of complex climate systems by uncovering previously inaccessible links.
  • GTX provides a significant advancement over standard linear analysis tools for climate network data.