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This paper introduces the Graphics Processing Unit (GPU)-based tool Geo-Temporal eXplorer (GTX), integrating a set of highly interactive techniques for visual analytics of large geo-referenced complex networks from the climate research domain. The visual exploration of these networks faces a multitude of challenges related to the geo-reference and the size of these networks with up to several million edges and the manifold types of such networks. In this paper, solutions for the interactive visual analysis for several distinct types of large complex networks will be discussed, in particular, time-dependent, multi-scale, and multi-layered ensemble networks. Custom-tailored for climate researchers, the GTX tool supports heterogeneous tasks based on interactive, GPU-based solutions for on-the-fly large network data processing, analysis, and visualization. These solutions are illustrated for two use cases: multi-scale climatic process and climate infection risk networks. This tool helps one to reduce the complexity of the highly interrelated climate information and unveils hidden and temporal links in the climate system, not available using standard and linear tools (such as empirical orthogonal function analysis).
Teleconnection analysis of climate data is an established research field analyzing network interactions in the climate system. Furthermore, the investigation over types of networks such as electricity, trading, or flight become more into focus in the context of climate related research, with respect to both climate mitigation and adaptation. These fields produce a multitude of complex, heterogeneous, geo-referenced climate related networks. Due to the size and the different properties of such networks, their investigation is not trivial. In the sense of the counterpart to sophisticated machine learning algorithms, visual analytics methods are crucial analyzing these networks visually, interactively keeping the climate researcher in the investigation loop. Existing visualization solutions can tackle the specifics of these networks only partially, in particular, they have problems with the size, geo-reference, their interlinkage, and the time-dependency of these kinds of complex networks. Filling this gap, we have developed a new visualization tool, which intensively uses the abilities of sophisticated graphic card processors to process large amounts of network data in a very fast and parallel manner. The abilities and flexibility of the proposed approach are illustrated for a classical climate teleconnection example and for a temperature-based infection disease network on flight routes.
