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| Titel |
Review: visual analytics of climate networks |
| VerfasserIn |
T. Nocke, S. Buschmann, J. F. Donges, N. Marwan, H.-J. Schulz, C. Tominski |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 5 ; Nr. 22, no. 5 (2015-09-23), S.545-570 |
| Datensatznummer |
250120999
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| Publikation (Nr.) |
 copernicus.org/npg-22-545-2015.pdf |
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| Zusammenfassung |
| Network analysis has become an important approach in studying complex
spatiotemporal behaviour within geophysical observation and simulation data.
This new field produces increasing numbers of large geo-referenced networks
to be analysed. Particular focus lies currently on the network analysis of
the complex statistical interrelationship structure within climatological
fields. The standard procedure for such network analyses is the extraction of
network measures in combination with static standard visualisation methods.
Existing interactive visualisation methods and tools for geo-referenced
network exploration are often either not known to the analyst or their
potential is not fully exploited. To fill this gap, we illustrate how
interactive visual analytics methods in combination with geovisualisation can
be tailored for visual climate network investigation. Therefore, the paper
provides a problem analysis relating the multiple visualisation challenges to
a survey undertaken with network analysts from the research fields of climate
and complex systems science. Then, as an overview for the interested
practitioner, we review the state-of-the-art in climate network visualisation
and provide an overview of existing tools. As a further contribution, we
introduce the visual network analytics tools CGV and GTX, providing tailored
solutions for climate network analysis, including alternative geographic
projections, edge bundling, and 3-D network support. Using these tools, the
paper illustrates the application potentials of visual analytics for climate
networks based on several use cases including examples from global, regional,
and multi-layered climate networks. |
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