Review: Visual Analytics of Climate Networks -..:: VCG Rostock

Nonlin. Processes Geophys., 22, 545–570, 2015 www.nonlin-processes-geophys.net/22/545/2015/ doi:10.5194/npg-22-545-2015 © Author(s) 2015. CC Attribution 3.0 License. Review: visual analytics of climate networks T. Nocke1, S. Buschmann2, J. F. Donges1,3, N. Marwan1, H.-J. Schulz4, and C. Tominski5 1Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam, Germany 2Hasso Plattner Institute, Prof.-Dr.-Helmert-Str. 2–3, 14482 Potsdam-Babelsberg, Germany 3Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, 114 19 Stockholm, Sweden 4Fraunhofer Institute for Computer Graphics Research, Joachim-Jungius-Str. 11, 18059 Rostock, Germany 5Institute for Computer Science, University of Rostock, Albert-Einstein-Str. 22, 18059 Rostock, Germany Correspondence to: T. Nocke ([email protected]) Received: 1 April 2015 – Published in Nonlin. Processes Geophys. Discuss.: 30 April 2015 Revised: 12 August 2015 – Accepted: 19 August 2015 – Published: 23 September 2015 Abstract. Network analysis has become an important ap- amples from global, regional, and multi-layered climate net- proach in studying complex spatiotemporal behaviour within works. 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 1 Introduction structure within climatological fields. The standard proce- dure for such network analyses is the extraction of network Data visualisation created within scientific contexts aims at measures in combination with static standard visualisation the provision of meaningful visual representations that sup- methods. Existing interactive visualisation methods and tools port the exchange of working results and provide scientists for geo-referenced network exploration are often either not with appropriate tools to reveal relations and hidden patterns known to the analyst or their potential is not fully exploited. within their data. The advantage of visualisation is that it es- To fill this gap, we illustrate how interactive visual analytics tablishes a direct interface between digital data in a computer methods in combination with geovisualisation can be tailored and the human perceptual and cognitive abilities, as it com- for visual climate network investigation. Therefore, the paper pactly and intuitively represents abstract relationships. provides a problem analysis relating the multiple visualisa- Visualisation techniques are available for different data tion challenges to a survey undertaken with network analysts classes – for instance, for 3-D scalar data representing three- from the research fields of climate and complex systems sci- dimensional phenomena such as CT scans (Zhang et al., ence. Then, as an overview for the interested practitioner, we 2011), for vector data representing data with a direction such review the state-of-the-art in climate network visualisation as air flow around an air plane (Brambilla et al., 2012), and and provide an overview of existing tools. As a further contri- multivariate data representing multiple data variables simul- bution, we introduce the visual network analytics tools CGV taneously (Fuchs and Hauser, 2009), such as temperature, and GTX, providing tailored solutions for climate network humidity, pressure, and wind speed. In scientific contexts, vi- analysis, including alternative geographic projections, edge sualisation is often used in a static manner, producing fixed bundling, and 3-D network support. Using these tools, the images or animations. paper illustrates the application potentials of visual analytics However, nowadays, there is a rising acceptance of inter- for climate networks based on several use cases including ex- active visualisation, still mainly for the purpose of present- ing scientific findings. Since 1990, significant advances have been made in enhancing visualisation as a flexible, easy-to- Published by Copernicus Publications on behalf of the European Geosciences Union & the American Geophysical Union. 546 T. Nocke et al.: Review: visual analytics of climate networks use data exploration tool. This includes the possibility to in- (a) teract directly with a view and having several different linked visualisations that immediately reflect any such interaction or changes of the underlying data, and vice versa. For intro- ductions to interactive visualisation in climate research, see Tominski et al.(2011) and Wong et al.(2014). Going even beyond this, the new research field of “visual analytics” has emerged within the last decade (Thomas and Cook, 2005), based on the idea of coupling human percep- tion abilities with automatised analysis methods and thus al- lowing new insights into huge complex data sets: “The goal of visual analytics is the creation of tools and techniques to Betweenness (log10(BC+1)) enable people to synthesise information and derive insight from massive, dynamic, ambiguous, and often conflicting data, [to] detect the expected and discover the unexpected, [to] provide timely, defensible, and understandable assess- ments and [to] communicate assessment effectively for ac- (b) tion. [. ] The challenge is to identify the best automated al- gorithm for the analysis task at hand, identify its limits which cannot be further automated, and then develop a tightly integrated solution which adequately integrates the best automated analysis algorithms with appropriate visualisation and interaction techniques.” (Keim et al., 2008, 2010). In this context, this paper reviews how the analysis of large geophysical networks such as climate networks, but also earth- quakes (Davidsen et al., 2008) and networks of rock fractures or cave passages (Phillips et al., 2015), can strongly benefit from visual analytics. To provide an example from the field of climate network Figure 1. (a) Network visualisation colour-coding scalar node mea- analysis, similarities of time series from grid- or station- sures (shortest path betweenness) on the map to study spatiotempo- based climate data can be transferred into a network struc- ral relations of the surface air temperature field (Python/PyNGL, ture of nodes and edges (see e.g. Tsonis and Roebber, 2004; reprinted from Donges et al., 2009a). (b) Visualisation high- Yamasaki et al., 2008; Donges et al., 2009a). Then, various lighting the location of network edges by colouring the number network measures are derived from the network topology of edges starting in a selected region (green box) of the map and from the often multivariate and typically time-dependent (Python/matplotlib.basemap, reprinted from Stolbova et al., 2014). data. Finally, a visualisation step is performed to investigate the spatial or spatiotemporal variability of the network properties and link them to the climate context. By applying Such plots provide simplified views of the network data, the complex network approach to climate data, interesting representing the node information while omitting the struc- and new insights into the climate system can be derived – ture denoted by the edges. In addition, missing interaction e.g. studying the stability of the global climate with respect options with such static “overview” images restrict the scien- to certain climate phenomena (e.g. Berezin et al., 2012), to tists’ investigation options for detecting possibly interesting, investigate moisture pathways and propagation of extreme partly unknown features in the data in an exploratory sense. rainfall events (Boers et al., 2013), or even to develop new Against this background, this article investigates the poten- prediction schemes (Steinhaeuser et al., 2011; Boers et al., tials and challenges arising from interactive visual analytics 2014; Ludescher et al., 2013, 2014). methods for networks and examines available tools for ex- With respect to the size of the networks consisting of large ploring geo-referenced climate networks, including two tai- numbers of edges on the one hand, and due to the restricted lored solutions developed by the authors. These software so- availability of suitable visualisation software solutions on the lutions tackle major obstacles arising in geo-referenced net- other hand, visualisation used in this context focuses mainly work visualisation, including “edge clutter” (or “spaghetti on the static representation of scalar network measures. Fig- plots”), coupled 3-D geo-networks, and performance issues ure1 illustrates typical static representations of mapping with respect to network sizes to be handled interactively. node betweenness (Fig.1a) and number of edges (Fig.1b) to This article is structured as follows: Sect.2 provides the a spatial grid by using general purpose tools such as Python background of geophysical climate networks, followed by or MATLAB. an in-depth problem analysis including a survey answered Nonlin. Processes Geophys., 22, 545–570, 2015 www.nonlin-processes-geophys.net/22/545/2015/ T. Nocke et al.: Review: visual analytics of climate networks 547 by researchers using network visualisation tools in Sect.3. j) (e.g. Donges et al., 2009a,b, 2011; Tsonis and Roebber, Thereafter, Sect.4 discusses suitable visualisation techniques 2004; Tsonis and Swanson, 2008; Yamasaki et al., 2008), but for such networks and Sect.5 lists software tools into which the threshold may also be chosen adaptively for each pair these techniques are integrated. To illustrate this state-of- based on suitable statistical significance tests of time series

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