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ABSTRACT: Geovisualization (GeoViz) is an intrinsically complex process. The analyst needs to look at data from various perspectives and at various scales, from "seeing the whole" to "attending to particulars " (Andrienko and Andrienko 2006). The analyst is also supposed to "see in relation", i.e. make numerous comparisons. This inherent complexity is multiplied by the complexity of the data that is explored and analyzed. The complex, multivariate data structure and heterogeneous components of most contemporary datasets necessitate a combined use of multiple techniques and approaches. There is no single visualization method capable to show "the whole". The analyst has to decompose this whole into views, examine these views and then try to synthesize the whole picture from the partial views. Also, because of large data volumes, we must use methods capable of simultaneously providing an overall view and exposing various "particulars". Looking for "particulars" requires therefore different techniques than "seeing the whole". Some existing visualization tools such as GeoVista and CommonGIS have successfully demonstrated the advantage of multiple-linked views and the use of information visualization (InfoViz) methods such as Parallel Coordinates and Heat maps to explore spatial multivariate data. GeoViz tools support interactive visual representation and analysis of spatio-temporal data, enabling analysts to explore geospatial and multivariate data from multiple perspectives. GeoViz is differentiated from GIS because it focuses on exploratory visual analysis rather than the pre-defined mapping. GeoViz research focuses particular attention on integrating cartographic approaches with interactive visual representations from information visualization, analytical data dissemination and visual analytics.
Information Visualization, 2007. IV '07. 11th International Conference; 08/2007
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ABSTRACT: Visual Analytics is the science of analytical reasoning supported by interactive visual interfaces. People use visual analytics tools and techniques to synthesize information; derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessments effectively for action. The issues stimulating this body of research provide a grand challenge in science: turning information overload into the opportunity of the decade. Visual analytics requires interdisciplinary science beyond traditional scientific and information visualization to include statistics, data mining, knowledge and discovery technologies, cognitive science and humancomputer interaction, production and presentation, and more. An important research agenda "Illuminating the Path" provides recommendations for the next generation suite of visual analytics technologies and is available at http://nvac.pnl.gov/agenda.stm .
Information Visualization, 2006. IV 2006. Tenth International Conference on; 08/2006
