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The complex nature of multivariate data sets calls for high interactive performance and intuitive metaphors. A specific type of multivariate data is where the variables sum up to a constant, here defined as multicomponent data. This application paper presents an interactive application for analysis of modeled multicomponent data. The aim is to find high performance variable combinations that fulfill...
Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis...
Geovisual analytics focuses on finding location-related patterns and relationship. Many approaches exist but generally do not scale well with large spatial datasets. We propose three enhancements that facilitate scalable geovisual analytics of voluminous geospatial data based on geographic mapping coordinated and linked with parallel coordinates (PC): 1) texture-based geographic mapping that exploits...
Data sets containing a combination of categorical and continuous variables (mixed data sets) are difficult to analyse since no generalized similarity measure exists for categorical variables. Quantification of categorical variables makes it possible to represent this type of data using techniques designed for numerical data. This paper presents a quantification process of categorical variables in...
Implementing InfoVis multivariate data tools, timelinked coordinated views and visual dynamic queries with conditioning from scratch is not a simple programming task. Our research objective is to develop a generic GeoAnalytics visualization (GAV) component toolkit, based on the principles behind visual analytics (VA), for dynamically exploring time-varying, geographically referenced and multivariate...
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