Worlds within worlds: metaphors for exploring n-dimensional virtual worlds
UIST '90 Proceedings of the 3rd annual ACM SIGGRAPH symposium on User interface software and technology
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Externalising abstract mathematical models
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Visualizing Multivariate Functions, Data, and Distributions
IEEE Computer Graphics and Applications
The Generalized Detail-In-Context Problem
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
IVEE: an Information Visualization and Exploration Environment
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
HyperSlice: visualization of scalar functions of many variables
VIS '93 Proceedings of the 4th conference on Visualization '93
Hypermoval: interactive visual validation of regression models for real-time simulation
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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The analysis of multidimensional functions is important in many engineering disciplines, and poses a major problem as the number of dimensions increases. Previous visualization approaches focus on representing three or fewer dimensions at a time. This paper presents a new focus+context visualization that provides an integrated overview of an entire multidimensional function space, with uniform treatment of all dimensions. The overview is displayed with respect to a user-controlled polar focal point in the function's parameter space. Function value patterns are viewed along rays that emanate from the focal point in all directions in the parameter space, and represented radially around the focal point in the visualization. Data near the focal point receives proportionally more screen space than distant data. This approach scales smoothly from two dimensions to 10-20, with a 1000 pixel range on each dimension.