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A Fractional Cartesian Composition Model for Semi-spatial Comparative Visualization Design

Ivan Kolesar, Stefan Bruckner, Ivan Viola, Helwig Hauser

JOURNAL ARTICLE: IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 851–860, 2017. DOI: 10.1109/TVCG.2016.2598870

Abstract

The study of spatial data ensembles leads to substantial visualization challenges in a variety of applications. In this paper, we present a model for comparative visualization that supports the design of according ensemble visualization solutions by partial automation. We focus on applications, where the user is interested in preserving selected spatial data characteristics of the data as much as possible—even when many ensemble members should be jointly studied using comparative visualization. In our model, we separate the design challenge into a minimal set of user-specified parameters and an optimization component for the automatic configuration of the remaining design variables. We provide an illustrated formal description of our model and exemplify our approach in the context of several application examples from different domains in order to demonstrate its generality within the class of comparative visualization problems for spatial data ensembles.

Published

IEEE Transactions on Visualization and Computer Graphics

  • Volume: 23
  • Number: 1
  • Pages: 851–860
  • Event: IEEE SciVis 2016
  • Location: Baltimore, USA
  • Date: January 2017
  • DOI: 10.1109/TVCG.2016.2598870

Documents and Links

  • paper

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BibTeX

@ARTICLE{Kolesar-2017-FCC,
  author = {Ivan Kolesar and Stefan Bruckner and Ivan Viola and Helwig Hauser},
  title = {A Fractional Cartesian Composition Model for Semi-spatial Comparative
	Visualization Design},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2017},
  volume = {23},
  pages = {851--860},
  number = {1},
  month = jan,
  abstract = {The study of spatial data ensembles leads to substantial visualization
	challenges in a variety of applications. In this paper, we present
	a model for comparative visualization that supports the design of
	according ensemble visualization solutions by partial automation.
	We focus on applications, where the user is interested in preserving
	selected spatial data characteristics of the data as much as possible—even
	when many ensemble members should be jointly studied using comparative
	visualization. In our model, we separate the design challenge into
	a minimal set of user-specified parameters and an optimization component
	for the automatic configuration of the remaining design variables.
	We provide an illustrated formal description of our model and exemplify
	our approach in the context of several application examples from
	different domains in order to demonstrate its generality within the
	class of comparative visualization problems for spatial data ensembles.},
  doi = {10.1109/TVCG.2016.2598870},
  event = {IEEE SciVis 2016},
  keywords = {visualization models, integrating spatial and non-spatial data visualization,
	design methodologies},
  location = {Baltimore, USA},
}






 Last Modified: Stefan Bruckner, 2017-06-14