Quantitative data visualization with interactive KDE surfaces

Martin Florek, Helwig Hauser

INPROCEEDINGS, Proceedings of the Spring Conference on Computer Graphics (SCCG 2010), May, 2010

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Abstract

Kernel density estimation (KDE) is an established statistical concept for assessing the distributional characteristics of data that also has proven its usefulness for data visualization. In this work, we present several enhancements to such a KDE-based visualization that aim (a) at an improved specificity of the visualization with respect to the communication of quantitative information about the data and its distribution and (b) at an improved integration of such a KDE-based visualization in an interactive visualization setting, where, for example, linking and brushing is easily possible both from and to such a visualization. With our enhancements to KDE-based visualization, we can extend the utilization of this great statistical concept in the context of interactive visualization.

Published

Proceedings of the Spring Conference on Computer Graphics (SCCG 2010)

• Pages: –
• Location: Budmerice, Slovakia
• Date: May 2010
• URL: http://dx.doi.org/10.1145/1925059.1925068

Media

BibTeX

@inproceedings{florek10kde,
	author = {Martin Florek and Helwig Hauser},
	title  ={Quantitative data visualization with interactive KDE surfaces},
	year = {2010},
	booktitle = {Proceedings of the Spring Conference on Computer Graphics (SCCG 2010)},
    location = {Budmerice, Slovakia},
	pages = {--},


	abstract = {Kernel density estimation (KDE) is an established 
statistical concept for assessing the distributional characteristics of 
data that also has proven its usefulness for data visualization. In this work,
we present several enhancements to such a KDE-based visualization that aim 
(a) at an improved specificity of the visualization with respect to the 
communication of quantitative information about the data and its distribution 
and (b) at an improved integration of such a KDE-based visualization in an 
interactive visualization setting, where, for example, linking and brushing 
is easily possible both from and to such a visualization. With our enhancements 
to KDE-based visualization, we can extend the utilization of this great 
statistical concept in the context of interactive visualization.},
  month = {May},
  url = {http://dx.doi.org/10.1145/1925059.1925068},
}