Quantitative data visualization with interactive KDE surfaces
Martin Florek, Helwig Hauser
INPROCEEDINGS,
Proceedings of the Spring Conference on Computer Graphics (SCCG 2010),
May, 2010
AbstractKernel 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 KDEbased 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 KDEbased 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 KDEbased 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)
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 KDEbased 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 KDEbased 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 KDEbased 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},
}
