A Fractional Cartesian Composition Model for Semi-spatial Comparative Visualization Design
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 possibleâeven 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.
I. Kolesar, S. Bruckner, I. Viola, and H. Hauser, "A Fractional Cartesian Composition Model for Semi-spatial Comparative Visualization Design," IEEE Transactions on Visualization and Computer Graphics, vol. 23, iss. 1, p. 851–860, 2017. doi:10.1109/TVCG.2016.2598870
[BibTeX]
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 possibleâeven 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.
@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",
number = "1",
pages = "851--860",
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 possibleâeven 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.",
pdf = "pdfs/Kolesar-2017-FCC.pdf",
images = "images/Kolesar-2017-FCC.jpg",
thumbnails = "images/Kolesar-2017-FCC.png",
youtube = "https://www.youtube.com/watch?v=_zk67fmryok",
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",
project = "physioillustration"
}