Selected Opportunities for Integrating Statistics and Visualization in Multi-dimensional Data Exploration
Abstract
Visualization and statistics both facilitate the understanding of complex data characteristics, and there is a long history of relations between the two fields. Traditional approaches for data analysis often consider passive visualizations of statistical data properties. Interactive visual analysis, however, as addressed in this talk, allows the iterative exploration and analysis of data in a guided human computer dialog. Graphical representations of the data and well-proven interaction mechanisms are used to concurrently show, explore, and analyze complex (i.e., time-dependent, multi-variate, and/or multi-dimensional) data. Interesting subsets of the data are interactively selected (brushed) directly on the screen, the relations are investigated in other linked views (including 2D scatterplots, histograms, function graph views, parallel coordinates, but also 3D views of volumetric data).In recent work, we have studied the integration of large amounts of locally aggregated statistical data properties as well as measures of outlyingnessin an interactive visual analysis process. The approach is demonstrated on the visual analysis of multi-dimensional climate data. A discussion of possibilities explains how a further combination of interactive statisticalplots and proven interaction schemes from visualization research shows greatpotential for future research.
J. Kehrer, Selected Opportunities for Integrating Statistics and Visualization in Multi-dimensional Data Exploration, 2010.
[BibTeX]
Visualization and statistics both facilitate the understanding of complex data characteristics, and there is a long history of relations between the two fields. Traditional approaches for data analysis often consider passive visualizations of statistical data properties. Interactive visual analysis, however, as addressed in this talk, allows the iterative exploration and analysis of data in a guided human computer dialog. Graphical representations of the data and well-proven interaction mechanisms are used to concurrently show, explore, and analyze complex (i.e., time-dependent, multi-variate, and/or multi-dimensional) data. Interesting subsets of the data are interactively selected (brushed) directly on the screen, the relations are investigated in other linked views (including 2D scatterplots, histograms, function graph views, parallel coordinates, but also 3D views of volumetric data).In recent work, we have studied the integration of large amounts of locally aggregated statistical data properties as well as measures of outlyingnessin an interactive visual analysis process. The approach is demonstrated on the visual analysis of multi-dimensional climate data. A discussion of possibilities explains how a further combination of interactive statisticalplots and proven interaction schemes from visualization research shows greatpotential for future research.
@MISC {kehrer10edaVis,
author = "Johannes Kehrer",
title = "Selected Opportunities for Integrating Statistics and Visualization in Multi-dimensional Data Exploration",
howpublished = "Talk at EDAVis: Workshop on Exploratory Data Analysis and Visualisation",
month = "May 27",
year = "2010",
abstract = "Visualization and statistics both facilitate the understanding of complex data characteristics, and there is a long history of relations between the two fields. Traditional approaches for data analysis often consider passive visualizations of statistical data properties. Interactive visual analysis, however, as addressed in this talk, allows the iterative exploration and analysis of data in a guided human computer dialog. Graphical representations of the data and well-proven interaction mechanisms are used to concurrently show, explore, and analyze complex (i.e., time-dependent, multi-variate, and/or multi-dimensional) data. Interesting subsets of the data are interactively selected (brushed) directly on the screen, the relations are investigated in other linked views (including 2D scatterplots, histograms, function graph views, parallel coordinates, but also 3D views of volumetric data).In recent work, we have studied the integration of large amounts of locally aggregated statistical data properties as well as measures of outlyingnessin an interactive visual analysis process. The approach is demonstrated on the visual analysis of multi-dimensional climate data. A discussion of possibilities explains how a further combination of interactive statisticalplots and proven interaction schemes from visualization research shows greatpotential for future research.",
images = "images/kehrer10edavis.jpg",
thumbnails = "images/kehrer10edavis_thumb.jpg",
location = "Vienna, Austria"
}