CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visual explanations: images and quantities, evidence and narrative
Visual explanations: images and quantities, evidence and narrative
The grammar of graphics
Visualizing Categorical Data
Visualizing Data
SAS System for Statistical Graphics,First Edition
SAS System for Statistical Graphics,First Edition
Clustering Algorithms
Interaction with the Reorderable Matrix
IV '99 Proceedings of the 1999 International Conference on Information Visualisation
Semiology of graphics
Visualizing categorical data in ViSta
Computational Statistics & Data Analysis - Data visualization
Mapping nominal values to numbers for effective visualization
Information Visualization - Special issue of selected and extended InfoVis 03 papers
IEEE Transactions on Visualization and Computer Graphics
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
Spatial ordering and encoding for geographic data mining and visualization
Journal of Intelligent Information Systems
Visual analytics of spatial interaction patterns for pandemic decision support
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
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This paper outlines a general framework for ordering information in visual displays (tables and graphs) according to the effects or trends which we desire to see. This idea, termed effect-ordered data displays, applies principally to the arrangement of unordered factors for quantitative data and frequency data, and to the arrangement of variables and observations in multivariate displays (star plots, parallel coordinate plots, and so forth).As examples of this principle, we present several techniques for ordering items, levels or variables "optimally", based on some desired criterion. All of these may be based on eigenvalue or singular-value decompositions.Along the way, we tell some stories about data display, illustrated by graphs--some surprisingly bad, and some surprisingly good--for showing patterns, trends, and anomalies in data. We hope to raise more questions than we can provide answers for.