Automating the design of graphical presentations of relational information
ACM Transactions on Graphics (TOG)
Query, analysis, and visualization of hierarchically structured data using Polaris
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cushion Treemaps: Visualization of Hierarchical Information
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
RecMap: Rectangular Map Approximations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
IEEE Computer Graphics and Applications
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Visualization of Geo-spatial Point Sets via Global Shape Transformation and Local Pixel Placement
IEEE Transactions on Visualization and Computer Graphics
Computational Geometry: Theory and Applications
ManyEyes: a Site for Visualization at Internet Scale
IEEE Transactions on Visualization and Computer Graphics
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Colorpleth maps are commonly used to display election results, either by using one distinct color for representing the winning party in each district or by showing a proportion between two parties on a bi-polar colormap, for example, from red to blue representing Republicans vs. Democrats. Showing only the largest party may disable insights into the data whereas using bipolar colormaps works only reasonably well in cases of two parties. To overcome these limitations we introduce a new technique for visualizing proportions in such categorical data. In particular, we combine bipolar colormaps with an adapted double-rendering of polygons to simultaneously visually represent the first two categories and the spatial location. Our technique enables the recognition of close election results as well as clear majorities in a scalable manner. We proof our concept by applying our technique in a prototype implementation used to display election results from the U. S. Presidential election in 2008 and elections of the German Bundestag in 2005 and 2009. Different interesting findings are presented, which would not be recognizable when visualizing only the winner. As we additionally represent the party with the second most votes, we are able to show changes in the spatial distribution of the votes as well as outlier regions with exceptional results. Our visualization technique therefore enables valuable insights into categorical data with a spatial reference.