  Information visualization is essential in making sense out of large data
sets. Often, high-dimensional data are visualized as a collection of points in
2-dimensional space through dimensionality reduction techniques. However, these
traditional methods often do not capture well the underlying structural
information, clustering, and neighborhoods. In this paper, we describe GMap: a
practical tool for visualizing relational data with geographic-like maps. We
illustrate the effectiveness of this approach with examples from several
domains All the maps referenced in this paper can be found in
http://www.research.att.com/~yifanhu/GMap
