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
@article{arxiv.0907.2585,
title = {GMap: Drawing Graphs as Maps},
author = {Emden R. Gansner and Yifan Hu and Stephen G. Kobourov},
journal= {arXiv preprint arXiv:0907.2585},
year = {2009}
}