@techreport{TD:100557,
	att_abstract={{We describe a practical approach for visualizing dynamic relational
data and multiple relationships on the same data using a geographic map metaphor,
where clusters of nodes form countries and neighboring countries correspond to
nearby clusters. Our aim is to compare two or more maps obtained using differ-
ent similarity metrics and to provide an interactive tool to visually explore the
effect of combining two or more similarity metrics. Our method ensures good
readability and mental map preservation, based on dynamic node placement with
node stability, dynamic clustering with cluster stability, and dynamic coloring
with color stability.
}},
	att_authors={yh573v},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 5th IEEE Pacific Visualization Symposium. {{, 2011-12-01}}

The definitive version was published in 19th International Symposium on Graph Drawing. {{, 2011-12-01}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={data visualization, maps, color matching},
	att_techdoc={true},
	att_techdoc_key={TD:100557},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100557_DS1_2011-12-02T14:05:06.248Z.pdf},
	author={Yifan Hu and Stephen Kobourov, Department of Computer Science, University of Arizona, Tucson, AZ and Sankar Veeramoni, Department of Computer Science, University of Arizona, Tucson, AZ},
	institution={{5th IEEE Pacific Visualization Symposium}},
	month={December},
	title={{Embedding, Clustering and Coloring for Dynamic Maps }},
	year=2011,
}