@inproceedings{e4b4dab8d0624bf9ad7ab9c92e4712dd,
title = "Computing Stable Demers Cartograms",
abstract = "Cartograms are popular for visualizing numerical data for map regions. Maintaining correct adjacencies is a primary quality criterion for cartograms. When there are multiple data values per region (over time or different datasets) shown as animated or juxtaposed cartograms, preserving the viewer{\textquoteright}s mental map in terms of stability between cartograms is another important criterion. We present a method to compute stable Demers cartograms, where each region is shown as a square and similar data yield similar cartograms. We enforce orthogonal separation constraints with linear programming, and measure quality in terms of keeping adjacent regions close (cartogram quality) and using similar positions for a region between the different data values (stability). Our method guarantees ability to connect most lost adjacencies with minimal leaders. Experiments show our method yields good quality and stability.",
keywords = "Cartograms, Mental-map preservation, Time-varying data",
author = "Soeren Nickel and Max Sondag and Wouter Meulemans and Markus Chimani and Stephen Kobourov and Jaakko Peltonen and Martin N{\"o}llenburg",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 27th International Symposium on Graph Drawing and Network Visualization, GD 2019 ; Conference date: 17-09-2019 Through 20-09-2019",
year = "2019",
doi = "10.1007/978-3-030-35802-0_4",
language = "English (US)",
isbn = "9783030358013",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "46--60",
editor = "Daniel Archambault and T{\'o}th, {Csaba D.}",
booktitle = "Graph Drawing and Network Visualization - 27th International Symposium, GD 2019, Proceedings",
}