Computing stable Demers cartograms

Soeren Nickel, Max Sondag, Wouter Meulemans, Markus Chimani, Stephen Kobourov, Jaakko Peltonen, Martin Nöllenburg

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
167 Downloads (Pure)


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’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.
Original languageEnglish
Title of host publication27th International Symposium Graph Drawing and Network Visualization (GD)
EditorsDaniel Archambault, Csaba D. Tóth
Place of PublicationCham
Number of pages15
ISBN (Electronic)978-3-030-35802-0
ISBN (Print)978-3-030-35801-3
Publication statusPublished - 2019
Event27th International Symposium on Graph Drawing and Network Visualization, GD 2019 - Prague, Czech Republic
Duration: 17 Sept 201920 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11904 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Symposium on Graph Drawing and Network Visualization, GD 2019
Country/TerritoryCzech Republic


  • Cartograms
  • Mental-map preservation
  • Time-varying data


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