A Displacement Method for Maps Showing Dense Sets of Points of Interest

  • Sarah TauscherEmail author
  • Karl Neumann
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


In the past, point data only play a minor role in map generalization, as points are either already the result of generalization or are used for objects which are only shown on large scale maps. Now, with the growing availability of web mapping services the role of point data has changed: Besides route planning, the most common function of web maps is the visualization of user queries for points of interest. The limited size of commonly used displays often results in a smaller scale as would be appropriate for the maps content. The state of the art to resolve occurring cluttered point sets, is on the one hand interactivity and on the other hand the selection of points. Thus, often the available space is not optimally used. Therefore, we propose a displacement method to improve the readability of dense sets of points of interest.


Displacement Point symbol Voronoi diagram 


  1. Aurenhammer F (1987) Power diagrams: properties, algorithms and applications. SIAM J Comput 16(1):78–96CrossRefGoogle Scholar
  2. Bereuter P, Weibel R (2013) Real-time generalization of point data in mobile and web mapping using quadtrees. Cartographic Geogr Inf Sci 40(4):271–281CrossRefGoogle Scholar
  3. Burghardt D, Meier S (1997) Cartographic displacement using the snakes concept. In: Proceedings of the Semantic modeling for the acquisition of topographic information from images and maps, Birkhauser Verlag, pp 59–71Google Scholar
  4. Harrie L (1999) The constraint method for solving spatial conflicts in cartographic generalization. Cartography Geogr Inf Sci 26:55–69CrossRefGoogle Scholar
  5. Højholt P (2000) Solving space conflicts in map generalization: using a finite element method. Cartography Geogr Inf Sci 27(1):65–73CrossRefGoogle Scholar
  6. Korpi J, Haybatollahi M, Ahonen-Rainio P (2014) Identification of partially occluded map symbols. Cartographic Perspectives, (76):19–32. doi: 10.14714/CP76.59
  7. Lonergan ME, Jones CB (2001) An iterative displacement method for conflict resolution in map generalization. Algorithmica 30(2):287–301CrossRefGoogle Scholar
  8. Mackaness WA (1994) An algorithm for conflict identification and feature displacement in automated map generalization. Cartography Geogr Inf Syst 21(4):219–232CrossRefGoogle Scholar
  9. Mackaness WA, Purves R (2001) Automated displacement for large numbers of discrete map objects. Algorithmica 30:302–311CrossRefGoogle Scholar
  10. Ruas A (1998) OO-constraint modelling to automate urban generalization process. In: Proceedings of the eighth international symposium on spatial data handling, Vancouver, Canada, 12–15 July 1998, pp 225–235Google Scholar
  11. Sarjakoski T, Kilpeläinen T (1999) Holistic cartographic generalization by least squares adjustment for large data sets. In: Proceedings of the 19th ICA/ACI conference, Ottawa, Canada, pp 1091–1098Google Scholar
  12. Sester M (2001) Optimization approaches for generalization. In: Proceedings of geographical information systems research, University of Glamorgan, Wales, 18–20 April 2001 pp 32–35Google Scholar
  13. Ware JM, Jones CB (1998) Conflict reduction in map generalization using iterative improvement. GeoInformatica 2(4):383–407CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Information SystemsTechnische UniversitätBrunswickGermany

Personalised recommendations