A Storage and Transfer Efficient Data Structure for Variable Scale Vector Data

  • Martijn Meijers
  • Peter van Oosterom
  • Wilko Quak
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


This paper deals with efficient data handling of variable scale vector data. Instead of pre-building a collection of data sets on different scales, we create an index structure on the base data set (largest scale data) that enables us to extract a map at exactly the right scale the moment we need it. We present both the classic version of the tGAP (topological Generalized Area Partitioning) data structure for storing our variable scale map, as well as an ameliorated version, both based on topological concepts. We prove that the classic structure needs in a worst case scenario O(e 2) edges (with e the number of edges at largest scale). In practice we observed up to a factor 15 more edges in the variable scale data structure. The tGAP structure has been optimized to reduce geometric redundancy, but the explosion of additional edges is due to the changing topological references. Our main achievement finds its roots in the reduction of the number of edge rows to be stored for the ‘lean’ version (by removing the topological referential redundancy of the classic tGAP), which is beneficial both for storage and transfer. We show that storage space for the data set, plus the index, is less than twice the size of the original data set. The ‘lean’ tGAP, as the classic tGAP, offers true variable scale access to the data and has also improved performance, mainly due to less data communication between server and client.


Original Edge Edge Version Edge Table Face Reference Save Storage Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bader, M. and Weibel, R. (1997). Detecting and Resolving Size and Proximity Conflicts in the Generalization of Polygonal Maps. In Proceedings of the 18th International Cartographic Conference., pages 1525–1532, Stockholm.Google Scholar
  2. Bertolotto, M. and Zhou, M. (2007). Efficient and consistent line simplification for web mapping. International Journal of Web Engineering and Technology, 3(2):139–156.CrossRefGoogle Scholar
  3. Bobzien, M., Burghardt, D., Petzold, I., Neun, M., and Weibel, R. (2006). Multi-Representation Databases with Explicitly Modelled Intra-Resolution, Inter-Resolution and Update Relations. In Proceedings Auto-Carto 2006, Vancouver.Google Scholar
  4. Buttenfield, B. and Wolf, E. (2007). “The road and the river should cross at the bridge” problem: Establishing internal and relative topology in an MRDB. In Proceedings of the 10th ICA Workshop on Generalization and Multiple Representation 2-3 August 2007, Moscow, Russia.Google Scholar
  5. Cecconi, A. and Galanda, M. (2002). Adaptive Zooming inWeb Cartography. In Computer Graphics Forum, volume 21, pages 787–799. Blackwell Synergy.Google Scholar
  6. Ellsiepen, M. (2007). Partial regeneralization and its requirements on data structure and generalization functions. In Kremers, H., editor, Proceedings 2nd ISGI 2007: International CODATA symposium on Generalization of Information, Lecture Notes in Information Sciences, pages 72–84, Germany. CODATA.Google Scholar
  7. Saalfeld, A. (1999). Topologically Consistent Line Simplification with the Douglas-Peucker Algorithm. Cartography and Geographic Information Science, 26(1):7–18.CrossRefGoogle Scholar
  8. Stoter, J., Morales, J., Lemmens, R., Meijers, M., Van Oosterom, P., Quak, W., Uitermark, H., and van den Brink, L. (2008). A data model for multi-scale topographical data. In Headway in Spatial Data Handling 13th International Symposium on Spatial Data Handling, pages 233–254.Google Scholar
  9. Töpfer, F. and Pillewizer, W. (1966). The principles of selection, a means of cartographic generalization. Cartographic Journal, 3(1):10–16.Google Scholar
  10. Van Oosterom, P. (1995). The gap-tree, an approach to “on-the-fly” map generalization of an area partitioning. In Müller, J., Lagrange, J., and Weibel, R., editors, GIS and Generalization, Methodology and Practice, page 120–132. Taylor & Francis.Google Scholar
  11. Van Oosterom, P. (2005). Scaleless topological data structures suitable for progressive transfer: the gap-face tree and gap-edge forest. In Proceedings Auto-Carto 2005, Las Vegas, Nevada. Cartography and Geographic Information Society (CaGIS).Google Scholar
  12. Van Oosterom, P., de Vries, M., and Meijers, M. (2006). Vario-scale data server in a web service context. In Ruas, A. and Mackaness, W., editors, Proceedings of the ICA Commission on Map Generalisation and Multiple Representation, pages 1–14, Paris, France. ICA Commission on Map Generalisation and Multiple Representation.Google Scholar
  13. Van Oosterom, P. and Vijlbrief, T. (1994). Integrating complex spatial analysis functions in an extensible gis. In Proceedings of the 6th International Symposium on Spatial Data Handling, pages 277–296, Edinburgh, Scotland.Google Scholar
  14. Vermeij, M., Van Oosterom, P., Quak, W., and Tijssen, T. (2003). Storing and using scaleless topological data efficiently in a client-server dbms environment. In 7th International Conference on GeoComputation, Southampton.Google Scholar
  15. Xinlin, Q. and Xinyana, Z. (2008). Multi-representation geographic data organization method dedicated for vector-based webgis. In Proceedings of the XXXVI congress of ISPRS, volume Part B4 ommision IV, pages 815–819.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Martijn Meijers
    • 1
  • Peter van Oosterom
    • 1
  • Wilko Quak
    • 1
  1. 1.Delft University of TechnologyOTB Research Institute for Housing, Urban and Mobility StudiesUSA

Personalised recommendations