Skip to main content

Interactive Scale-dependent multidimensional Point Data Selection using enhanced Polarization Transformation

  • Chapter
  • First Online:
Advances in Cartography and GIScience. Volume 1

Part of the book series: Lecture Notes in Geoinformation and Cartography ((ICA))

Abstract

Different fields such as Geovisualization, Web mapping or thematic and topographic cartography all need to incorporate a most recognizable and faithful representation of the real world by different map objects at different scales. The objective of this work was to enhance the existing point selection method - the Polarization Transformation - to an automatic scaledependent point data selection method for multidimensional point data sets, which is implemented in an interactive (Web-) user interface. Benefits of the new method are that in the resulting point selection the global as well as the local characteristics of the spatial point distribution and of the spatial point density are preserved; both for 2D- and 3D- point data sets. Within an interactive user interface the user can upload a point data set, define either the achieved output scale or the wanted number of points to be selected. Then the determined results using the enhanced polarization approach are shown in 2D or 3D to the user. In this work an existing 2D point selection evaluation method for points, based on Voronoi areas, was enhanced for 3D point selection evaluation by using Voronoi volumes. Thus the evaluation verified the similarity of point density and distribution before and after the point data selection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ahuja N (1982) Dot pattern processing using Voronoi neighborhoods. IEEE Transactions on Pattern Analysis and Machine Intelligence 4 (3), 336–343.

    Article  Google Scholar 

  • Ahuja N, Tuceryan M (1989) Extraction of early perceptual structure in dot patterns: integrating region, boundary and component gestalt. Computer Vision, Graphics and Image Processing 48 (3), 304–356.

    Article  Google Scholar 

  • Andrienko G, Andrienko N (2007) Blending Aggregation and Selection: Adapting Parallel Coordinates for the Visualisation of Large Datasets. The Cartographic Journal, 2005, v.42 (1), pp. 49-60.

    Article  Google Scholar 

  • Bertin J (1983). Semiology of Graphics. Diagrams, Networks, Maps, The University of Wisconsin Press, Madison [B83].

    Google Scholar 

  • Bjørke J (1996) Framework for entroy-based map evaluation. Cartography and Geographic Information Systems 23 (2), 78–95.

    Article  Google Scholar 

  • Burghardt D, Purves R S and Edwardes A J (2004) Techniques for on-the-fly generalisation of thematic point data using hierarchical data structures. Proceedings of the GIS Research UK 12th Annual Conference, Norwich, UK.

    Google Scholar 

  • Burghardt D, Cecconi A (2007) Mesh simplification for building typification. International Journal of Geographical Information Science, 21(3): 283–298.

    Article  Google Scholar 

  • De Berg M, Bose P, Cheong O, Morin P (2004) On simplifying dot maps, Computational Geometry: Theory and Applications, v.27 n.1, p.43-62, January 2004.

    Google Scholar 

  • Eckert M (1921) Die Kartenwissenschaft. DeGruyter, Berlin/New York.

    Google Scholar 

  • Edwardes A, Burghardt D & Weibel R (2005). Portrayal and Generalisation of Point Maps for Mobile Information Services. In: Meng, L., Zipf, A. & Reichenbacher, T. (eds.). Map-based Mobile Services – Theories, Methods and Implementations. Springer-Verlag. Flewelling D M, Egenhofer M J (1993) Formalizing importance: parameters for settlement selection from a geographic database, in Proceedings of Auto-Carto 11 (11th International Conference on Automated Cartography), Minneapolis, 1993, pp. 167–175.

    Google Scholar 

  • Hake G, Grünreich D and Meng L (2002) Kartographie. Visualisierung raum-zeitlicher Informationen (8. Ausgabe). DeGruyter, Berlin/New York.

    Google Scholar 

  • Krisp J M, Peters S (2010) Visualizing Dynamic 3D Densities: A Lava-lamp approach, Workshop on Movement Research, 13th AGILE International Conference on Geographic Information Science, 10-14 May 2010 - Guimarães, Portugal.

    Google Scholar 

  • Krisp J M, Peters S Burkert F, Butenuth M (2010) Visual Identification of Scattered Crowd Movement Patterns Using a Directed Kernel Density Estimation, SPM2010 Mobile Tartu, 26.-28. August 2010, Tartu, Estonia.

    Google Scholar 

  • Krisp J M, Peters S, Murphy C E, FAN H (2009) Visual Bandwidth Selection for Kernel Density Maps, PFG Photogrammetrie Fernerkundung Geoinformation, 5/2009, S.445-454.

    Google Scholar 

  • Li ZL (2007b) Algorithmic Foundation of Multi-scale Spatial Representation. CRC Press (Taylor & Francis Group), Bacon Raton. 280pp.

    Google Scholar 

  • Li Z, Huang P (2002) Quantitative measures for spatial information of maps. International Journal of Geographical Information Systems 16 (7), 699–709.

    Article  Google Scholar 

  • Langran GE, Poicker TK (1986) Integration of name selection and name placement. In: Proceedings of second International Symposium on Spatial Data Handling, Washington, USA, pp. 50-64.

    Google Scholar 

  • Mackaness WA, Ruas A & Sarajakoski LT (2007) Generalisation of Geographic Information: Cartographic Modelling and Applications. Elsevier Science.

    Google Scholar 

  • Mustière S, Moulin B (2002) “What is Spatial Context in Cartographic Generalisation?”. IAPRS & SIS, Geospatial Theory, processing and Applications, Vol. 34, No. 4, Ottawa, Canada, 8- 12 July, 2002, pp. 274-278.

    Google Scholar 

  • Neumann J (1994) The topological information content of a map: an attempt at a rehabilitation of information theory in cartography. Cartographica 31 (1), 26–34.

    Google Scholar 

  • NVAC (2010) National Visualization and Analytics Center, http://nvac.pnl.gov/, accessed 01.09.2010.

  • Peters S, Krisp J (2010) Density calculation for moving points, 13th AGILE International Conference on Geographic Information Science, 10-14 May 2010 - Guimarães, Portugal.

    Google Scholar 

  • Qian H, Wu F, Deng H (2005) A model of point cluster selection with circle characters. Science of Surveying and Mapping, Beijing, 3, pp.83-86.

    Google Scholar 

  • Qian H (2006) Automated cartographic generalization and intelligent generalization process control. PhD thesis, Information Engineering University.

    Google Scholar 

  • Qian H, Meng L, Wu F and Wang J (2006) The generalization of point clusters and its quality assessment based on a polarization approach. Mapping and Image Science, 4, pp.55-63.

    Google Scholar 

  • Qian H, Meng L & Zhang M (2007) Network simplification based on the algorithm of polarization transformation. In:CD-Proceedings of the XXIII International Cartographic Conference (ICC), Cartographic Generalization and Multiple Representation, Moscow, Russia, 4-10 August, 2007.

    Google Scholar 

  • Scott DW, 1992: Mulivariate Density Estimation. Wiley.

    Google Scholar 

  • Slocum T A, McMaster R B, Kessler F C and Howard H H (2005) Thematic Cartography and

    Google Scholar 

  • Geographic Visualization. Second Edition. Upper Saddle River, New Jersey: Pearson Education. Sukhov V (1967) Information capacity of a map entropy. Geodesy and Aerophotography 10 (4), 212–215.

    Google Scholar 

  • Sukhov V (1970) Application of information theory in generalization of map contents. International Yearbook of Cartography 10 (1), 41–47.

    Google Scholar 

  • Töpfer F, Pillewizer W (1966) The principles of selection: A means of cartographic generalization. The Cartographic Journal, 3, pp. 10–16.

    Google Scholar 

  • Van Kreveld M, Van Oostrum R, Snoeyink J (1997) Efficient settlement selection for interactive display. In: Proceedings of Auto Carto 13, Bethesda, MD, USA, pp. 287-296.

    Google Scholar 

  • VISMASTER (2010) Visual Analytics - Mastering the Information Age, http://www.vismaster.eu/, accessed 01.09.2010.

  • Weibel R, Jones CB (1998) Computational perspective on map generalization, GeoInformatica, 2, pp. 307–314.

    Article  Google Scholar 

  • Xu T, He R, Wang P (2004) Transient Stabilization Estimation of Electric System based on Stat Theory. Electrical Engineering Transaction, Beijing, 5, pp.13-16.

    Google Scholar 

  • Yan H, Weibel R (2008). An Algorithm for Point Cluster Generalization Based on the Voronoi Diagram. Computers & Geosciences, 34(8): 939-954.

    Article  Google Scholar 

  • Yukio S (1997) Cluster perception in the distribution of point objects. Cartographica 34 (1), 49–61.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stefan Peters .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Peters, S. (2011). Interactive Scale-dependent multidimensional Point Data Selection using enhanced Polarization Transformation. In: Ruas, A. (eds) Advances in Cartography and GIScience. Volume 1. Lecture Notes in Geoinformation and Cartography(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19143-5_22

Download citation

Publish with us

Policies and ethics