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Spatial Factors Affecting User’s Perception in Map Simplification: An Empirical Analysis

  • Vincenzo Del Fatto
  • Luca Paolino
  • Monica Sebillo
  • Giuliana Vitiello
  • Genoveffa Tortora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5373)

Abstract

In this paper, we describe an empirical study we conducted on the application of a simplification algorithm, meant to understand which factors affect the human’s perception of map changes. In the study, three main factors have been taken into account, namely Number of Polygons, Number of Vertices and Screen Resolution. An analysis of variance (ANOVA) test has been applied in order to compute such evaluations. As a result, number of vertices and screen resolution turn out to be effective factors influencing the human’s perception while number of polygons as well as interaction among the factors do not have any impact on the measure.

Keywords

Ramer-Douglas-Peucker Algorithm Human Factors Cognitive Aspects Maps Controlled Experiment ANOVA Test 

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References

  1. 1.
    Bloch, M., Harrower, M.: MapShaper.org: A Map Generalization Web Service. In: Proc. of Autocarto 2006, Vancouver, Canada, June 26-28 (2006)Google Scholar
  2. 2.
    Buttenfield, B., McMaster, R.B.: Map Generalization: Making Rules for knowledge representation, Longman Group, UK (1991)Google Scholar
  3. 3.
    Cromley, R.G., Campbell, G.M.: Integrating quantitative and qualitative aspects of digital line simplification. The Cartographic Journal 29(1), 25–30 (1992)CrossRefGoogle Scholar
  4. 4.
    Douglas, D., Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer 10(2) (1973)Google Scholar
  5. 5.
    Dutton, G.: Scale, Sinuosity, and Point Selection in Digital Line Generalization. Cartography and Geographic Information Science 26(1), 33–53 (1999)MathSciNetCrossRefGoogle Scholar
  6. 6.
    ESRI ArcView, http://www.esri.com (accessed 3.2. 03/06/08)
  7. 7.
    Hershberger, J., Snoeyink, J.: Speeding up the Douglas-Peucker line simplification algorithm. In: Proc. 5th Intl. Symp. on Spatial Data Handling, vol. 1, pp. 134–143 (1992)Google Scholar
  8. 8.
    Kazemi, S., Lim, S.: Deriving Multi-Scale GEODATA from TOPO-250K Road Network Data. Journal of Spatial Science 52(1) (2007)Google Scholar
  9. 9.
    Kazemi, S., Lim, S., Rizos, C.: A review of Map and Spatial Database Generalization for Developing a Generalization Framework. In: ISPRS Conference, Generalization and Data Mining (2004)Google Scholar
  10. 10.
    Lee, D.: Generalization within a geoprocessing framework. In: Proceedings of GEOPRO 2003 Workshop, Mexico City, November, pp. 1–10 (2003)Google Scholar
  11. 11.
    MacEachren, A.M.: How maps work: Representation, visualization, and design. Guilford Press, New York (1995)Google Scholar
  12. 12.
    McMaster, R.B., Shea, K.S.: Generalization in Digital Cartography. Association of American Geographer, Washington (1992)Google Scholar
  13. 13.
    Montello, D.R.: Cognitive map design research in the twentieth century: Theoretical and empirical approaches. Cartography and Geographic Information Science 29(3) (2002)Google Scholar
  14. 14.
    Montello, D.R., Golledge, R.: Scale and Detail in the Cognition of Geographic Information. University of California, California (1999), http://www.ncgia.ucsb.edu/Publications/Varenius_Reports/Scale_and_Detail_in_Cognition.pdf
  15. 15.
    Nakos, B.: Comparison of manual versus digital line generalization. In: Proceedings of Workshop on Generalization, Ottawa, Canada (August 1999)Google Scholar
  16. 16.
    Ramer, U.: An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing 1, 244–256 (1972)CrossRefGoogle Scholar
  17. 17.
    Saalfeld, A.: Topologicaljy Consistent Line Simplification with the RDP Algorithm. Cartography and Geographic Information Science 26(1), 7–18 (1999)CrossRefGoogle Scholar
  18. 18.
    Sheldon, M.R.: Introduction to probability and Statistics for Engineers and Scientists, 2nd edn. Academic Press, London (2003)Google Scholar
  19. 19.
    Slocum, T.A., Blok, C., Jiang, B., Koussoulakou, A., Montello, D.R., Fuhrmann, S., Hedley, N.R.: Cognitive and usability issues in geovisualization. Cartography and Geographic Information Science 28, 61–75 (2001)CrossRefGoogle Scholar
  20. 20.
    SYSTAT ver.12., http://www.systat.com
  21. 21.
    Topfer, F., Pilliwizer, W.: The Principles of Map Selection. The Cartographic Journal 3, 10–16 (1966)CrossRefGoogle Scholar
  22. 22.
    Visvalingam, M., Whyatt, J.D.: Line generalization by repeated elimination of points. The Cartographic Journal 30(1), 46–51 (1993)CrossRefGoogle Scholar
  23. 23.
    Visvalingam, M., Williamson, P.J.: Simplification and generalization of large-scale data for roads - a comparison of two filtering algorithms. Cartography and Geographic Information Systems 22(4), 264–275 (1995)CrossRefGoogle Scholar
  24. 24.
    Wang, Z., Muller, J.C.: Line generalization based on analysis of shape characteristics. Cartography and Geographic Information Systems 25(1), 3–15 (1998)CrossRefGoogle Scholar
  25. 25.
    Wu, S.T., Márquez, M.R.G.: A non-self-intersection Douglas-Peucker Algorithm. In: Proceedings Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPHI XVI (2003)Google Scholar
  26. 26.
    Zhao, H., Li, X., Jiang, L.: A modified Douglas-Peucker simplification algorithm. In: Proc. of Geoscience and Remote Sensing Symposium (IGARSS 2001), Sydney, Australia (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vincenzo Del Fatto
    • 1
  • Luca Paolino
    • 1
  • Monica Sebillo
    • 1
  • Giuliana Vitiello
    • 1
  • Genoveffa Tortora
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di SalernoFisciano (SA)Italy

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