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Shape-Aware Line Generalisation With Weighted Effective Area

  • Sheng Zhou
  • Christopher B. Jones

Abstract

Few line generalisation algorithms provide explicit control over the style of generalisation that results. In this paper we introduce weighted effective area, a set of area-based metrics for cartographic line generalisation following the bottom-up approach of the Visvalingam-Whyatt algorithm. Various weight factors are used to reflect the flatness, skewness and convexity of the triangle upon which the Visvalingam-Whyatt effective area is computed. Our experimental results indicate these weight factors may provide much greater control over generalisation effects than is possible with the original algorithm. An online web demonstrator for weighted effective area has been set up.

Keywords

Effective Area Delaunay Triangulation Line Generalisation Graphic Limit Graphic Threshold 
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.

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References

  1. Douglas, D.H. and Peucker, T.K., 1973, Algorithms for the reduction of the number of points required to represent a digitised line or its caricature. The Canadian Cartographer, 10(2), 112–122.Google Scholar
  2. Normant, F. and van de Walle, A., 1996, The Sausage of Local Convex Hulls of a Curve and the Douglas-Peucker Algorithm, Cartographica, 33(4), 25–35Google Scholar
  3. Ogniewicz, R.L. and Kübler, O., 1995, Hierarchic Voronoi Skeletons, Pattern Recognition, 28(3), 343–359CrossRefGoogle Scholar
  4. Plazanet, C., 1995, Measurements, Character ization, and Classi fication for Automated Line Feature Generalization. ACSM/ASPRS Annual Convention and Exposition, Vol. 4 (Proc. Auto-Carto 12): 59–68Google Scholar
  5. Ramer, U., 1972, An iterative procedure for polygonal approximation of planar closed curves. Computer Graphics and Image Processing 1, 244–256.Google Scholar
  6. Robinson, A.H., Morrison, J.L., Muehrcke, P.C., Kimerling, A.J. and Guptill, S.C., 1995, Elements of Cartography, sixth edition. John Wiley & Sons, Inc.Google Scholar
  7. van der Poorten, P.M. and Jones, C.B., 2002, Characterisation and generalisation of cartographic lines using Delaunay triangulation. International Journal of Geographical Information Science 16(8), 773–794.CrossRefGoogle Scholar
  8. Visvalingam, M. and Whyatt, J.D., 1993, Line generalisation by repeated elimination of points. Cartographic Journal, 30(1), 46–51.Google Scholar
  9. Visvalingam, M. and Williamson, P.J, 1995, Simplification and generalization of large scale data for roads. Cartography and Geographic Information Science 22(4), 3–15.Google Scholar
  10. Visvalingam, M. and Herbert, S., 1999, A computer science perspective on the bendsimplification algorithm. Cartography and Geographic Information Science 26(4), 253–270.Google Scholar
  11. Zhou, S. and Jones, C.B., 2001, Multi-Scale Spatial Database and Map Generalisation. ICA Commission on Map Generalization 4th Workshop on Progress in Automated Map GeneralizationGoogle Scholar
  12. Zhou, S. and Jones, C.B., 2003, A Multi-representation Spatial Data Model. Proc. 8th International Symposium on Advances in Spatial and Temporal Databases (SSTD’03), LNCS 2750, 394–411Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sheng Zhou
    • 1
  • Christopher B. Jones
    • 1
  1. 1.School of Computer ScienceCardiff UniversityCardiffUK

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