SPATIAL STRUCTURES TO SUPPORT AUTOMATIC GENERALISATION

Automating the cartographic generalisation process has been the focus of much research. It has repeatedly been reported that one critical task is to model the spatial relationships between map features to understand their meaning and importance for the target map. This paper will review the different spatial structures that are usually used by researchers to support their automatic generalisation solutions. This paper then proposes a model for building and storing a proximity graph between features. This model has been designed for supporting generalisation. This means that the graph works as a logical structure that links the features together by references, allowing fast analysis. Different types of graphs are presented. An efficient technique is described to build a triangulation on a set of features, as well as the tools to derive the other types of graphs from it. These tools have been implemented, the results are presented in the paper.

[1]  P. Højholt Solving Space Conflicts in Map Generalization: Using a Finite Element Method , 2000 .

[2]  Tinghua Ai,et al.  Displacement methods based on field analysis , 2002 .

[3]  Jonathan Richard Shewchuk,et al.  Triangle : Engineering a 2 D Quality Mesh Generator and Delaunay , 2005 .

[4]  Rex A. Dwyer A faster divide-and-conquer algorithm for constructing delaunay triangulations , 1987, Algorithmica.

[5]  Jun Chen,et al.  Automated building generalization based on urban morphology and Gestalt theory , 2004, Int. J. Geogr. Inf. Sci..

[6]  Weiping Yang MANAGING SPATIAL OBJECTS WITH THE VMO-TREE , 1996 .

[7]  Nicolas Regnauld,et al.  GENERALISING OS MASTERMAP ® TOPOGRAPHIC BUILDINGS AND ITN ROAD CENTERLINES TO 1:50 000 SCALE USING A SPATIAL HIERARCHY OF AGENTS, TRIANGULATION AND TOPOLOGY , 2005 .

[8]  Jonathan Richard Shewchuk,et al.  Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator , 1996, WACG.

[9]  Victor J. D. Tsai,et al.  Fast topological construction of Delaunay triangulations and Voronoi diagrams , 1993 .

[10]  W. Mackaness Analysis of Urban Road Networks to Support Cartographic Generalization , 1995 .

[11]  Jean-François Hangouët,et al.  Voronoï Diagrams on Line Segments: Measurements for Contextual Generalization Purposes , 1997, COSIT.

[12]  Robert L. Scot Drysdale,et al.  A comparison of sequential Delaunay triangulation algorithms , 1995, SCG '95.

[13]  W. Mackaness,et al.  Use of Graph Theory to Support Map Generalization , 1993 .

[14]  Anne Ruas,et al.  A Method vor Building Displacement in Automated Map Generalisation , 1998, Int. J. Geogr. Inf. Sci..

[15]  Robert Weibel Models and Experiments for Adaptive Computer-Assisted Terrain Generalization , 1992 .

[16]  William A. Mackaness,et al.  Automating the Detection and Simplification of Junctions in Road Networks , 1999, GeoInformatica.

[17]  R. Horton EROSIONAL DEVELOPMENT OF STREAMS AND THEIR DRAINAGE BASINS; HYDROPHYSICAL APPROACH TO QUANTITATIVE MORPHOLOGY , 1945 .

[18]  R. Thomson,et al.  The ‘ Good Continuation ’ Principle of Perceptual Organization applied to the Generalization of Road Networks , 2002 .

[19]  Robert Weibel,et al.  A review and conceptual framework of automated map generalization , 1988, Int. J. Geogr. Inf. Sci..

[20]  N. Regnauld Contextual Building Typification in Automated Map Generalization , 2001, Algorithmica.