Description
Book SynopsisWith the widespread use of GIS, multi-scale representation has become an important issue in the realm of spatial data handling. However, no book to date has systematically tackled the different aspects of this discipline. Emphasizing map generalization, Algorithmic Foundation of Multi-Scale Spatial Representation addresses the mathematical basis of multi-scale representation, specifically, the algorithmic foundation.
Using easy-to-understand language, the author focuses on geometric transformations, with each chapter surveying a particular spatial feature. After an introduction to the essential operations required for geometric transformations as well as some mathematical and theoretical background, the book describes algorithms for a class of point features/clusters. It then examines algorithms for individual line features, such as the reduction of data points, smoothing (filtering), and scale-driven generalization, followed by a discussion of algorithms for a class of line fea
Table of ContentsIntroduction. Mathematical Background. Theoretical Background. Algorithms for Transformations of Point Features. Algorithms for Point-Reduction of Individual Line Features. Algorithms for Smoothing of Individual Line Features. Algorithms for Scale-Driven Generalization of Individual Line Features. Algorithms for Transformation of a Set of Line Features. Algorithms for Transformations of Individual Area Features. Algorithms for Transformations of a Set of Area Features. Algorithms for Displacement of Features. Algorithms for Transformations of Three-Dimensional Surfaces and Features. Epilogue.
