Boca Raton: Taylor & Francis Group. - 2007. 308 p. With the widespread use of GIS, multi-scale representation has taken center stage 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 о class of line features including contours, hydrographic (river) networks, and transportation networks, The author also addresses algorithms for individual area features, a class of area features, and various displacement operations. The final chapter briefly covers algorithms for 3-D surfaces and 3-D features. Providing a thorough treatment of low-level algorithms. Algorithmic Foundation of Multi-Scale Spatial Representation supplies the mathematical groundwork for multi-scale representations of spatial data. Features Provides comprehensive coverage of the algorithms needed to achieve multi-scale representations Covers the low-level algorithms available for the multi-scale representations of different types of spatial features Discusses multi-scale representation of 3-D surfaces and 3-D features Includes the most recent research results, such as roster-based work, algorithm improvements, snakes, wavelets, and empirical mode decomposition Contains over 250 illustrations to supplement the discussion Contents Introduction 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 Transformations 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 Index
Чтобы скачать этот файл зарегистрируйтесь и/или войдите на сайт используя форму сверху.
Монография, Springer-Verlag, Berlin, 2010. — 828 p. — ISBN: 978-3-642-03646-0.
GI Software Tools
Spatial Statistics in ArcGIS
Spatial Statistics in SAS
Spatial Econometric Functions in R
GeoDa: An Introduction to Spatial Data Analysis
STARS: Space-Time Analysis of Regional Systems
Space-Time Intelligence System Software for the Analysis of...
Springer, 2012. — 1130 p. 600 illus. in color. — ISBN: 3540726780, 9783540726807
Computer science provides a powerful tool that was virtually unknown three generations ago. Some of the classical fields of knowledge are geodesy (surveying), cartography, and geography. Electronics have revolutionized geodetic methods. Cartography has faced the dominance of the computer that...