Ebook: The design and analysis of spatial data structures

Spatial data consist of points, lines, rectangles, regions, surfaces, and volumes. The representation of such data is becoming increasingly important in applications in computer graphics, computer vision, database management systems, computer-aided design, solid modeling, robotics, geographic information systems (GIS), image processing, computational geometry, pattern recognition, and other areas. Once an application has been specified, it is common for the spatial data types to be more precise. For example, consider a geographic information system (GIS). In such a case, line data are differentiated on the basis of whether the lines are isolated (e.g., earthquake faults), elements of tree-like structures (e.g., rivers and their tributaries), or elements of networks (e.g., rail and highway systems). Similarly region data are often in the form of polygons that are isolated (e.g., lakes), adjacent (e.g., nations), or nested (e.g., contours). Clearly the variations are large.