This book by leading experts in the field provides readers with a wide range of applications and methods for spatial database management systems, and allows readers to gain hands-on experience with examples in the book. It balances theory (cutting-edge research) and practice (commercial trends) to provide a comprehensive and clear overview.Includes coverage of GIS application trends as spatial networks; discussion of spatial data mining; overview of OGIS standard spatial datatypes and operations; object-relational database framework applied in each chapter.For professionals in the field of Databases, Geographic Information Systems, Geography, Remote Sensing, Multimedia Information, Civil and Mechanical Engineering, Forestry, CAD/CAM, Health Informatics, and Natural Resource Management.

Shashi Shekhar, is a professor and the head of the Spatial Database Research Group in the Department of Computer Science at the University of Minnesota Sanjay Chawla is a Senior Technical Instructor with Vignette Corporation in Waltham, Massachusetts

Over the years it has become evident in many areas of computer applications that the functionality of database management systems has to be enlarged to include spatially referenced data. The study of spatial database management systems (SDBMS) is a step in the direction of providing models and algorithms for the efficient handling of data related to space.Spatial databases have now been an active area of research for over two decades. Their results, example, spatial multidimensional indexing, are being used in many different areas. The principle impetus for research in SDBMs comes from the needs of existing applications such as geographical information systems (GIS) and computer-aided design (CAD), as well as potential applications such as multimedia information systems, data warehousing, and NASA's earth observation system. These spatial applications have over one million existing users.Major players in the commercial database industry have products specifically designed to handle spatial data. These products include the spatial data engine (SDE), by Environment Systems Research Institute (ESRI); as well as spatial datablades for object-relational database servers from many vendors including Intergraph, Autodesk, Oracle, IBM and Informix. Research prototypes include Postgres, Geo2, and Paradise. The functionality provided by these systems includes a set of spatial data types such as the point, line, and polygon, and a set of spatial operations, including intersection, enclosure, and distance. An industry-wide standard set of spatial data types and operations has been developed by the Open Geographic Information Systems (OGIS) consortium. The spatial types and operations can be made a part of an object-relational query language such as SQL3. The performance enhancement provided by these systems includes a multidimensional spatial index and algorithms for spatial access methods, spatial range queries, and spatial joins.The integration of spatial data into traditional databases amounts to resolving many nontrivial issues at various levels. They range from deep ontological questions about the modeling of space, for example, whether it should be field based or object based, thus paralleling the wave-particle duality in physics to more mundane but important issues about file management. These diverse topics make research in SDBMS truly interdisciplinary.Let us use the example of a country dataset to highlight the special needs of spatial databases. A country has at least one nonspatial datum, its name, and one spatial datum, its boundary. There is no ambiguity about storing or representing its name, but unfortunately it is not true for its boundary. Assuming that the boundary is represented as a collection of straight lines, we need to include a spatial data typelineand the companion typespointandregionin the database system to facilitate spatial queries on the objectcountry.These new data types need to be manipulated and composed according to some fixed rules leading to the creation of a spatial algebra. Because spatial data is inherently visual and usually voluminous, database systems have to be augmented to provide visual query processing and special spatial indexing . Other important database issues such as concurrency control, bulk loading, storage, and security have to be revisited and fine-tuned to build an effective spatial database management system.This book evolved from the class notes of a graduate course on Scientific Databases (Csci 8705) in University of Minnesota. Researchers and students both within and outside the Computer Science Department found the course very useful and applicable to their work. Despite the good response and high level of interest in the topic, no textbook available in the market was able to meet the interdisciplinary needs of the audience. A recent book by Scholl et al., 2001 focuses on traditional topics related to query langu