The book is intended for researchers, practitioners, and students working in various fields and disciplines related to Geospatial Information Science and technology.
Spatial analysis and data mining are increasingly facing the challenges of Big Data as more and more types of crowd sourcing spatial data are used in GIScience, such as movement trajectories, cellular phone calls, and social networks. In order to effectively manage these massive data collections, new methods and algorithms are called for. The book highlights state-of-the-art advances in the handling and application of spatial data, especially spatial Big Data, offering a cutting-edge reference guide for graduate students, researchers and practitioners in the field of GIScience.
The work should also be considered as a major reference and as a review of the literature, since it includes tables of parameterizatlons, procedures, filed experiments, useful constants, and graphs of various phenomena under a variety of conditions. It is assumed that the work will be used at the beginning graduate level for students with an undergraduate background in meteorology, but the author envisions, and has catered for, a heterogeneity in the background and experience of his readers.
Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.
Topics include:Data Structures and AlgorithmsStrings and TextNumbers, Dates, and TimesIterators and GeneratorsFiles and I/OData Encoding and ProcessingFunctionsClasses and ObjectsMetaprogrammingModules and PackagesNetwork and Web ProgrammingConcurrencyUtility Scripting and System AdministrationTesting, Debugging, and ExceptionsC Extensions
Thoroughly updated with material related to the GRASS6, the third edition includes new sections on attribute database management and SQL support, vector networks analysis, lidar data processing and new graphical user interfaces. All chapters were updated with numerous practical examples using the first release of a comprehensive, state-of-the-art geospatial data set.
Open Source GIS: A GRASS GIS Approach (third edition) preserves the continuity of previous editions by maintaining the proven book’s structure and continues to target a professional audience composed of researchers and practitioners in government and industry as well as graduate students interested in geospatial analysis and modeling.