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Zusatztext "? This book is about the rapidly growing field of geographic data mining-systematic procedures for searching through these vast resources in support of science! intelligence-gathering! and decision-making. It includes chapters on new methods of visualization and statistical analysis that together can produce new geographic knowledge out of the vast unorganized morass of information that is now available to us. This second edition of a work that first appeared in 2001 gives an essential and detailed update on developments in a rapidly advancing field."-Michael Goodchild! University of California! Santa Barbara! USA Informationen zum Autor Jiawei Han, Harvey J. Miller Klappentext This second edition includes updated material on geographic knowledge discovery, geographic data warehouse research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, INGENS 2.0, and geovisualization techniques. Recognizing the growth in mobile technologies and trajectory data, this edition provides five new chapters on knowledge discovery from spatiotemporal and mobile objects databases. It also contains new chapters on data quality issues, medoid computation, geographically weighted regression, and an integrated approach to multivariate analysis and geovisualization. Zusammenfassung Includes material on geographic knowledge discovery, geographic data warehouse research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, INGENS 2.0 and geovisualization techniques. This title provides chapters on knowledge discovery from spatiotemporal and mobile objects databases. Inhaltsverzeichnis Introduction. Spatiotemporal Data Mining Paradigms and Methodologies. Fundamentals of Spatial Data Warehousing for Geographic Knowledge Discovery. Analysis of Spatial Data with Map Cubes: Highway Traffic Data. Data Quality Issues and Geographic Knowledge Discovery. Spatial Classification and Prediction Models for Geospatial Data Mining. An Overview of Clustering Methods in Geographic Data Analysis. Computing Medoids in Large Spatial Datasets. Looking for a Relationship? Try GWR. Leveraging the Power of Spatial Data Mining to Enhance the Applicability of GIS Technology. Visual Exploration and Explanation in Geography: Analysis with Light. Multivariate Spatial Clustering and Geovisualization. Toward Knowledge Discovery about Geographic Dynamics in Spatiotemporal Databases. The Role of a Multitier Ontological Framework in Reasoning to Discover Meaningful Patterns of Sustainable Mobility. Periodic Pattern Discovery from Trajectories of Moving Objects. Decentralized Spatial Data Mining for Geosensor Networks. Beyond Exploratory Visualization of Space-Time Paths. ...
