Geographic data mining and knowledge discovery
著者
書誌事項
Geographic data mining and knowledge discovery
（Chapman & Hall/CRC data mining and knowledge discovery series）
Chapman & Hall/CRC, c2009
2nd ed
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注記
Previous ed.: 2001
内容説明・目次
内容説明
The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal Databases
Since the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (GVis) have been developed. In addition, there has been a rise in the use of knowledge discovery techniques due to the increasing collection and storage of data on spatiotemporal processes and mobile objects. Incorporating these novel developments, this second edition reflects the current state of the art in the field.
New to the Second Edition
Updated material on geographic knowledge discovery (GKD), GDW research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, the INGENS 2.0 software, and GVis techniques
New chapter on data quality issues in GKD
New chapter that presents a tree-based partition querying methodology for medoid computation in large spatial databases
New chapter that discusses the use of geographically weighted regression as an exploratory technique
New chapter that gives an integrated approach to multivariate analysis and geovisualization
Five new chapters on knowledge discovery from spatiotemporal and mobile objects databases
Geographic data mining and knowledge discovery is a promising young discipline with many challenging research problems. This book shows that this area represents an important direction in the development of a new generation of spatial analysis tools for data-rich environments. Exploring various problems and possible solutions, it will motivate researchers to develop new methods and applications in this emerging field.
目次
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.
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