Geographic Data Mining and Knowledge Discovery (2nd Ed.) Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
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.
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.
Date de parution : 05-2009
15.6x23.4 cm
Thèmes de Geographic Data Mining and Knowledge Discovery :
Mots-clés :
Geographic Knowledge Discovery; Spatial Data Mining; Space-Time Path Visualization; Data Sets; Geosensor Networks; Data Mining; Decentralized Spatial Data Mining; Data Cube; Periodic Pattern Discovery; Map Cube; Multitier Ontological Framework; Sustainable Mobility Patterns; Data Warehouses; Spatiotemporal Databases; Spatial Association Rules; Geovisualization; Spatio Temporal Database; Multivariate Spatial Clustering; MRF Model; GIS Technology; Spatial Data; ETL Process; GWR; Da Ta; Spatial Datasets; Data; Medoids; Visualization Component; Clustering Methods; Data Mining Tasks; Geographic Data Analysis; Geospatial Data Mining; National Spatial Data Infrastructure; Spatial Prediction Model; Geographic Dynamics; Spatial Classification; DBMS; SAR Model; Data Quality; Geographic Data Mining; Spatial Data Analysis; Spatial Data Warehousing; Local Extrapolation; Spatiotemporal Data Mining
