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E-raamat: Geographic Data Mining and Knowledge Discovery

3.67/5 (6 hinnangut Goodreads-ist)
Edited by (University of Utah, Salt Lake City, USA), Edited by (University of Illinois at Urbana-Champaign, USA)
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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.

Arvustused

" This book is about the rapidly growing field of geographic data miningsystematic 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

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.
University of Utah, Salt Lake City, USA University of Illinois at Urbana-Champaign, USA
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Püsilink: https://www.kriso.ee/db/97814200739802e.html
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