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© 2009

Knowledge Discovery in Spatial Data

Book

Part of the Advances in Spatial Science book series (ADVSPATIAL)

Table of contents

About this book

Introduction

This book deals with knowledge discovery and data mining in spatial and temporal data, seeking to present novel methods that can be employed to discover spatial structures and processes in complex data. Spatial knowledge discovery is examined through the tasks of clustering, classification, association/relationship, and process. Among the covered topics are discovery of spatial structures as natural clusters, identification of separation surfaces and extraction of classification rules from statistical and algorithmic perspectives, detecting local and global aspects of non-stationarity of spatial associations and relationships, unraveling scaling behaviors of time series data, including self-similarity, and long range dependence. Particular emphasis is placed on the treatment of scale, noise, imperfection and mixture distribution. Numerical examples and a wide scope of applications are used throughout the book to substantiate the conceptual and theoretical arguments.

Keywords

Algorithm Clustering Geographical Information System Knowledge Discovery Remote Sensing Spatial Data Mining classification data mining geographic data

Authors and affiliations

  1. 1.Dept. Geography &Chinese University of Hong KongShatin, New TerritoriesHong Kong/PR China

Bibliographic information

Reviews

From the reviews:

“A research monograph on methods and algorithms, which represents the author’s rich research experience and achievements. Such perspective provides an invaluable resource for advanced users. … it achieves its aim of providing thoughtful and provocative demonstrations on the issues of spatial knowledge discovery and data mining from the conceptual, theoretical and empirical points of view. … recommended for scholars in any discipline interested in the geographical dimensions of large data sets. … an up-to-date contribution to the field of spatial knowledge discovery and data mining.” (Xinyue Ye, Regional Studies, Vol. 45 (6), June, 2011)