Spatial Data Mining

Theory and Application

  • Deren Li
  • Shuliang Wang
  • Deyi Li

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Deren Li, Shuliang Wang, Deyi Li
    Pages 1-22
  3. Deren Li, Shuliang Wang, Deyi Li
    Pages 23-55
  4. Deren Li, Shuliang Wang, Deyi Li
    Pages 57-118
  5. Deren Li, Shuliang Wang, Deyi Li
    Pages 119-155
  6. Deren Li, Shuliang Wang, Deyi Li
    Pages 157-173
  7. Deren Li, Shuliang Wang, Deyi Li
    Pages 175-185
  8. Deren Li, Shuliang Wang, Deyi Li
    Pages 187-201
  9. Deren Li, Shuliang Wang, Deyi Li
    Pages 203-256
  10. Deren Li, Shuliang Wang, Deyi Li
    Pages 257-297
  11. Deren Li, Shuliang Wang, Deyi Li
    Pages 299-308

About this book

Introduction

·        This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project ‘the Belt and Road Initiatives’.








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Keywords

Spatial data mining Data field Cloud model Big data clustering GIS data mining Remote sensing image mininig Spatiotemporal video data mining

Authors and affiliations

  • Deren Li
    • 1
  • Shuliang Wang
    • 2
  • Deyi Li
    • 3
  1. 1.Wuhan UniversityWuhanChina
  2. 2.School of softwareBeijing Institute of TechnologyBeijingChina
  3. 3.Tsinghua UniversityBeijingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-48538-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2015
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-662-48536-1
  • Online ISBN 978-3-662-48538-5
  • About this book
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