Overview
- Presents up-to-date work on core theories and applications of spatial data mining, combining the principles of data mining and geospatial information science
- Proposes data fields, cloud model, and mining views methods, and presents empirical applications in the context of GIS and remote sensing
- Explores spatiotemporal video data mining for protecting public security, and discerns the brightness of night time light images for evaluating the severity of the Syrian Crisis
- Honored as “a milestone of spatial data mining” in the book review of Science Bulletin, and won the Fifth Chinese Excellent Publications Award (Books) for the Chinese edition
- Includes supplementary material: sn.pub/extras
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Table of contents (10 chapters)
Keywords
About this book
· 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’.
Authors and Affiliations
About the authors
Shuliang Wang, PhD, a scientist in data science and software engineering, is a professor in Beijing Institute of Technology in China. His research interests include spatial data mining, and software engineering. For his innovatory study of spatial data mining, he was awarded the Fifth Annual InfoSci-Journals Excellence in Research Awards of IGI Global, IEEE Outstanding Contribution Award for Granular Computing, and one of China’s National Excellent Doctoral Thesis Prizes.
Deyi Li, PhD, a scientist in computer science and artificial intelligence, is the founder of cloud model. He is now a professor in Tsinghua University in China, a membership of Chinese Academy of Engineering and a membership of the Euro-Asia International Academy of Science. His research interests include networked data mining, artificial intelligence with uncertainty, cloud computing, and cognitive physics. For his contribution, he was awarded many international and national prizes or awards, e.g. the Premium Award by IEE Headquarters, the IFAC World Congress Outstanding Paper Award, National Science and Technology Progress Award and so on.
Bibliographic Information
Book Title: Spatial Data Mining
Book Subtitle: Theory and Application
Authors: Deren Li, Shuliang Wang, Deyi Li
DOI: https://doi.org/10.1007/978-3-662-48538-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2015
Hardcover ISBN: 978-3-662-48536-1Published: 05 April 2016
Softcover ISBN: 978-3-662-56936-8Published: 25 April 2018
eBook ISBN: 978-3-662-48538-5Published: 23 March 2016
Edition Number: 1
Number of Pages: XXVIII, 308
Number of Illustrations: 22 b/w illustrations, 81 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Remote Sensing/Photogrammetry, Artificial Intelligence