Overview
- Introduces four unique properties related to the nature of spatial data that must be accounted for in any data analysis
- Covers Spatial Autocorrelation
- Discusses Spatial Dependency in Multiple Spatial Scales
- Includes supplementary material: sn.pub/extras
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About this book
Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book.
This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed.
This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.
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Table of contents (7 chapters)
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Front Matter
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Overview of Spatial Big Data Science
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Front Matter
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Classification of Earth Observation Imagery Big Data
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Front Matter
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Future Research Needs
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Front Matter
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Authors and Affiliations
Bibliographic Information
Book Title: Spatial Big Data Science
Book Subtitle: Classification Techniques for Earth Observation Imagery
Authors: Zhe Jiang, Shashi Shekhar
DOI: https://doi.org/10.1007/978-3-319-60195-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-60194-6Published: 21 July 2017
Softcover ISBN: 978-3-319-86802-8Published: 04 August 2018
eBook ISBN: 978-3-319-60195-3Published: 13 July 2017
Edition Number: 1
Number of Pages: XV, 131
Number of Illustrations: 10 b/w illustrations, 27 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Remote Sensing/Photogrammetry, Earth System Sciences