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Synopsis
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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- PublisherSpringer
- Publication date2018
- ISBN 10 3319868020
- ISBN 13 9783319868028
- BindingPaperback
- LanguageEnglish
- Number of pages146
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Spatial Big Data Science
Published by
Springer International Publishing, 2018
ISBN 10: 3319868020
ISBN 13: 9783319868028
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Kartoniert / Broschiert
Seller: moluna, Greven, Germany
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces four unique properties related to the nature of spatial data that must be accounted for in any data analysisCovers Spatial AutocorrelationDiscusses Spatial Dependency in Multiple Spatial ScalesEmerging Spatial Big Data. Seller Inventory # 458627094
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Spatial Big Data Science
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Springer, Berlin, Springer International Publishing, Springer Aug 2018, 2018
ISBN 10: 3319868020
ISBN 13: 9783319868028
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Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 131 pp. Englisch. Seller Inventory # 9783319868028
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Spatial Big Data Science : Classification Techniques for Earth Observation Imagery
Published by
Springer, Berlin, Springer International Publishing, Springer, 2018
ISBN 10: 3319868020
ISBN 13: 9783319868028
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783319868028
Quantity: 2 available
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Spatial Big Data Science: Classification Techniques for Earth Observation Imagery
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Paperback. Condition: Brand New. reprint edition. 131 pages. 9.25x6.10x0.34 inches. In Stock. Seller Inventory # 3319868020
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Spatial Big Data Science : Classification Techniques for Earth Observation Imagery
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