Spatial Big Data Science

Zhe Jiang

ISBN 10: 3319868020 / ISBN 13: 9783319868028

Published by Springer, Berlin, Springer International Publishing, Springer Aug 2018, 2018

About this Item

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

Bibliographic Details

• Title: Spatial Big Data Science
• Publisher: Springer, Berlin, Springer International Publishing, Springer Aug 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 3319868020
• ISBN 13: 9783319868028
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 237W x 156H x 9D millimeters 237 millimeters width by 156 millimeters height by 9 millimeters depth
• Weight: 237 grams

About this title

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.