Spatial Big Data Science

Zhe Jiang, Shashi Shekhar

ISBN 10: 3319601946 / ISBN 13: 9783319601946

Published by Springer International Publishing AG, CH, 2017

About this Item

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 # LU-9783319601946

Bibliographic Details

• Title: Spatial Big Data Science
• Publisher: Springer International Publishing AG, CH
• Publication Date: 2017
• Language: English
• ISBN 10: 3319601946
• ISBN 13: 9783319601946
• Binding: Hardback
• Edition: 1st ed. 2017.
• Condition: New
• Dimensions: 15.88W x 1.27H x 23.5D centimeters 15.88 centimeters width by 1.27 centimeters height by 23.5 centimeters depth
• Weight: 431 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.