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Spatial Analysis Using Big Data

Spatial Analysis Using Big Data
Methods and Urban Applications

by Yoshiki Yamagata,Hajime Seya

  • Publisher : Academic Press
  • Release : 2019-11-03
  • Pages : 302
  • ISBN : 0128131322
  • Language : En, Es, Fr & De
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Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others. Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science Provides computer codes written in R, MATLAB and Python to help implement methods Applies these methods to common problems observed in urban and regional economics

Spatial Big Data, BIM and advanced GIS for Smart Transformation

Spatial Big Data, BIM and advanced GIS for Smart Transformation
City, Infrastructure and Construction

by Sara Shirowzhan,Willie Tan,Samad M. E. Sepasgozar

  • Publisher : MDPI
  • Release : 2020-12-02
  • Pages : 166
  • ISBN : 3039360302
  • Language : En, Es, Fr & De
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This book covers a range of topics including selective technologies and algorithms that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This book also presents an agenda for future investigations to address the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities. Some of selected specific tools presented in this book are as a simulator for improving the smart parking practices by modelling drivers with activity plans, a bike optimization algorithm to increase the efficiency of bike stations, an agent-based model simulation of human mobility with the use of mobile phone datasets. In addition, this book describes the use of numerical methods to match the network demand and supply of bicycles, investigate the distribution of railways using different indicators, presents a novel algorithm of direction-aware continuous moving K-nearest neighbor queries in road networks, and presents an efficient staged evacuation planning algorithm for multi-exit buildings.

Geographical Data Science and Spatial Data Analysis

Geographical Data Science and Spatial Data Analysis
An Introduction in R

by Lex Comber,Chris Brunsdon

  • Publisher : SAGE
  • Release : 2020-12-02
  • Pages : 360
  • ISBN : 1526485435
  • Language : En, Es, Fr & De
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We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (ie the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ text book, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.

Spatial Planning in the Big Data Revolution

Spatial Planning in the Big Data Revolution
A Book

by Voghera, Angioletta,La Riccia, Luigi

  • Publisher : IGI Global
  • Release : 2019-03-15
  • Pages : 359
  • ISBN : 1522579281
  • Language : En, Es, Fr & De
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Through interaction with other databases such as social media, geographic information systems have the ability to build and obtain not only statistics defined on the flows of people, things, and information but also on perceptions, impressions, and opinions about specific places, territories, and landscapes. It is thus necessary to systematize, integrate, and coordinate the various sources of data (especially open data) to allow more appropriate and complete analysis, descriptions, and elaborations. Spatial Planning in the Big Data Revolution is a critical scholarly resource that aims to bring together different methodologies that combine the potential of large data analysis with GIS applications in dedicated tools specifically for territorial, social, economic, environmental, transport, energy, real estate, and landscape evaluation. Additionally, the book addresses a number of fundamental objectives including the application of big data analysis in supporting territorial analysis, validating crowdsourcing and crowdmapping techniques, and disseminating information and community involvement. Urban planners, architects, researchers, academicians, professionals, and practitioners in such fields as computer science, data science, and business intelligence will benefit most from the research contained within this publication.

Spatial Analysis with R

Spatial Analysis with R
Statistics, Visualization, and Computational Methods

by Tonny J. Oyana

  • Publisher : CRC Press
  • Release : 2020-08-31
  • Pages : 334
  • ISBN : 1000173453
  • Language : En, Es, Fr & De
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In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data mining This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.

Spatial Data Handling in Big Data Era

Spatial Data Handling in Big Data Era
Select Papers from the 17th IGU Spatial Data Handling Symposium 2016

by Chenghu Zhou,Fenzhen Su,Francis Harvey,Jun Xu

  • Publisher : Springer
  • Release : 2017-05-04
  • Pages : 237
  • ISBN : 9811044244
  • Language : En, Es, Fr & De
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This proceedings volume introduces recent work on the storage, retrieval and visualization of spatial Big Data, data-intensive geospatial computing and related data quality issues. Further, it addresses traditional topics such as multi-scale spatial data representations, knowledge discovery, space-time modeling, and geological applications. Spatial analysis and data mining are increasingly facing the challenges of Big Data as more and more types of crowd sourcing spatial data are used in GIScience, such as movement trajectories, cellular phone calls, and social networks. In order to effectively manage these massive data collections, new methods and algorithms are called for. The book highlights state-of-the-art advances in the handling and application of spatial data, especially spatial Big Data, offering a cutting-edge reference guide for graduate students, researchers and practitioners in the field of GIScience.

An Introduction to R for Spatial Analysis and Mapping

An Introduction to R for Spatial Analysis and Mapping
A Book

by Chris Brunsdon,Lex Comber

  • Publisher : SAGE
  • Release : 2018-12-10
  • Pages : 336
  • ISBN : 1526454203
  • Language : En, Es, Fr & De
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This is a new edition of the accessible and student-friendly 'how to' for anyone using R for the first time, for use in spatial statistical analysis, geocomputation and digital mapping. The authors, once again, take readers from ‘zero to hero’, updating the now standard text to further enable practical R applications in GIS, spatial analyses, spatial statistics, web-scraping and more. Revised and updated, each chapter includes: example data and commands to explore hands-on; scripts and coding to exemplify specific functionality; self-contained exercises for students to work through; embedded code within the descriptive text. The new edition includes detailed discussion of new and emerging packages within R like sf, ggplot, tmap, making it the go to introduction for all researchers collecting and using data with location attached. This is the introduction to the use of R for spatial statistical analysis, geocomputation, and GIS for all researchers - regardless of discipline - collecting and using data with location attached.

Hierarchical Modeling and Analysis for Spatial Data, Second Edition

Hierarchical Modeling and Analysis for Spatial Data, Second Edition
A Book

by Sudipto Banerjee,Bradley P. Carlin,Alan E. Gelfand

  • Publisher : CRC Press
  • Release : 2014-09-12
  • Pages : 584
  • ISBN : 1439819173
  • Language : En, Es, Fr & De
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Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application. New to the Second Edition New chapter on spatial point patterns developed primarily from a modeling perspective New chapter on big data that shows how the predictive process handles reasonably large datasets New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling New chapter on the theoretical aspects of geostatistical (point-referenced) modeling Greatly expanded chapters on methods for multivariate and spatiotemporal modeling New special topics sections on data fusion/assimilation and spatial analysis for data on extremes Double the number of exercises Many more color figures integrated throughout the text Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages The Only Comprehensive Treatment of the Theory, Methods, and Software This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modeling for spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.

Cloud Computing for Geospatial Big Data Analytics

Cloud Computing for Geospatial Big Data Analytics
Intelligent Edge, Fog and Mist Computing

by Himansu Das,Rabindra K. Barik,Harishchandra Dubey,Diptendu Sinha Roy

  • Publisher : Springer
  • Release : 2018-12-11
  • Pages : 289
  • ISBN : 3030033597
  • Language : En, Es, Fr & De
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This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.

Big Data Applications in Geography and Planning

Big Data Applications in Geography and Planning
An Essential Companion

by Mark Birkin,Graham Clarke,Jonathan Corcoran,Robert Stimson

  • Publisher : Edward Elgar Publishing
  • Release : 2021-05-28
  • Pages : 288
  • ISBN : 1789909791
  • Language : En, Es, Fr & De
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This unique book demonstrates the utility of big data approaches in human geography and planning. Offering a carefully curated selection of case studies, it reveals how researchers are accessing big data, what this data looks like and how such data can offer new and important insights and knowledge.

Hands-On Geospatial Analysis with R and QGIS

Hands-On Geospatial Analysis with R and QGIS
A beginner’s guide to manipulating, managing, and analyzing spatial data using R and QGIS 3.2.2

by Shammunul Islam

  • Publisher : Packt Publishing Ltd
  • Release : 2018-11-30
  • Pages : 354
  • ISBN : 1788996984
  • Language : En, Es, Fr & De
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Practical examples with real-world projects in GIS, Remote sensing, Geospatial data management and Analysis using the R programming language Key Features Understand the basics of R and QGIS to work with GIS and remote sensing data Learn to manage, manipulate, and analyze spatial data using R and QGIS Apply machine learning algorithms to geospatial data using R and QGIS Book Description Managing spatial data has always been challenging and it's getting more complex as the size of data increases. Spatial data is actually big data and you need different tools and techniques to work your way around to model and create different workflows. R and QGIS have powerful features that can make this job easier. This book is your companion for applying machine learning algorithms on GIS and remote sensing data. You’ll start by gaining an understanding of the nature of spatial data and installing R and QGIS. Then, you’ll learn how to use different R packages to import, export, and visualize data, before doing the same in QGIS. Screenshots are included to ease your understanding. Moving on, you’ll learn about different aspects of managing and analyzing spatial data, before diving into advanced topics. You’ll create powerful data visualizations using ggplot2, ggmap, raster, and other packages of R. You’ll learn how to use QGIS 3.2.2 to visualize and manage (create, edit, and format) spatial data. Different types of spatial analysis are also covered using R. Finally, you’ll work with landslide data from Bangladesh to create a landslide susceptibility map using different machine learning algorithms. By reading this book, you’ll transition from being a beginner to an intermediate user of GIS and remote sensing data in no time. What you will learn Install R and QGIS Get familiar with the basics of R programming and QGIS Visualize quantitative and qualitative data to create maps Find out the basics of raster data and how to use them in R and QGIS Perform geoprocessing tasks and automate them using the graphical modeler of QGIS Apply different machine learning algorithms on satellite data for landslide susceptibility mapping and prediction Who this book is for This book is great for geographers, environmental scientists, statisticians, and every professional who deals with spatial data. If you want to learn how to handle GIS and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful but is not necessary.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics
A Book

by Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian

  • Publisher : IGI Global
  • Release : 2019-11-29
  • Pages : 355
  • ISBN : 1799811948
  • Language : En, Es, Fr & De
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Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Modern Technologies for Big Data Classification and Clustering

Modern Technologies for Big Data Classification and Clustering
A Book

by Seetha, Hari,Murty, M. Narasimha,Tripathy, B. K.

  • Publisher : IGI Global
  • Release : 2017-07-12
  • Pages : 360
  • ISBN : 1522528067
  • Language : En, Es, Fr & De
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Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.

Big Data in Cardiology. Predicting, Preventing and Managing Diseases

Big Data in Cardiology. Predicting, Preventing and Managing Diseases
A Book

by Bikal Dhungel

  • Publisher : GRIN Verlag
  • Release : 2020-10-28
  • Pages : 59
  • ISBN : 3346284379
  • Language : En, Es, Fr & De
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Master's Thesis from the year 2020 in the subject Health - Health Sciences - Health Logistics, grade: 1,7, Linnaeus University (School of Informatics), course: Information Systems, language: English, abstract: This study was conducted to analyze this process closer focusing on a case of Cardiology. Conducting a comprehensive literature review and qualitative expert interviews, the impact of big data in the field of Cardiology was explored. The result of the study shows that big data can play a positive role in three aspects: prediction of disease, prevention of disease and management of disease. Big data enables us to build models that can be used to predict the occurrence of disease. Based on this information, actions can be taken to prevent the disease. Data also helps to manage the disease by offering helpful insights. Medical personnel can retrieve the patient data, with the help of AI, they can make faster decisions allowing them to spend more quality time with the patients and reduce cognitive errors. Through the interviews, it was understood that even though the positive role of big data has been acknowledged, the implementation is still a challenge due to various limitations. The challenges lie mainly on technical know-how and domain knowledge. Further challenges were data security and privacy issues that need to be addressed to mitigate the risks that can be caused by them. The examples of big data implementation in various cases like in heart failure prediction or prevention shows a positive picture. The overwhelming majority of case studies analyzed in this regard show an optimistic picture. Due to growing importance and use of smart devices, IoT, genomics and the recent developments in the field of ICTs, it is expected that big data will not only leave a positive influence on the field of Cardiology, it will also change the way medicine is practiced and healthcare is offered. The statement ‘Data is the new oil’ has been broadly acknowledged due to its wide-ranging importance. Utilizing big data offers a variety of benefits. Although the health sector was late in terms of exploiting the benefits of big data, currently, the adoption is accelerating. Healthcare is increasingly becoming an information science and the implementation of electronic medical records (EMR) and other information systems is growing rapidly. The patient data originating from smart devices and other sources like genomic databases are supporting the healthcare sector offering better healthcare delivery and increasing efficiency, hence saving costs.

Big Data Analytics

Big Data Analytics
6th International Conference, BDA 2018, Warangal, India, December 18–21, 2018, Proceedings

by Anirban Mondal,Himanshu Gupta,Jaideep Srivastava,P. Krishna Reddy,D.V.L.N. Somayajulu

  • Publisher : Springer
  • Release : 2018-12-11
  • Pages : 424
  • ISBN : 3030047806
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining.

Big Data Analytics for Satellite Image Processing and Remote Sensing

Big Data Analytics for Satellite Image Processing and Remote Sensing
A Book

by Swarnalatha, P.,Sevugan, Prabu

  • Publisher : IGI Global
  • Release : 2018-03-09
  • Pages : 253
  • ISBN : 1522536442
  • Language : En, Es, Fr & De
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The scope of image processing and recognition has broadened due to the gap in scientific visualization. Thus, new imaging techniques have developed, and it is imperative to study this progression for optimal utilization. Big Data Analytics for Satellite Image Processing and Remote Sensing is a critical scholarly resource that examines the challenges and difficulties of implementing big data in image processing for remote sensing and related areas. Featuring coverage on a broad range of topics, such as distributed computing, parallel processing, and spatial data, this book is geared towards scientists, professionals, researchers, and academicians seeking current research on the use of big data analytics in satellite image processing and remote sensing.

Big Data and Visual Analytics

Big Data and Visual Analytics
A Book

by Sang C. Suh,Thomas Anthony

  • Publisher : Springer
  • Release : 2018-01-15
  • Pages : 263
  • ISBN : 331963917X
  • Language : En, Es, Fr & De
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This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.

Big Data Support of Urban Planning and Management

Big Data Support of Urban Planning and Management
The Experience in China

by Zhenjiang Shen,Miaoyi Li

  • Publisher : Springer
  • Release : 2017-09-26
  • Pages : 456
  • ISBN : 3319519298
  • Language : En, Es, Fr & De
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In the era of big data, this book explores the new challenges of urban-rural planning and management from a practical perspective based on a multidisciplinary project. Researchers as contributors to this book have accomplished their projects by using big data and relevant data mining technologies for investigating the possibilities of big data, such as that obtained through cell phones, social network systems and smart cards instead of conventional survey data for urban planning support. This book showcases active researchers who share their experiences and ideas on human mobility, accessibility and recognition of places, connectivity of transportation and urban structure in order to provide effective analytic and forecasting tools for smart city planning and design solutions in China.

Big Data in Engineering Applications

Big Data in Engineering Applications
A Book

by Sanjiban Sekhar Roy,Pijush Samui,Ravinesh Deo,Stavros Ntalampiras

  • Publisher : Springer
  • Release : 2018-05-02
  • Pages : 384
  • ISBN : 9811084769
  • Language : En, Es, Fr & De
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This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.

Social Sensing and Big Data Computing for Disaster Management

Social Sensing and Big Data Computing for Disaster Management
A Book

by Zhenlong Li,Qunying Huang

  • Publisher : Routledge
  • Release : 2020-11-23
  • Pages : 192
  • ISBN : 9780367617653
  • Language : En, Es, Fr & De
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Social Sensing and Big Data Computing for Disaster Management captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically, analysed within this book are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems. Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, and unreliable in some aspects. It comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion. This book was originally published as a special issue of the International Journal of Digital Earth.