Social Sensing and Big Data Computing for Disaster Management
Social Sensing and Big Data Computing for Disaster Management

Author: Zhenlong Li

Publisher: Routledge

Published: 2020-12-17

Total Pages: 205

ISBN-13: 1000261492

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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.

Social Sensing and Big Data Computing for Disaster Management
Social Sensing and Big Data Computing for Disaster Management

Author: Zhenlong Li

Publisher: Routledge

Published: 2020-12-17

Total Pages: 233

ISBN-13: 1000261530

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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.

Big Data in Emergency Management: Exploitation Techniques for Social and Mobile Data
Big Data in Emergency Management: Exploitation Techniques for Social and Mobile Data

Author: Rajendra Akerkar

Publisher: Springer Nature

Published: 2020-09-14

Total Pages: 194

ISBN-13: 3030480992

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This contributed volume discusses essential topics and the fundamentals for Big Data Emergency Management and primarily focusses on the application of Big Data for Emergency Management. It walks the reader through the state of the art, in different facets of the big disaster data field. This includes many elements that are important for these technologies to have real-world impact. This book brings together different computational techniques from: machine learning, communication network analysis, natural language processing, knowledge graphs, data mining, and information visualization, aiming at methods that are typically used for processing big emergency data. This book also provides authoritative insights and highlights valuable lessons by distinguished authors, who are leaders in this field. Emergencies are severe, large-scale, non-routine events that disrupt the normal functioning of a community or a society, causing widespread and overwhelming losses and impacts. Emergency Management is the process of planning and taking actions to minimize the social and physical impact of emergencies and reduces the community’s vulnerability to the consequences of emergencies. Information exchange before, during and after the disaster periods can greatly reduce the losses caused by the emergency. This allows people to make better use of the available resources, such as relief materials and medical supplies. It also provides a channel through which reports on casualties and losses in each affected area, can be delivered expeditiously. Big Data-Driven Emergency Management refers to applying advanced data collection and analysis technologies to achieve more effective and responsive decision-making during emergencies. Researchers, engineers and computer scientists working in Big Data Emergency Management, who need to deal with large and complex sets of data will want to purchase this book. Advanced-level students interested in data-driven emergency/crisis/disaster management will also want to purchase this book as a study guide.

Social Sensing
Social Sensing

Author: Dong Wang

Publisher: Morgan Kaufmann

Published: 2015-04-17

Total Pages: 232

ISBN-13: 0128011319

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Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book

Cloud Computing in Remote Sensing
Cloud Computing in Remote Sensing

Author: Lizhe Wang

Publisher: CRC Press

Published: 2019-07-11

Total Pages: 293

ISBN-13: 042994988X

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This book provides the users with quick and easy data acquisition, processing, storage and product generation services. It describes the entire life cycle of remote sensing data and builds an entire high performance remote sensing data processing system framework. It also develops a series of remote sensing data management and processing standards. Features: Covers remote sensing cloud computing Covers remote sensing data integration across distributed data centers Covers cloud storage based remote sensing data share service Covers high performance remote sensing data processing Covers distributed remote sensing products analysis

Big Data Computing for Geospatial Applications
Big Data Computing for Geospatial Applications

Author: Zhenlong Li

Publisher: MDPI

Published: 2020-11-23

Total Pages: 222

ISBN-13: 3039432443

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The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.

5th World Congress on Disaster Management: Volume II
5th World Congress on Disaster Management: Volume II

Author: S. Anand Babu

Publisher: Taylor & Francis

Published: 2022-09-21

Total Pages: 631

ISBN-13: 100082747X

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World Congress on Disaster Management (WCDM) brings researchers, policy makers and practitioners from around the world in the same platform to discuss various challenging issues of disaster risk management, enhance understanding of risks and advance actions for reducing risks and building resilience to disasters. The fifth WCDM deliberates on three critical issues that pose the most serious challenges as well as hold the best possible promise of building resilience to disasters. These are Technology, Finance, and Capacity. WCDM has emerged as the largest global conference on disaster management outside the UN system. The fifth WCDM was attended by more than 2500 scientists, professionals, policy makers and practitioners all around the world despite the prevalence of pandemic.

Big Crisis Data
Big Crisis Data

Author: Carlos Castillo

Publisher: Cambridge University Press

Published: 2016-07-04

Total Pages: 225

ISBN-13: 1107135761

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Social media is invaluable during crises like natural disasters, but difficult to analyze. This book shows how computer science can help.

Social Sensing
Social Sensing

Author: Dong Wang

Publisher:

Published: 2015

Total Pages: 0

ISBN-13:

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Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book.

Urban Analytics with Social Media Data
Urban Analytics with Social Media Data

Author: Tan Yigitcanlar

Publisher: CRC Press

Published: 2022-07-20

Total Pages: 437

ISBN-13: 1000599663

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The use of data science and urban analytics has become a defining feature of smart cities. This timely book is a clear guide to the use of social media data for urban analytics. The book presents the foundations of urban analytics with social media data, along with real-world applications and insights on the platforms we use today. It looks at social media analytics platforms, cyberphysical data analytics platforms, crowd detection platforms, City-as-a-Platform, and city-as-a-sensor for platform urbanism. The book provides examples to illustrate how we apply and analyse social media data to determine disaster severity, assist authorities with pandemic policy, and capture public perception of smart cities. This will be a useful reference for those involved with and researching social, data, and urban analytics and informatics.