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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
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.
An exploration of the benefits of cloud computing in geoscience
research and applications as well as future research directions,
Spatial Cloud Computing: A Practical Approach discusses the
essential elements of cloud computing and their advantages for
geoscience. Using practical examples, it details the geoscience
requirements of cloud computing, covers general procedures and
considerations when migrating geoscience applications onto cloud
services, and demonstrates how to deploy different applications.
The book discusses how to choose cloud services based on the
general cloud computing measurement criteria and cloud computing
cost models. The authors examine the readiness of cloud computing
to support geoscience applications using open source cloud software
solutions and commercial cloud services. They then review future
research and developments in data, computation, concurrency, and
spatiotemporal intensities of geosciences and how cloud service can
be leveraged to meet the challenges. They also introduce research
directions from the aspects of technology, vision, and social
dimensions. Spatial Cloud Computing: A Practical Approach a common
workflow for deploying geoscience applications and provides
references to the concepts, technical details, and operational
guidelines of cloud computing. These features and more give
developers, geoscientists, and IT professionals the information
required to make decisions about how to select and deploy cloud
services.
An exploration of the benefits of cloud computing in geoscience
research and applications as well as future research directions,
Spatial Cloud Computing: A Practical Approach discusses the
essential elements of cloud computing and their advantages for
geoscience. Using practical examples, it details the geoscience
requirements of cloud computing, covers general procedures and
considerations when migrating geoscience applications onto cloud
services, and demonstrates how to deploy different applications.
The book discusses how to choose cloud services based on the
general cloud computing measurement criteria and cloud computing
cost models. The authors examine the readiness of cloud computing
to support geoscience applications using open source cloud software
solutions and commercial cloud services. They then review future
research and developments in data, computation, concurrency, and
spatiotemporal intensities of geosciences and how cloud service can
be leveraged to meet the challenges. They also introduce research
directions from the aspects of technology, vision, and social
dimensions. Spatial Cloud Computing: A Practical Approach a common
workflow for deploying geoscience applications and provides
references to the concepts, technical details, and operational
guidelines of cloud computing. These features and more give
developers, geoscientists, and IT professionals the information
required to make decisions about how to select and deploy cloud
services.
Both environmental and human challenges, such as natural disasters,
require scientifically sound simulations of physical phenomena to
better understand the past and predict future trends for improved
decision support. However, such simulations pose great computing
challenges to both Earth and computer sciences. This book addresses
those challenges through a series of strategies. Adaptively-coupled
nested models are used to resolve the computational challenges and
enable the computability of dust storm forecasting by dividing the
large geographical area into multiple subdomains with much small
area. Cloud computing platforms are adopted and optimized through
spatiotemporal patterns to support loosely-coupled nested model
execution. This book also investigates and utilizes
interoperability technologies to facilitate data access, model
input integration, model coupling, and output dissemination and
utilization. The book provides a guide to address computing and
model interoperability issues that arise when performing scientific
model simulation.
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