Geography Reference
In-Depth Information
aggregation and subset, make the information extraction extremely computational
intensive. For example, the computational and storage requirements for deriving
regional and global water, energy, and carbon conditions from multi-sensor and
multi-temporal datasets far exceed the capacity of a single computer (Kumar et al.
2006
). Usually, high performance computing (HPC), such as parallel computing,
grid computing and cloud computing, is needed to support the massive volume
of big data handling including increasingly sophisticated and complex geophysical
algorithms and models (Guan
2008
; Huang and Yang
2011
; Huang et al.
2012
).
18.2.4
Visualization
Visualizing 3D/4D environmental big data is critical to understand and predict
geographic phenomena for relevant decision making. Efforts of developing visu-
alization tools to support the analysis of dynamic geographic phenomena started
approximately a few decades ago (Hibbard et al.
1994
). Despite the developments
of geovisualization tools such as virtual globes, displaying the dynamic phenomena
represented by 3D/4D data remains a challenge due to the complexity and volume
of the data. As online visualization becomes a generic requirement for big data
in many occasions, it introduces more challenges on integrating, processing and
visualizing geospatial data in a web-based environment. Effective visualization
relies on efficient data organizations to facilitate fast access of these 3D/4D data,
advanced visualization algorithms to display the phenomena described by such data,
and state-of-the-art computing techniques to address the computational complexity
(Lietal.
2011a
). The advances in Graphics Processing Units (GPUs) have yielded
better rendering capacity as well as computing power, serving as ubiquitous and
affordable computing devices for big data applications (Stone et al.
2007
).
18.2.5
Applications
Geographic applications with massive data involved are complex. More than often,
the computing demands exceed the computing capacity and become a driver
for computing science advancements (Yang et al.
2011b
). Different computing
strategies should be adopted to speed up the computing process for different
applications (Huang et al.
2012
). For example, climate change analysis poses
grand challenges for computing sciences when we perform climate modeling and
simulation over global area for a long time interval, such as 10-200 years. In
addition, the initialization of such models requires different types of data from
different organizations and in different formats. To better address twenty-first
century geographic science and application challenges, cross cutting and interop-
erability solutions are required to integrate interdisciplinary data, information, and
knowledge for science advancements.
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