Geography Reference
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Second, we can now collect important information more frequently using active
and passive sensors working at a frequency of years, months, days, or even
seconds and milliseconds to detect different parameters that change at different
speeds according to their physical nature. For example, biological cells change in
milliseconds, temperature will change hourly and the Earth's elevation changes at a
rate of years to millions of years without human intervention.
Third, we have sensors of different spatial resolution to detect various phenomena
for different usage. In weather forecasting, the resolution generally is at the
kilometer level in order to study the global circulation of wind, heat flux, and water,
and to assist individual decision making. On the other hand, the study of water
contamination for public health purposes requires microscopic observation (Hart
and Martinez 2006 ).
Fourth, sensors with different spectral capabilities were developed to collect a
wide range of spectral resolution and scope coverage from nanometer to millimeter.
Studies were conducted to better characterize different species in ecological studies
and minerals in geological studies based on data collected using hyper-spectral
instruments (Hart and Martinez 2006 ).
Fifth, the growth of sensors in resolution of space, time, spectral, and biological
scope has greatly increased our capabilities to observe the Earth surface and
provided greater support to enhance understanding and, eventually, the prediction of
different geographic phenomena (Akyildiz et al. 2002 ). We have increased weather
forecasting scope/resolution from several hours to 10 days and from state/provinces
to cities and zip code level with good accuracy on a global extent. Citizens as sensors
have also provided significant amount of data at petabytes per day level through
social media and other contemporary technologies, such as smart phones.
These observational or simulation datasets are of great value to different domains
of human scientific quests and application developments because they provide
baseline snapshot data about the Earth at specific times. For example, the datasets
can be (1) managed and utilized to build a Digital Earth (Yang et al. 2008 ),
(2) integrated at different levels for emergency response, such as for hurricane
Sandy, and (3) integrated or fed into complex models for better understanding
of geographic phenomena which will lead to make better informed predictions
regarding issues such as long term climate change, which can better aid policy
making (Yang et al. 2010 ). Further, the analyses of social media data can provide
critical information that may not be available through other methods - for example,
people twit breakouts of disease, social events, and plan for activities.
Recognizing the demand for big data and their dispersal, various domains and
communities, such as the Global Earth Observation (GEO), have been established
to share, process, and utilize big data for extracting valuable information and knowl-
edge. Big data will help us gain new insights about the geographic phenomena, and
further our understanding of geographic phenomena for making better decisions.
However, handling big data poses grand challenges for computing, this chapter
introduces through several projects the computing challenges of big data handling
and potential solutions for these challenges.
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