Geoscience Reference
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16.4 PROBLEMS AND PROSPECTS: TOWARDS OPEN GEOCOMPUTATION
The explosive development of ubicomp during the past two decades has created unprecedented
opportunities and daunting challenges for GC. This section reviews GC's problems and prospects
in the age of ubicomp.
16.4.1 u BicoMP and S Patial B ig d ata
Ubicomp has contributed to what is popularly known as the big data deluge. Because an increasing
number of data carry spatial and temporal tags, GC must explore creative ways to deal with spatial
big data (Shekhar et al., 2012). The spatial big data deluge is of particular concern primarily because
of the three Vs : variety, volume and velocity (Janowicz, 2012). Big data is not only about the great
variety and large volume but also the speed at which data are created and updated. The three Vs of
spatial big data pose formidable technical challenges for GC in the coming decade.
Until recently, the geospatial community has had a rather narrow definition of what is considered
geographic data or information, often heavily influenced by the legacy of traditional cartography.
But rapid advances in a plethora of technologies - GPS, smartphones, sensor networks, cloud com-
puting, etc., especially all of the technologies loosely called Web 2.0 - have radically transformed
how geographic data are collected, stored, disseminated, analysed, visualised and used (Chee and
Franklin, 2010). This trend is best reflected in Google's mantra that 'Google maps = Google in
maps' (Ron, 2008). The insertion of an in between Google and maps perhaps signifies one of the
most fundamental changes in the history of human mapping efforts. Nowadays, users can search
though Google maps not only for traditional spatial/map information but also for almost any kind
of digital information (such as Wikipedia entries, Flickr photos, YouTube videos and Facebook/
Twitter postings) as long as it is geotagged. Furthermore, in contrast to the traditional top-down
authoritative process of geographic data production by government agencies, citizens have played
an increasingly important role in producing geographic data of all kinds through a bottom-up
crowdsourcing process. As a result, we now have a great variety of geocoded data growing on a
daily basis from molecular to global scales covering almost everything we can think of on or near
the Earth's surface.
Due to the ubiquity of information-sensing mobile devices, aerial sensory technologies (remote
sensing), software logs, cameras, RFID readers, wireless sensor networks and other types of data-
gathering devices, 1-5 EB (1 EB = 10 18 B) of data is created daily and 90% of the data in the world
today were created within the past 2 years (MacIve, 2010). The amount of data humanity creates
is doubling every 2 years; 2010 is the first year that we reached 1 ZB (10 21 B), while in 2011 alone,
the world generated approximately 1.8 ZB of data. The explosive growth of big data is rapidly
transforming all aspects of governments, businesses, education and science. By 2020, the volume of
the world's data will increase by 50 times from today's volume (Gantz and Reinsel, 2011). We will
need 75 times more IT-related infrastructure in general and 10 times more servers to handle the new
data. Metaphors of data storage have evolved from bank, to warehouse, to portal and now to cloud.
Data storage cost has dropped dramatically during the past two decades. Between 2005 and 2011
alone, costs of storage dropped by 5/6. Not surprisingly, how to deal with the new reality of big data
tops the agendas of governments, industry and multiple disciplines in the academy (IWGDD, 2009;
CORDIS, 2010).
Although it is a challenging task to estimate the precise volume of geospatial data out there, we
can safely say geospatial data are becoming an important part of the big data torrent. Geospatial
information in general and volunteered geographic information (VGI) in particular should be
understood in the context of big data. Crowdsourcing, the Internet of Things and big data are
rapidly converging in the domain of geospatial technologies (Ball, 2011). Of course, due to rapid
technological advances, what is considered big or small is a moving target. In the McKinsey report
(Manyika et al., 2011), personal location data have been singled out as one of the five primary
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