Geoscience Reference
In-Depth Information
has coincided with (and built upon) the development of Web 2.0 technologies. Such technologi-
cal advances have altered the GeoWeb paradigm, from enabling people to consume spatial data to
empowering people to contribute it. Moreover, the GeoWeb now provides a way to organise and
share information, and developments relating to crowdsourcing and user interaction are providing a
means to build and maintain virtual communities and alter how information is produced and con-
sumed (Scharl, 2007). In a sense, these advances are bringing us closer to the Digital Earth vision
and are allowing us to build richer and more realistic virtual worlds.
As we have discussed previously, the GeoWeb is providing a means for the broader dissemination
of and access to locational data. However, we also need to consider issues relating to privacy with
regard to the availability and exploitation of personal content of locational information (Elwood,
2010; Elwood and Leszczynski, 2011). This is especially acute with the explosive growth in social
media. For example, in 2011, Twitter users were posting approximately 200 million tweets per
day (Twitter, 2011). A year later, this doubled to 400 million (Forbes, 2012), reaching a world-
wide rate of over 270,000 tweets per minute. At the same time, Flickr users upload in excess of
3000 images per minute (Sapiro, 2011), and YouTube users upload approximately 72 h of video per
minute (YouTube, 2013). These are remarkable volumes of user-generated data, signifying the shift
that has occurred in recent years in digital data production. While in the past, established govern-
ment or commercial organisations were responsible for generating most of the digital data, today, it
is estimated that approximately 75% of all digital data are contributed by individual users (Mearian,
2011). This trend in data growth is expected to become even more significant over the next several
years (Hollis, 2011), as computing and technological advances are solidifying the role of the general
public as the main contributor and consumer of big data, which has the potential to revolutionise
SDIs for scientific purposes (Craglia et al., 2008).
Coincident with these trends is the proliferation of location-aware devices (as we have dis-
cussed earlier). One could argue that all human artefacts have a location (Scharl, 2007), and
even in the Web 1.0 world, 20% of Web pages had geographic identifiers (Delboni et al., 2005),
but this is being changed substantially as people are using their smartphones or tablets to post
information on the Web. This means that a large portion of user-generated content contributed
through Web services is geolocated, thus fostering the emergence of a new type of geographical
information: user-generated, geolocated (or georeferenced) multimedia feeds of diverse thematic
content. These feeds are of diverse value, because they are expressions of geo-journalism, con-
veying current information about significant events, ranging from political movements and upris-
ings (e.g. Pollock, 2011; Stefanidis et al., 2013b) to natural disasters (e.g. Crooks et al., 2013).
These feeds also communicate users' opinions and views (Bruns and Burgess 2011; Tumasjan
et al., 2011) or even communicate their experiential perception of the space around them (as in
the concept of urban imaginaries of Kelley, 2013).
As a result, social media feeds are becoming increasingly geosocial in the sense that they
often have a substantial geographical content. This can take the form of coordinates from which
the contributions originate or of references to specific locations. At the same time, information
on the underlying social structure of the user community can be derived by studying the interac-
tions between users (e.g. formed as they respond to, or follow, other users), and this information
can provide additional context to the data analysis. Geosocial media therefore emerges as an
alternative form of geographical information, which, through its volume and richness, opens
new avenues and research challenges for the understanding of dynamic events and situations
(Croitoru et al., 2013).
Looking forward, one can expect to see a growth in AR applications tied to specific locations,
especially with the future release of Google Glass and similar products. Currently there is much
development work in AR where information is overlaid onto our view of the physical world, but
this is likely to become more widespread as mobile phones and other technologies become more
ubiquitous. This will add another dimension to the GeoWeb by providing a special kind of loca-
tion-based mashup (Anand et al., 2010). For example, Figure 4.11 shows UCLive, an AR map of
