Geoscience Reference
In-Depth Information
cost. This latter concern is, however, changing through the development of open-source geographi-
cal information-related software (such as QGIS and R), enabling people to manipulate and analyse
data, just as OpenOffice allows people to use word and data packages.
More recently, VGI and the GeoWeb have played an important role in post-disaster response and
recovery mapping (e.g. Norheim-Hagtun and Meier, 2010; Zook et al., 2010) by utilising dedicated
GeoWeb services (such as OpenStreetMap or Google Map Maker). Not only can the public contrib-
ute information (e.g. Roche et al., 2013), but map mashups have been used to merge authoritative
and non-authoritative data in crisis situations (Goodchild, 2009). For example, Google Maps API
was used to display authoritative information of fires taken from RSS feeds from fire departments
in combination with VGI coming from citizens on the ground (Goodchild and Glennon, 2010; Liu
and Palen, 2010; Roche et al., 2013).
Some would argue that harnessing the power of the crowd reduces the burden of geographical
data collection. However, this new paradigm in which citizens are directly involved in the collection
and compiling of datasets is introducing new challenges with respect to data quality. While in the
past many geographical datasets were collected and compiled by central authoritative entities (e.g.
the Ordnance Survey of the United Kingdom) using highly controlled data collection practices and
data quality procedures, the very nature of VGI turns this on its head. Authors have already started
to assess the quality of VGI like OpenStreetMap, by comparing it to established authoritative map-
ping organisations such as the UK Ordnance Survey road datasets (Haklay, 2010) or exploring its
completeness and accuracy with respect to point features (Jackson et al., 2013).
One could consider VGI
good enough
for its purpose, especially in situations where it presents
the only reasonable means to collect information in a timely manner. In addition, some would
argue that OpenStreetMap like Wikipedia is a process of evolving a good product, not a complete
product in itself because there is no end goal in sight as to what constitutes the best map (or the
best entry in the case of Wikipedia; see Hudson-Smith et al., 2009b). Moreover, while access to the
Internet is increasing, not all areas will participate in data collection, as it is dependent on access
to the Internet. This resurfaces questions relating to the digital divide (the
haves
and
have nots
; see
Longley et al., 2006, and Buys et al., 2009).
In conjunction with the emergence of VGI, we are witnessing an explosion of geographical infor-
mation from social media (e.g. Twitter, Flickr). This brings forward a different model of geographi-
cal information contribution. While it is still true that users actively contribute geographical data, a
new model is emerging where the users' intent is not to directly contribute geographical data (e.g.
a map) but rather contribute information (e.g. a geotagged picture from a family vacation or text
describing a planned or unplanned event such as a flood) that happens to have an associated geo-
graphical component and a corresponding footprint. Harvesting and analysing such ambient geo-
graphical information (AGI, Stefanidis et al., 2013a) represents a substantial challenge needing new
skill sets as it resides at the intersection of disciplines like geography, computational social sciences,
linguistics and computer science. Nevertheless, it can provide us with unparalleled insight on a
broad variety of cultural, societal and human factors, particularly as they relate to human and social
dynamics in a networked society (Scharl and Tochtermann, 2007). For example, in Figure 4.3, we
show a screenshot from GeoSocial Gauge (2014) showing live updates of Twitter traffic associated
with President Obama. It comprises four views of the streaming data. On the top left, we have a map
with overlaid icons indicating the origin locations of social media feeds (tweets in this case) and a
corresponding heat map. On the top right, we display streaming social media feeds, harvested from
the Twitter API which are coloured according to sentiment (light grey for positive, dark grey for
negative). On the bottom left, we show a word cloud representation of the content of these messages.
Finally, on the bottom right, we show a rank of the origin locations of these contributions. At the
same time, this growth in geographical information at the personal level also raises issues of privacy
(see Elwood and Leszczynski, 2011).
The role of crowdsourcing and the GeoWeb is a move from a one-to-many, top-down, authori-
tative means of collecting and distributing geographical information, which was analogous with
