Geoscience Reference
In-Depth Information
items in less than 6 months. Such data have the potential to provide valuable fuel for many GC and
e-Research applications in the immediate future (e.g. Birkin and Malleson, 2011). In particular,
social data that have been sourced from crowds could be used to provide real-time input to social
simulation models to ensure that models remain up to date with current social trends. Real-time data
assimilation in this manner is commonplace in many hard-science fields (e.g. Collins, 2007) but is
yet to make headway in social simulation.
10.11 MULTIDIMENSIONAL VISUALISATION
One further way in which developments in computational processing have facilitated geospatial
applications is in allowing truly 4D representations of the landscape and built environment. High-
resolution images can now be constructed and resolved from multiple perspectives in order to cre-
ate the effect of movement in space and time. Current examples are largely experimental and to
some extent playful. For example, CASA has recently demonstrated a
Pigeon Sim
, in which users
are encouraged to emulate the local bird population in flying over and through the city's streets.
The future applications of these technologies to architecture and city planning are potentially
wide-ranging. Indeed, it can be argued that effective visualisation of both real and simulated envi-
ronments, albeit at a more modest scale, is already an important feature of the latest e-Research
applications, as in the spatial modelling workflows of Section 10.2 earlier.
Another important application of visualisation methods is to the problem of
viewsheds
, in which
the objective is to simulate the visibility of different objectives in a landscape. The Wilderness
Institute at the University of Leeds has pioneered applications of this type to understand the envi-
ronmental impact of wind turbines, particularly within the Scottish countryside.
What if
simulations
are also constructed to gauge the impact of an expansion of wind farms which are now a feature of
UK energy policy (Carver and Washtell, 2012).
10.12 CONCLUSIONS
Developments in computational technology have given rise to new VREs which look to be influ-
encing the scope of GC in a subtle but persistent way. These new VREs are exploiting compu-
tational power in order to support simulations which are increasingly at microscopic scales and
agent-based but deployed for massive populations of individuals; they can provide access to data
sets which are remotely distributed and capture intelligence from a dazzlingly wide array of ori-
gins, in which the emergence of crowdsourced material from the bottom up is an outstandingly
important trend. They also allow real-time visualisations in space and time which can support the
interpretation of both the data and the simulations, as well as providing a potentially novel research
track in its own right.
While the early VREs were based on grid technologies, a new
cloud
of data and applications is
emerging. Intelligent mobile devices with the ability to capture and process data are an important
feature of this cloud. The future for GC looks to be an exciting mix of micro-scale models and
simulations, which can be calibrated and validated to massive and increasingly diverse data sets and
whose results can be easily dispersed, shared and visualised.
REFERENCES
Adnan, M., Longley, P., Singleton, A. D., and Turton, I. 2014. Parallel computing in geography. In
GeoComputation
,
2nd
edn., eds. R.J. Abrahart and L. See, pp. 49-68. Boca Raton, FL: Taylor & Francis
Group.
Ashby, D. and Longley, P. 2005. Geocomputation, geodemographics and resource allocation for local policing.
Transactions in GIS
9:53-72.
Atkins, K., Marathe, A. and Barrett, C. 2007. A computational approach to modeling commodity markets.
Computational Economics
30(2):125-142.
