Geoscience Reference
In-Depth Information
Leeds. Two years earlier, the School of Geography in Leeds had created a Centre for Computational
Geography. The original intention was to develop a new computational paradigm for doing human
geography, but subsequently the human focus was de-emphasised as it became readily apparent
that it was equally applicable to many areas of physical geography. Openshaw (1994a,b, 1995a)
describes various attempts at devising a computational human geography (CHG) research agenda.
However, a major
difficulty
was that the ideas and methodologies being advocated in CHG seemed
to be far more attractive to physical geographers than to human geographers! This
difficulty
is
neatly and instantly resolved by using the term GeoComputation. The new word is also a useful
device for further broadening the attractions of a computational paradigm. However, the study of
earth systems is now of interest to a whole host of disciplines, many of which share common inter-
ests and common methodologies. Would they now feel left out in the development of computational
geography? The words
computational geography
were just too parochial and restricting. It also
limited the scope of the computational to geographers and would have excluded other disciplines
that may have wanted to be involved because they too study
geo
-contexts. Maybe only a non-
geographer would have dared say as much. So it was an
ice geographer
(Tavi Murray) who (either
deliberately or accidentally - which is now lost in the coffee-flavoured mists of time) invented the
term GeoComputation as a more meaningful alternative to
computational geography
. At a stroke,
she changed the original words
computational human geography
into something far more general
(GeoComputation) that was instantly understood and which applied equally to many physical and
human phenomena and was also inherently multidisciplinary. The subsequent use of a capital C in
the middle of the word GeoComputation can be attributed to Bob Abrahart. It is designed to empha-
sise the importance of the computation component and hence emphasise this very distinctive char-
acteristic. It is a pity that the irst topic on GC dropped the upper-case middle C; it is said because
the publishers did not like it! Here, we think that it is of sufficient importance as a distinctive logo
and trademark and have put it back!
1.3 SO WHAT IS DISTINCTIVE ABOUT GEOCOMPUTATION?
GC is not just the application of computers in geography nor is it just about computation for its own
sake. It is meant to imply the adoption of a large-scale computationally intensive scientific paradigm
as a tool for doing all manner of geographical research. Some will now claim they have been doing
GC for 10 or 30 years or more. This is certainly possible, but if they were then, until 1996, it was
certainly called something else; terms such as mathematical modelling, simulation and statistical
modelling all spring to mind.
There are three aspects which make GC special. Firstly, there is an emphasis on the
geo
subjects.
This is partly a disciplinary focus to the areas of interest, but it is more than geography. GC is con-
cerned with geographical or spatial information of all types but until recently the distinctiveness
of geographical data had been lost. In much of the quantitative work in geography, the geo-aspects
were either completely missing or underdeveloped and underemphasised. It may now appear really
weird that so many of the quantitative methods used in geography were (and still are) geo-poor!
Somehow the
geo
was left out, except as a description of the data source being used. Geographical
data were, it seems, the same as any other data, and methods used in the more advanced physi-
cal sciences could be imported unchanged into geography. Indeed they were, and this provided
the basis for the quantitative revolution in geography, a process that lasted about 30 years (from
the early 1960s onward) and saw the introduction of a whole host of statistical and mathematical
analysis and modelling methods. Unfortunately, many geographers were slow to realise the unique
and special features of geographical data that limited or rendered invalid many of these early quan-
titative and science-based tools. Mention can be made here that spatial data constitute populations
(rather than samples) of spatially dependent (rather than independent) data. Likewise, the lack of
applicability of stationarity assumptions and the substitution of the local for the global all mas-
sively complicate geographical study. Gradually as data environments became richer and computers