criticisms ofpositivism, which have been used
as the stick to beat other areas of quantitative,
computational and spatial scientific human
geography? Not necessarily. For example, if
we accept the proposition thatvisualization
of pattern suggests insight into the processes
that generate that pattern (e.g. Batty and
Longley, 1994), and if the researcher goes
beyond what is empirically observable to ask
questions and form concepts about the more
fundamental structures and mechanisms for
the events or phenomena under study, then
the tenets ofrealismor critical realist philoso-
phies are approached (Danermark, Ekstro ̈m,
Jakobsen and Karlsson, 2001).
Is, then, Couclelis (1998, p. 22) still right
to say that geocomputation has ‘no [single]
philosophy (and proud of it!)’? Perhaps so.
Perhaps, desirably so. For, as computers and
computation develop and evolve, new oppor-
tunities are presented for innovative geograph-
ical problem solving, alternative expressions of
geographical enquiry and fresh geographical
theorization,explanationand understanding.
A few years ago the character of geocomputa-
tion could be conveyed by specifying what it
was not: it wasnotsimplygeographic infor-
mation systemsbut, rather, a reaction to the
(then) limited geometric data manipulations
and mapping capabilities offered by GIS.
What was sought was the flexibility for more
sophisticated and creative spatial statistical an-
alysis, data visualization, process modelling
and dynamic simulation that broke out of the
GIS straightjacket. These various domains of
geocomputation –spatial analysis, geovisua-
lization, geosimulation and the application
ofartificial intelligencefor geographical
problem solving and knowledge discovery –
still characterize geocomputation. But the
‘definition’ bycounterfactualhas begun to
age, asinteroperabilityand the ability to
customise GIS have led to more sophisticated
geocomputational methods to be implemen-
ted within a GIS environment (Maguire,
Batty and Goodchild, 2005).
Nevertheless, new technologies could also
yield a clearer identity for geocomputation.
Computational ‘grid’ technologies – an allusion
to electricity power grids – offer the opportunity
for researchers to ‘plug in’ to high-performance
computer networks under the rubric of ‘e-’
(electronic) social science. Martin (2005)
identifies four essential research issues for
e-social science: automateddata mining;
visualization of spatial data uncertainty; in-
corporation of an explicitly spatial dimension
into simulation modelling; and neighbour-
hoodclassification(seegeodemographics)
from multi-source distributed data sets.
These, he argues, could each be considered
as important elements of a grid-enabled, geo-
computational toolkit. It is this potential to
contribute to the new e-science research
environments that may crystallize geocom-
putation as a distinct research field spanning
geographyand related disciplines. rhSuggested reading
Ehlen, Caldwell and Harding (2002); Gahegan
(1999); Macmillan (1998); Martin (2005).geodemographics Geodemographics is ‘the
analysis of people by where they live’ (Sleight,
2004) or, more precisely, by a data-based
classification of residential location (although
classifications have also been produced for
workplace, financial services andcyberspace).
The origins of geodemographics include
Charles Booth’spovertyMaps of London
(1898–9; see http://booth.lse.ac.uk) and the
1920s–1930schicago schoolof urban soci-
ology. During the twentieth century, the
increasing availability of nationalcensusdata
and the development of computation permit-
ted multivariate summaries of census zones to
be produced, and for those areas to be grouped
together on a like-with-like basis using cluster-
ing techniques (see classification and
regionalization).
Those methodological developments pro-
vided the foundation for modern geodemo-
graphics – a major industry used by corporate,
governmental, non-profit and political groups
to deliver key advertising and services to their
audiences, customers and users (Weiss, 2000).
Commercial applications emerged during
the late 1970s with the launch of PRIZM, by
Claritas, in the USA and ACORN, by CACI,
in the UK. Today’s classifications include not
only census data, but also shopping, electoral,
financial and other data about the ‘objects’ to
be classified (commonly individuals, house-
holds, postcodes, Zip codes,census tractsor
electoral wards). ACORN currently categor-
izes 1.9 million UK postcodes into one of five,
seventeen or fifty-six types (plus some ‘unclas-
sified’), using over 125 demographic statistics
and 287 lifestyle variables. PRIZM NE incorp-
orates bothhouseholdand census data to
describe, for example, Beverley Hills 90210 as
containing ‘Blue Blood Estates’, ‘Bohemian
Mix’ and ‘Money & Brains’ (amongst other
segments). There are geodemographic classifi-
cations of most of Western Europe, Northern
America, Brazil, Peru, Australasia, SouthGregory / The Dictionary of Human Geography 9781405132879_4_G Final Proof page 276 2.4.2009 6:30pmGEODEMOGRAPHICS