Geography Reference
In-Depth Information
numerical models was particularly important. In physical
geography, it allowed the development of process models that
had a much stronger scientifi c basis than descriptive models
such as the Davisian cycle of erosion (see box in Chapter 2). In
human geography, the models had been distinctly sparse but
the models of city structure and growth were worthy of that
description, though they were derived from outside geography.
The kind of advance made possible is exemplifi ed by Torsten
Hägerstrand's work on diffusion, which employed sophisticated
yet transparent probability theory to demonstrate the spread
of innovations over time and space. Figure 23 offers one of the
early examples from his studies in rural Sweden and is concerned
with the gradual acceptance of subsidies by farmers over time.
Diagram A shows the simulation grid made up of cells for which
probability scores based, for example, on distance from the source
of subsidies, can be calculated. This grid is superimposed over
the actual area in B, on which is shown the actual diffusion of
subsidies after three years. Such spatial models are also used
in medical geography to analyse the spread of diseases and in
population geography to demonstrate patterns of migration over
time.
It is probably true to say that only a minority of human
geographers currently retain an interest in numerical analysis but
this state of affairs has its critics. For example:
Geography is losing its way precisely because so many if its
practitioners have retreated from the quest of creating robust,
defensible generalisations about spatial patterns and processes.
P. A. Longley and M. J. Barnsley,
'The Potential of Geographical Information Systems' (2004)
There is, however, one fi eld of geography where numerical skills
have found a new home and are forging ahead; that is the science
of Geographical Information Systems (GIS).
