1 Spatial Modelling of the Terrestrial Environment - Georeferencial

240 Spatial Modelling of theTerrestrial Environment
west of Cambridge and in the vicinity of Huntingdon. Wards with low values along the
corridor reflect relatively free-flowing stretches of the A14 with low-settlement density.
The dormitory villages west of Cambridge also have fairly high levels, as do the immediate
suburbs of the City in the West and East. The policy case reveals that the alternative policy
package has achieved noticeable reductions in the A14 corridor, especially in the vicinity
of Huntingdon and in suburban West Cambridge. Values also tend to drop in the city centre.
Significantly, however, there is a very clear increase of exposure in the A10 corridor in the
north of the county, especially in the vicinity of Ely. This is despite the low density, rural
population pattern in this area and the wide expanses of open countryside.
11.6 Conclusion
To date, remotely sensed data from satellites has seen remarkably little take-up by the
planning community despite the fact that the complexities of data processing associated
with its use have largely been removed and the costs of data purchase are now extremely
low. Land use and transport models have become well established and their use as a basis
for policy testing is proved. Emissions modelling exercises, however, are usually carried
out by different sectors of the community with little reference to wide area environmental
impacts of underlying policy. This research has demonstrated how GIS can provide a
powerful basis for integrating these disparate areas of activity, thereby providing a powerful
tool for modelling the environmental impacts of different planning policy packages. The
Cambridgeshire case study has illustrated the power of the approach in the context of
emissions impacts on settlement, but the same techniques could be used in a wide variety
of planning and environmental assessment situations.
The results of the case study raise a number of interesting questions. In reality, the
policies tested are so extreme that it would be almost impossible to implement them within
a single plan review period. Despite this, the models suggest that they only achieve a
partial solution to the problems of traffic, congestion and emissions in the study area.
This suggests that development has a high level of momentum, which makes effective
implementation of policy change extremely difficult over relatively short time horizons.
Furthermore, although the policies tested have strong sustainability credentials in terms of
encouraging reductions in car usage in favour of public transport, the predicted outcomes
are not so clearly sustainable. The index of emissions shows a shift of impact away from
areas that are heavily populated to more rural areas where development is much less dense.
Outcomes measured in terms of the impact on public health might well be very positive but
different impact measures based on environmental quality would clearly show a substantial,
net deterioration. Equally, it seems clear that from an ecological point of view, the same
policy may prove sustainable in the context of one species and quite the opposite in the
context of another.
Perhaps the ultimate conclusion of this study is that integrated GIS that bring together
complex models go far beyond offering approaches to environmental analysis. They can
provide the basis for testing and learning about the likely outcomes of policy measures
before they are implemented and before their undesirable effects become too difficult to
reverse. Unless it can be clearly demonstrated that planning policies really do lead to