Neil Sang and Mathew Aitkenhead
The approaches discussed in relation to most scenario models relate to some form of simulation, mathematical model or representation in a formal modelling language. The range of computational or technological complexity involved is variable, but in most cases a very high degree of domain knowledge is also required with respect to the system under investigation.
Retaining the necessary skills on a wide range of problems is difficult, and indeed current scientific knowledge is not always sufficient to understand and represent a particular system. For large coupled systems, with a wide range of socio-economic, ecological and bio-physical systems interacting, this may not be feasible. For simpler systems, in specific cases, it may not be necessary. This chapter explores data mining as a pragmatic alternative or complementary approach. It also looks at the role of a spatial data infrastructure in lowering the cost of modelling work.