Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).
Analysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.