It is in this sense that we can speak of underlying, unobservable spatial data generation processes, about which we would like to infer. The inferences which we would like to be able to make are about these processes, which for a variety of reasons may not be directly observable. Using political behaviour as an example, we could seek to establish the identities of voters, hoping to link their ballot papers to their other characteristics, such as place of residence or birth, sex, age, occupation, etc. An exit poll could be used to achieve this, but then the focus would be on the individual level, rather than on the local, territorial, or ecological links. Aggregation in itself should not be avoided, not least because it often returns in one form or other as classifications used as explanatory variables related to cleavages, be it socio-occupational class, organisation, or some other structuring variable above the level of the individual.
Spatial aggregation brings with it a number of specific problems.
The boundary effects at the edges of the study area are often impossible to control for. In addition, the often arbitrary nature of the assignment of observation units to aggregates, known as the modifiable areal unit problem, has to be recognised. They separate the scale problem, where results change from less aggregated to more aggregated spatial units, from the aggregation problem caused by arbitrary choices made in zoning, that is assigning basic spatial units to contiguous zones. Zones in turn imply the contiguity of member units, while groups require no contiguity. It is quite clear that the results of analysis are dependent on the particular lattice of areal unit boundaries chosen, and that different results may be yielded by analyses using different boundaries.