Using typical and frequent generalization problems, the following two examples demonstrate how constraints are instantiated
and how they can be translated to measures. While the constraints themselves are unspecific to any particular
data model, algorithms for data enrichment and conflict detection (e.g., measures) and data generalization must be specialized
for vector or raster data. Problems and advantages of each data model resulting from this fact are discussed.
Only basic properties of categorical data have been incorporated in the description of generalization algorithms. Countless
others can be specified to modify the generalization process, respecting specific aspects of the data used. Furthermore
it should be pointed out that the problems and solutions presented in both examples are normally part of an integrated
generalization strategy of interrelated operators and algorithms and should not be looked at in isolation.