Completeness refers to whether there are data lacking in the database compared to what exists in the real world. Essentially, it is important to be able to assess what does and what does not belong to a complete data set as intended by its producer. It might be incomplete (i.e. it is “missing” features which exist in the real world), or overcomplete (i.e. it contains “extra” features which do not belong within the scope of the data set as it is defined).
Completeness can relate to either spatial, temporal, or thematic aspects of a data set. For example, a data set of property boundaries might be spatially incomplete because it contains only 10 out of 12 suburbs; it might be temporally incomplete because it does not include recently subdivided properties; and it might be thematically overcomplete because it also includes building footprints.
Student is abel to explain and apply quality assessment procedures (level 1, 2 and 3).