Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.