Any geographic dataset is only a representation of reality, and it inevitably leaves its user with uncertainty about the nature of the real world that is being represented. This uncertainty may concern positions, as discussed in the Section 2.3.2, Probability density, but it may also concern attributes, and even topological relationships. Uncertainty in data will propagate into uncertainty about conclusions derived from data. For example, uncertainty in positions will cause uncertainty in distances computed from those positions, in the elements of a W matrix, and in the results of analyses based on that matrix.
Uncertainty can be due to the inaccuracy or limitations of measuring instruments, since an instrument that measures a property to limited accuracy leaves its user uncertain about the true value of the property. It can be due to vagueness in definitions, when land is assigned to classes that are not rigorously defined, so that different observers may classify the same land differently. Uncertainty can also be due to missing or inadequate documentation, when the user is left to guess as to the meaning or definitions of certain aspects of the data. Clearly it is important to spatial analysts to know about the uncertainties present in data, and to investigate how those uncertainties impact the results of analysis. A range of techniques have been developed, and there is a rich literature on uncertainty in spatial data and its impacts (see further, Zhang and Goodchild, 2002 and Longley et al. (2015, Ch5).