An inconsistency measure of spatial data sets with respect to topological constraints

An inconsistency measure can be used to compare the quality of different datasets and to quantify the cost of data cleaning. In traditional relational databases, inconsistency is defined in terms of constraints that use comparison operators between attributes. Inconsistency measures for traditional databases cannot be applied to spatial datasets because spatial objects are complex and the constraints are typically defined using spatial relations. This paper proposes an inconsistency measure to evaluate how dirty a spatial dataset is with respect to a set of integrity constraints that define the topological relations that should hold between objects in the dataset. The paper starts by reviewing different approaches to quantify the degree of inconsistency and showing that they are not suitable for the problem. Then, the inconsistency measure of a dataset is defined in terms of the degree in which each spatial object in the dataset violates topological constraints and where possible representations of spatial objects are points, curves, and surfaces. Finally, an experimental evaluation demonstrates the applicability of the proposed inconsistency measure and compares it with previously existing approaches.