Evaluating Patterns of Spatial Relations to Enhance Data Quality

Effective use of data stored in spatial databases requires methods for evaluation and enhancement of the quality of the data. Spatial data quality can be evaluated using a measure of internal validity, or consistency, of a data set. Capturing spatial data consistency is possible with a multi-step approach. A distance measure is used to detect implicit spatial relations between neighboring objects. The next step involves identifying the types of relations between these neighboring objects using topology based constraints. The semantic information of objects, together with topological relations are combined to discover patterns, or rules, in the data. These rules are based on the analysis of the relations between each object and each of its neighbors, as well as between each object and all of its neighbors. Patterns of spatial relations, represented as rules are validated using available metadata, as well as trend analysis and Monte Carlo simulation techniques. These can now be used as the basis for automated detection of inconsistencies among spatial objects, where possible inconsistencies are detected when one or more rules are violated. Detected inconsistencies can then be adjusted, thus increasing the quality of spatial data sets.