Evaluating Patterns of Spatial Relations to Enhance Data Quality

David Gadish, Ph.D.
Assistant Professor of Information Systems
California State University Los Angeles
5151 State University Drive
Los Angeles, CA, 90032
Tel: 323-343-2924
Email: dgadish@calstatela.edu


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. T he 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.

Keywords: Consistency, Patterns, Spatial Relations 

In: McRoberts, R. et al. (eds).  Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.


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