Handling Data Consistency through Spatial Data Integrity Rules in Constraint Decision Tables

von Fei Wang

With the rapid development of the GIS world, spatial data are being increasingly shared, transformed, used and re-used. The quality of spatial data is put in a high priority because spatial data of inadequate quality is of little value to the GIS community. Several main components of spatial data quality were indentified by international standardization bodies such as ISO/TC 211, OGC and FGDC, which consists of seven usual quality elements: lineage, positional accuracy, attribute accuracy, semantic accuracy, temporal accuracy, logical consistency and completeness (two different names for similar aspects of quality are grouped in the same category).

In this dissertation our work focuses on the data consistency issue of the spatial data quality components, which involves the logical consistency as well as semantic and temporal aspects. Due to complex geographic data characteristics, various data capture workflows and different data sources, the final large datasets often result in inconsistency, incompleteness and inaccuracy. To reduce spatial data inconsistency and provide users the data of adequate quality, the specification of spatial data consistency requirements should be explicitly described. Data producers can follow the defined data consistency requirements to validate how well a geospatial dataset meets the criteria set, and then produce desirable geospatial data.