Spatial data quality capture through inductive learning

The relatively weak uptake of spatial error handling capabilities by commercial GIS companies and users can in part be attributed to the relatively low availability and high costs of spatial data quality information. Based on the well established artificial intelligence technique of induction, this paper charts the development of an automated quality capture tool. By learning from example, the tool makes very efficient use of scarce spatial data quality information, so helping to minimise the cost and maximise availability of data quality. The example application of the tool to a telecommunications legacy data capture project indicates the practicality and potential value of the approach.