Spatial Data Quality Control through the use of an Active Repository
The issue of spatial data quality is of concern to researchers and practitioners in the field of Spatial Information Systems (SIS). The results of any spatial analysis depend heavily on the data on which the analysis is based. Despite this, the users of most spatial data sets have no idea of the accuracy of the data contained within them. They base their subsequent analysis using the datasets on the assumption that the data is error free or that errors are kept to an 'acceptable' level. In order to ensure the results of analysis it is imperative that a facility for reporting data quality of the dataset is provided so that error levels can be monitored. To this end it is now becoming common for data providers to furnish their clients with metadata that is data about data, on quality, lineage and age. Data quality research issues in SIS include topological consistency; consistency between spatial and attribute data; and consistency between spatial objects representation and their true representation on the ground. The last category is subdivided into spatial accuracy and attribute accuracy. To some extent these errors can be reduced by adopting a more rigorous approach to integrity constraint management including the imposition of constraints upon data entered into the database. This paper describes an integrated development environment for SIS. At the core of this environment is an active repository that stores and maintains integrity constraints defined by the SIS developer. The result of such an approach is the control of quality in data captured by resulting systems and the facility to report on the quality of the data stored within their databases.