Improving geographical data quality analysis
Geographic data are significant information sources in commerce, industry, and daily life. There is an arising awareness that geographic information is often inaccurate, uncertain, incomplete and of a qualitative nature and thus not easily merged with current quantitative GIS. The circulation of geographic information as well as the difficulty for users to appreciate its quality may result in wrong uses or interpretations, which increase the number of cases brought to court. Thus, the Esprit-4 project REVIGIS has set out to establish theories and tools to assist those who use geographic data in comprehending uncertain information. Various parameters were used to describe quality information. Some of these included data positional accuracy, semantic accuracy and completeness. Furthermore, each parameter can describe data at different levels of detail regarding quality such as quality of a dataset, of a single object class and of a single object instance. Finally, a data model was designed in order to support the management of heterogeneous data quality information at different levels of analysis.
