Developing a GEO label: providing the GIS community with quality metadata visualisation tools

Geospatial data quality and quality visualisation has always been an area of active research within the geographic information community. Subjected to processes of generalisation, abstraction, and aggregation, geospatial data can only provide an approximation of the real world, and therefore almost always suffers from imperfect quality (Goodchild, 1995). Objective quality measures of geospatial data relate to the “difference between the data and the real world that they represent” (Goodchild, 2006, p. 13). Objective quality information (e.g., lineage, completeness, logical consistency, positional, temporal and attribute accuracy, uncertainty measures, etc.) is often found in formal metadata documents supplied by a dataset provider or in technical reports which describe quality checks. Subjective measures of quality relate to a dataset’s “fitness for use”, meaning that, in order to assess the quality of data, we need to have information about the data to be used as well as the actual user need (e.g., Chrisman, 1991). Subjective quality information can include informal reports from other users describing how they used a dataset, users’ ratings of data or assessment of data relevance, recommendations for appropriate/inappropriate uses of the data, or supplementary advice from dataset providers, such as warnings about problems in specific areas.