Integrating geospatial data acquired from diverse resources has been drawing a tremendous attention in recent years. Although many barriers have been successfully removed by the Open GIS technology, merely superimposing the acquired geospatial data together in the map interface certainly does not suffice the needs for correct application use. We argued that every distributed geospatial feature must be equipped with quality information that can characterize its unique property and enable future applications to unambiguously and automatically parse the necessary information to aid the making of integration decisions. Such a quality-aware approach helps users to build a knowledge-based working environment where the quality difference of acquired data can be correctly identified and analyzed. Furthermore, the design of GIS-based functions can incorporate built-in GIS processing knowledge to ensure the precision and accuracy of the analyzed results. As the future GIS-based application will largely depend on data dynamically collected from other resources, we successfully demonstrated that the necessity of data quality information and how it can be incorporated into the design of future data sharing environment, e.g., SDI.