Study of Uncertainty or Error propagation during heterogeneous data integration

Abstract

Spatial data interoperability, as the central issue of Geographic Information Science: has received considerable attention in the recent years. Numerous Geographical Information Systems (GIS) interoperability research projects and initiatives have addressed the issues of format and representational heterogeneity across spatial datasets, spatial metadata (supporting data ) integration, spatial data interchange standards, organization of spatial data infrastructure nodes, data quality etc. Spatial data quality has been recognized as an important factor in GIS and studied in data modeling for many environmental, natural resources census and other related branches. While many solutions have emerged for handling syntactic and structural differences across spatial data sources, additional types of heterogeneity have received less attention. Driven by both practical challenges and trends in computing, the focus of spatial data integration research is moving to specific models and technologies of seamless, automatic and on-demand spatial data integration. Integration of heterogeneous geospatial data offers possibilities to manually and automatically derive new information, which are not available when using only a single data source. Furthermore, it allows for consistent representation and propagation of updates from one data set to the other. However, different acquisition methods, data schemata and updating cycles of the content can lead to discrepancies in geometric and thematic accuracy and correctness which hamper the combined integration. To overcome these difficulties, appropriate methods for the integration and harmonization of data from different sources and of different types are needed. newline