Spatial data integration: a necessity for spatially enabling government

Copyright confirmation in progress. Any queries to umer-enquiries@unimelb.edu.auIntroduction Most governmental decisions involve a spatial component (Williamson and Wallace, 2006), therefore informative decision making within governments is highly reliant upon multi-sourced spatial data. The ability to spatially enable governments through the use of integrated multi-source spatial data at different governmental levels makes governmental decisions incredibly efficient (Mitchell, 2006b), though governments rarely produce all the data required for their business. Rather, they obtain and integrate data from different sources. However, the diversity of data producers hinders effective spatial data integration. There are many technical and non-technical obstacles in the integration of multi-sourced spatial data and this is one of the major problems in sharing and using spatial data among government organizations. From a technical perspective, spatial data may differ semantically, syntactically and structurally. Institutional, social, policy and legal issues also hinder data integration. In order to effectively overcome these issues, a holistic framework is required to manage and address the issues. SDIs aim to facilitate the integration of multi-source spatial data by providing a holistic framework in which spatial data stakeholders (governments, private sector, etc) interact with spatial data effectively through technological components. There are inconsistencies in the various data within an SDI which lead to data inconsistency and hinder data integration. These inconsistencies should be managed through the SDI framework. However, at the moment, the SDI framework does not deal with these inconsistencies effectively. Hence, we need to identify and map the inconsistencies and develop tools and guidelines within the framework of an SDI to manage them. This will then make it easier for data to be integrated across and within government organisations