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Development of an interoperable tool to facilitate spatial data integration in the context of SDI

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The integration of multisource heterogeneous spatial data is one of the major challenges for many spatial data users. To facilitate multisource spatial data integration, many initiatives including federated databases, feature manipulation engines (FMEs), ontology-driven data integration and spatial mediators have been proposed. The major aim of these initiatives is to harmonize data sets and establish interoperability between different data sources. On the contrary, spatial data integration and interoperability is not a pure technical exercise, and there are other nontechnical issues including institutional, policy, legal and social issues involved. Spatial Data Infrastructure (SDI) framework aims to better address the technical and nontechnical issues and facilitate data integration. The SDIs aim to provide a holistic platform for users to interact with spatial data through technical and nontechnical tools. This article aims to discuss the complexity of the challenges associated with data integration and propose a tool that facilitates data harmonization through the assessment of multisource spatial data sets against many measures. The measures represent harmonization criteria and are defined based on the requirement of the respective jurisdiction. Information on technical and nontechnical characteristics of spatial data sets is extracted to form metadata and actual data. Then the tool evaluates the characteristics against measures and identifies the items of inconsistency. The tool also proposes available manipulation tools or guidelines to overcome inconsistencies among data sets. The tool can assist practitioners and organizations to avoid the time-consuming and costly process of validating data sets for effective data integration.
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Keywords: SDI; heterogeneous spatial data; multisource spatial data; spatial data integration

Document Type: Research Article

Affiliations: Department of Geomatics, Centre for Spatial Data Infrastructure and Land Administration, University of Melbourne, Victoria, Australia

Publication date: April 1, 2010

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