Infrastructure Engineering - Research Publications
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ItemSpatial Data Integration Challenges: Australian Case Studies(Spatial Sciences Institute, 2007)
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ItemBridging SDI design gaps(Centre of Geo-Information Technologies (cGIT), 2006)The environment we inhabit is integrated and to properly manage the environment it is necessary to look at all environmental components and making multi-criteria decision about environment mostly needs an integrated view of built and natural environmental components to better interpret it.Despite the integrated nature of environment and requirements of users to integrate different components of environment, information about different elements of environment is being collected and managed by fragmented agencies under different and mostly inconsistent policies and standards to satisfy their own needs –for a single discipline- with little attention to the broad range of users – a multi-disciplinary approach. This fragmentation results in heterogeneity of technical and non-technical issues surrounding integration of datasets.An SDI is an initiative to facilitate the cooperation among all stakeholders and the interaction with standards and technological components and one of its objectives is to facilitate the integration of multi-source spatial data sets.This paper aims to address different issues connected to the integration of multi-source data sets in order to better serve different communities through their SDI initiatives and also a better management and sharing of their spatial data. The paper aims to discuss both technical and non-technical issues related to the integration of multi-source data sets in alignment with an ongoing research project devoted to developing models, guidelines and associated tools to facilitate the integration of multi-source datasets within an SDI.
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ItemThe development of a frameworkand associated tools for the integration ofmulti-sourced spatial datasets( 2006)The world of today heavily relies on spatial data to manage the natural and built environments.Monitoring and controlling the impact of human activities on environment and the impact of naturalenvironment changes (including natural hazards) on built environment is a major policy driver formany jurisdictions. The mitigation of natural hazards including tsunami, earthquake and landslide andsecuring citizens against them has become another priority of nations , especially after Indonesia’stsunami. September 11 was also a remarkable point in history which attracts attentions towardsproviding more efficient counter-terrorism initiatives to secure citizens.In many countries meeting sustainable development objectives including social cohesion andenvironment protection together with economical growth is the most overwhelming policy driver. Inthese countries most of legislations and decisions are made, if they meet sustainable developmentobjectives.All above mentioned activities try to control the natural and built environments and monitor theimpacts of one environment on the other one. To control and monitor built and natural environments,the components of these environments need to be integrated to provide the factual model of realworld. Effective access and use of spatial data has been addressed by developing SDIs (Spatial DataInfrastructures) which one of its objectives is to address and provide requirements for effective dataintegration.The effective integration of built and natural environmental datasets is an ultimate goal of manyspatial decision making systems which has not been fully achieved, however technical integration andinteroperability of multi-sourced spatial data have received much attention. The integration of multisourcedspatial data due to the diversity of data providers needs more than technical tools andconsiderations. Institutional, social, legal and policy requirements must also be taken intoconsideration in order to achieve effective integratio
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ItemSpatial data integration: a necessity for spatially enabling government(University of Melbourne, The Centre for SDIs and Land Administration, 2007)Introduction 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.