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Spatial Data Infrastructure Frameworks to Support

Spatial Data Infrastructure Frameworks to Support Decision Making for Sustainable Development Mary-Ellen Feeney Abbas Rajabifard Ian Williamson Department of Geomatics, University of Melbourne, Australia. Overview. Decision making for Sustainable Development

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Spatial Data Infrastructure Frameworks to Support

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  1. Spatial Data Infrastructure Frameworks to Support Decision Making for Sustainable DevelopmentMary-Ellen Feeney Abbas Rajabifard Ian Williamson Department of Geomatics, University of Melbourne, Australia

  2. Overview • Decision making for Sustainable Development • SDI Concept and Decision making • Brief Introduction to the concept of DSS • The DSS & SDI context • Impacts for Developing SDIs to support DSS • Exploratory Case Studies of different SDI • Models for DSS support - regional Australia

  3. Changing Humankind-land relationships Sustainable Development Objectives Multicriteria Decision-Making (social, economic, environmental …) Decision Support Systems (multicriteria datainformationdecision alternatives) Spatial Data Spatial Data Infrastructures Decision Making for Sustainable Development

  4. Ecology Society Economy Decision Making for Sustainable Development Economy Society Ecology From Bellamy 2000

  5. The SDI concept & Decision Making The principle objective for developing SDI is to achieve better outcomes from spatially related economic, social and environmental decision-making.

  6. Access Network People Policy Data Standards Components of SDI Dynamic

  7. Components of SDI Dynamic Access Network People Policy Data Standards

  8. How do SDIs support Decision Making? • Through facilitating the provision of standardised, interoperable datasets and information that are accessible, useable, exchangeable • But, are data and information enough to support decision making?

  9. 1 Decision Space Solution Space 2 Spatial Data Information 3 Processing Decision Support The Nature of Decision Making?

  10. Generally computer-based information systems Support decision-making activities in the exercise of judgement Do not actually make the decision Characterised by integrated modeling and analysis facilities, including … Decision Support Systems (DSS)

  11. Decision Support Systems (DSS) • tools for obtaining, analysing & presenting information • modeling & simulation tools • multi-criteria modeling for selecting from a set of defined alternatives • Expert systems for rule-based decision making in defined situations • Life-cycle analysis & green design tools.

  12. Aid rather than replace decision makers Decision Support Systems • Generally combinations of ‘SYSTEM’ tools • Can restrict or expand decision options • May facilitate user-directed change • Can be for specific decision environments/ generic tool

  13. Decision Support Systems vs Tools • = complexity ie. • number of criteria; • incorporate preferences & values; • number of decision-makers  • decision-making model; • support existing data & ‘gaps in data’; • generation of alternative (prioritised) solutions

  14. Why What How When DSS & SDI : CONTEXT Relevance and Significance ?

  15. Why What • Questions are we trying to answer • - sustainable development objectives • Data do we require to answer them DSS & SDI : CONTEXT • Capability to validate data quality, • Process data quantity quickly & effectively & • Model new and more variable decision making

  16. To ask the necessary Questions How When • To model data to achieve satisfactory answers • To better manage spatial information towards Sustainable Development Objectives • Need to incorporate time-frames into decision making processes • e.g.. multiple stages, time-frames for criteria • Need for temporal data modeling developments (time series data analyses) DSS & SDI : CONTEXT

  17. Developing SDIs to support DSS ... • understanding of other’s data needs/resources • awareness of data availability, quality & limitations • Improved data by publishing & standards coordination. • Increased confidence in data use - consistency • Precipitant for collaborative data-sharing agreements. • data availability, collection, storage, access, • users with differing expertise in the GI use • incentives to integrate social, environmental, economic & spatial data • transfer of R&D to stakeholders

  18. Classification of Different SDI Models • 1. motivation for development • 2. expected outcomes • 3. management • 4. participants • 5. measures of progress • 6. political/administrative function • 7. time frame-committment

  19. Herbert River Information Centre- QLD Developing SDI using a Product Model: 1. Sharing/modification existing datasets, collection of key additional datasets 2. integrated databases of region 3. unincorporated partnerships between 11 agencies 4. private & public sector (3 tiers) 5. Completion of the Mapping Project on time 6. Regional (Sub-State) 7. fixed project period - 3 years

  20. Herbert River Information Centre- QLD Developing SDI using a Process Model: 1. Resource that supports spatial decision-making & planning for natural resource management 2. Resource Information Centre- GIS facilities, consultation, project management, data access & coordination 3. HRIC Management - Independednt of partners, 4. 6 partners - private & public sector (3 tiers) 5. Financial & Objective Sustainability - 3 years-10 6. Regional (Sub-State) 7. 10 year + (period after which partnerships reviewed)

  21. Integrated Information Management System - NSW & QLD Developing SDI using a Process Model: 1. Facilitate discovery & use of resources for Catchment Management Decision-making 2. Information Management Systems incorporating access to data & Modeling Systems 3. University & Government Partners, Govt. Funding 4. 3 partners - 2 State public sector, University QLD 5. Establishment, Prototype testing & Feedback 6. Regional (Sub-State) particularly Catchment-oriented 7. Dependent on Community & Agency Uptake

  22. vs Conclusions in decision support for sustainable development... • Process Models for SDI development: • offer access networks to data • forums of consultation (web-based or service centres) • DSS to support the application & modification of data • Product Models for SDI development: • improved data availability, coordinated collection, cross-agency data collaboration • integrated data products with defined quality & maintenance time-frames

  23. Acknowledgements These findings are from exploratory case studies in ongoing PhD research. They provide a broad-brush review of initiatives central to State SDI developments in Australia. They result from pilot-work in selecting & testing criteria for the comparison of SDI & DSS developments that is undergoing continuing development & refinement. • Land Victoria of the Victorian Government • Land & Property Information Centre of NSW • Department of Technology & Management NSW • Australian Research Council • Spatial Data Infrastructure Research Group, Department of Geomatics, University of Melbourne

  24. International Symposium on SDI 19-20 November, 2001 University of Melbourne, Australia http://www.sli.unimelb.edu.au/SDI

  25. Symposium Purpose • To explore the institutional and technical issues influencing the development of SDIs. • To examine and debate the directions of development of SDIs in the future. Web-site http://www.sli.unimelb.edu.au/SDI Email sdi@sunrise.sli.unimelb.edu.au Registration Deadline: October 31

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