Spatial data supply chains in Australia and New Zealand


Published on

There are a multitude of organisations in Australia and New Zealand pursuing spatial data supply chain initiatives. There is little to no co-ordination of these developments, leading to duplication of effort, wasted investment and missed opportunities. This presentation presents the results of the CRC-SI “Alignment Study”; an inventory of these initiatives, gaps and overlaps and research opportunities that arise.

Published in: Technology, Business
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Give examples (road DB):Jurisdictions collect & maintain road dataSubmit updated road data set every 6 months to central custodian. With agreed metadata and attribute schemas, in GML format. Central Custodian validates complianceDataset and metadata converted into common schema and DB format for maintenance DBData is integrated with national dataset, coordinate transformation and attribute harmonisation to ensure national consistency. Production DB (optimised for maintenance) is continually maintained, versioned and replicated to delivery DB (optimised for delivery, may be outside Firewall)'On demand' generation of regional roadmaps, road datasets with specific attributes for intelligent transport monitoring, and 4WD track mapsroad datasets clipped for regions & converted to shapefiles. Downloadable via secure FTP. 4WD tracks published as Web Service.Logistics company subscribes and regularly downloads national road dataset for route planning
  • will have a single point of truth where relevant data elements can be cached, or value added data can be maintained;are continuously updatable from multiple sources;support the supply of the same information in many different forms or “supply views” (format and structure) to support the many tools that are used,support multiple versions of these products to support traceability and transparency, have automated update and distribution, andenable automated input from e.g. sensors or volunteered geographic information (VGI).
  • So: what is the state of SDSCs in Australia/New Zealand?
  • <10% service & Value oriented, rest mainly ‘supply push’It is noteworthy that the primary business drivers seem internal or institutional drivers, while a ‘pull’ driver such as improving value to stakeholders comes second last.
  • The strong focus on a government audience (25 of 32 projects) is consistent with the identified business drivers.By Gov’t agencies, for Gov’t agencies
  • This question addresses the key technical capabilities underpinning SDSCs, as listed in section ‎3.2.Not entirely surprisingly, almost all projects have a Single Point of Truth data store, and facilitate Continuous Updates. Other key ingredients of spatial data supply chains are less frequently present, while sensor- and VGI input capabilities exist in only one third of the projects.
  • As noted before (Section ‎4.3.5), mature, automated spatial data supply chains are still in their infancy in Australia and New Zealand. Only a minority of steps in the reviewed supply chains are fully automated (10-20%), and only 7 projects (semi-) automate the entire supply chain.Among the reviewed projects, the capability for automated publishing and integration are the least developed, markedly less than the other supply chain steps.
  • Spatial data supply chains in Australia and New Zealand

    1. 1. SPATIAL DATA SUPPLY CHAINSIN AUSTRALIA & NEW ZEALAND Overlaps, Gaps and Opportunities Maurits van der Vlugt Spatial Information Strategist
    2. 2. OVERVIEWProblem Statement (“Why this Project?”)Spatial Data Supply Chains (“Why do you Care?”)Project Approach (“How did we do it?”)Outcomes (“What did we find?”)Next Steps (“What now?”)
    3. 3. SDI: DELIVERING FUNDAMENTALSPATIAL DATA FOR DECISION MAKINGCommon Reference FrameworkReliableConsistentAuthoritative
    4. 4. CRITICAL ENABLER Spatial Data Supply Chains (SDSC)  Multi Source  Consistent, Reliable  Automated, Flexible, Distributed E.g. PSMA’s G-NAF, BoM’s GeoFabric Source: CRC-SI
    5. 5. WHAT MAKES A GOOD SDSC?Single Point of Truth;Continuously updatable from multiple sources;“Supply Views”;Version Management;Automated Update and Distribution;Automated Input (sensors or VGI).
    6. 6. HOWEVER… Myriad of initiatives In relative isolation Re-inventing wheels Duplicating EffortCan we improve this?
    7. 7. SDSC ALIGNMENT STUDYIdentify SDSC initiatives in ANZComparative AnalysisGaps and Overlaps  Re-usable components?  Joint R&D and investment opportunities?
    8. 8. APPROACH1. Quick Scan of 2. Alignment 3. Collaborativerelevant Study Demo Projectinitiatives • Review ANZ • Validation of • Public-Private Initiatives Ph1 outcomes • Working • Analysis • SDSC demonstrator Framework Reference • Contributions to • Review & Architecture standards Analyse • Detailed processes • Scientific Analysis • IP development Literature • Recommendati Review ons for • Identify Alignment and Strategic Collaboration Partners & • Input to Opportunities Research • Proposal for Ph Agenda 2 • Proposal for Demo Projects
    9. 9. OUTCOMES37 Project approached, 32 ResponsesLargely GovernmentOnly 7 Automate entire supply chain50% in pre-production stagesOnly 6 out of 32 having a full, long term sustainable governance framework in place
    10. 10. # ofBusiness DriverDRIVERS BUSINESS Projects (N=32)Maintain a consistent, authoritativedataset to support evidence based 10decision makingMeet statutory obligations 9Improve data access & sharing across 8organisationsImprove efficiency & reduce 5duplicationPublish data for scientific research 4Improve value & service to 3
    11. 11. # ofAudience Projects (N=32)Government agencies 25Public organisations and business 14groupsScientific Community 10General Public 6
    12. 12. DOES THE PROJECT/SYSTEM HAVE:3530252015 Dont Know10 No 5 Yes 0
    13. 13. SYSTEM MATURITY In production, with 2 Governance Framework 6 In production 8 Prototype/trial Under development 10 6 Other
    14. 14. ‘BEST OF BREED’ PROJECTSAutomated  Publish  Maintain  ExtractMature  In production
    15. 15. SHORTLIST FOR PH2 ANALYSIS LINZ Data Service Maori Land Geographic Information System PSMA Systems SISS - Spatial Information Services Stack SLIP Enabler The Australian Hydrological Geospatial Fabric (Geofabric) VSDL - Victorian Spatial Data Library
    16. 16. CONCLUSIONS Status of SDSC:  Infancy  Supply Driven  Developed in Isolation Capability Gaps  Automation  Maturity  Standards Plenty of Opportunity for Collaboration  re-use of components  Joint investment and R&D
    17. 17. NEXT STEPS Integrate QLD & NSW in Ph 1 Analysis Ph 2 : from early 2012  Detailed analysis of shortlisted projects  Reference Architecture  R&D Agenda  Scope collaborative demo project
    18. 18. THANK YOU – QUESTIONS? Acknowledgements  CRC-SI (Kylie Armstrong, Mary Sue Severn)  Project Participants More Information  Http:// 