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SPATIAL DATA SUPPLY CHAINS

IN AUSTRALIA & NEW ZEALAND


                    Overlaps, Gaps and
                    Opportunities

                    Maurits van der Vlugt
                    Spatial Information Strategist
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?”)
SDI: DELIVERING FUNDAMENTAL
SPATIAL DATA FOR DECISION MAKING
Common Reference
 Framework
Reliable
Consistent
Authoritative
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
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).
HOWEVER…

 Myriad of initiatives

 In relative isolation
 Re-inventing wheels



 Duplicating Effort


Can we improve this?
SDSC ALIGNMENT STUDY

Identify SDSC initiatives in ANZ
Comparative Analysis
Gaps and Overlaps
   Re-usable components?
   Joint R&D and investment opportunities?
APPROACH
1. Quick Scan of      2. Alignment          3. Collaborative
relevant              Study                 Demo Project
initiatives
  • 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
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
# of
Business DriverDRIVERS
  BUSINESS                             Projects
                                        (N=32)
Maintain a consistent, authoritative
dataset to support evidence based        10
decision making
Meet statutory obligations                9
Improve data access & sharing across
                                          8
organisations
Improve efficiency & reduce
                                          5
duplication
Publish data for scientific research      4
Improve value & service to
                                          3
# of
Audience                            Projects
                                     (N=32)

Government agencies                   25

Public organisations and business
                                      14
groups

Scientific Community                  10


General Public                         6
DOES THE PROJECT/SYSTEM HAVE:
35
30
25
20
15
                           Don't Know
10
                           No
 5
                           Yes
 0
SYSTEM MATURITY



                       In production, with
          2            Governance Framework
              6
                       In production
  8
                       Prototype/trial


                       Under development
                  10
      6                Other
‘BEST OF BREED’ PROJECTS

Automated
   Publish
   Maintain
   Extract

Mature
   In production
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
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
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
THANK YOU – QUESTIONS?

 Acknowledgements
   CRC-SI (Kylie Armstrong, Mary Sue Severn)
   Project Participants



 More Information
   Http://www.crcsi.com.au/Research/Program-3/SDI-Alignment-Study
   Maurits.vandervlugt@mercuryps.com.au

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Spatial data supply chains in Australia and New Zealand

  • 1. SPATIAL DATA SUPPLY CHAINS IN AUSTRALIA & NEW ZEALAND Overlaps, Gaps and Opportunities Maurits van der Vlugt Spatial Information Strategist
  • 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. SDI: DELIVERING FUNDAMENTAL SPATIAL DATA FOR DECISION MAKING Common Reference Framework Reliable Consistent Authoritative
  • 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.
  • 6. 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).
  • 7. HOWEVER…  Myriad of initiatives  In relative isolation  Re-inventing wheels  Duplicating Effort Can we improve this?
  • 8. SDSC ALIGNMENT STUDY Identify SDSC initiatives in ANZ Comparative Analysis Gaps and Overlaps  Re-usable components?  Joint R&D and investment opportunities?
  • 9. APPROACH 1. Quick Scan of 2. Alignment 3. Collaborative relevant Study Demo Project initiatives • 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
  • 10. 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
  • 11. # of Business DriverDRIVERS BUSINESS Projects (N=32) Maintain a consistent, authoritative dataset to support evidence based 10 decision making Meet statutory obligations 9 Improve data access & sharing across 8 organisations Improve efficiency & reduce 5 duplication Publish data for scientific research 4 Improve value & service to 3
  • 12. # of Audience Projects (N=32) Government agencies 25 Public organisations and business 14 groups Scientific Community 10 General Public 6
  • 13. DOES THE PROJECT/SYSTEM HAVE: 35 30 25 20 15 Don't Know 10 No 5 Yes 0
  • 14. SYSTEM MATURITY In production, with 2 Governance Framework 6 In production 8 Prototype/trial Under development 10 6 Other
  • 15. ‘BEST OF BREED’ PROJECTS Automated  Publish  Maintain  Extract Mature  In production
  • 16. 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
  • 17. 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
  • 18. 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
  • 19. THANK YOU – QUESTIONS?  Acknowledgements  CRC-SI (Kylie Armstrong, Mary Sue Severn)  Project Participants  More Information  Http://www.crcsi.com.au/Research/Program-3/SDI-Alignment-Study  Maurits.vandervlugt@mercuryps.com.au

Hinweis der Redaktion

  1. 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
  2. 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).
  3. So: what is the state of SDSCs in Australia/New Zealand?
  4. <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.
  5. 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
  6. 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.
  7. 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.