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Spatial Decision Support Portal- Presented at AAG 2010

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A presentation prepared for the American Association of Geographers (AAG) 2010 Annual Meeting in Washington DC. The presentation discusses work done by the University of Redlands and the SDS …

A presentation prepared for the American Association of Geographers (AAG) 2010 Annual Meeting in Washington DC. The presentation discusses work done by the University of Redlands and the SDS Consortium to organize and provide access to the body of knowledge regarding Spatial Decision Support

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  • In the context of adaptive management, we go through the cycle of identifying the problems in the current state of our ecosystem, identify our goal for a desired state of the system, and, based on our scientific understanding of various aspect of the ecosystem, we investigate and assess the current conditions, come up with alternative plans to reach our goal, and select the best one to implement, preferably after having dome some impact analysis of the actions that we are going to take. And we monitor our plan implementation and its effectiveness, and adjust our plan in the next cycle if needed.Because of the complexity of the ecosystem, and because the complexity in evaluating our potential actions, computational support is often needed in the process of such decision making.There has been a vast body of theories, methods, resources including tools, data sources, etc. developed to help solving complex spatial decision problems. Spatial decision support is an active research area in GIScience and various other research communities. One of our purposes of developing the SDS Knowledge Portal is to organize the vast body of knowledge and resources, to create a portal that our army clients can come and gain a better understanding of the decision making process, and to find the kind of decision process workflow, methods, tools, data, literature and example case studies for that are pertinent to their decision problem at hand.
  • In 2008 about 30 researchers, experts, and practitioners representing about 25 agencies met at the University of Redlands and formed an informal SDS Consortium. One of the primary objectives was to establish some “semantic clarity” by defining terms and concepts within the field. One of these definitions was SDS itself…Thedefinition of spatial decision support has been broadened by the SDS Consortium. It includes not only the support/assistance for choosing alternative solutions, but also the support/assistance from the beginning to the end of the decision making/planning process and beyond (plan implementation, monitoring, review)
  • Beyond just Knowledge Portal
  • To this end, we have collaborated with a group of experts in this field including scholars, practitioners and toolmakers in SDS to develop a conceptual framework to capture the body of knowledge in this field, and to systematically classify the various resources useful for spatial decision making, and to provide a standard set of terms describing this body of knowledge for a user community. The conceptual framework drives the browsing and searching functions on the Portal.
  • Play Lindsey’s animation.It starts with a typical structured decision process, with its phases and each phase has sub steps. Then it zooms in to one of the steps (alternative ranking), shows the various information associated with this step. Part of the information is about some commonly used methods. Zoom in to this method (weighted linear combination). Look at some detailed information about this method. This method is one of the method in a method hierarchy. This method is implemented by a bunch of tools. One of them is EMDS. Zoom to EMDS. Show various attributes of EMDS. One of them is about the case studies where EMDS was applied. Zoom to this case study. Case studies are recorded with a set of parameters. One of the is the decision problem type it addressed. Show the decision type hierarchy.The animation stops at this graph. If you could embed the animation in the ppt, that would be great, otherwise you have to play it outside of the slides. This is only a tiny segment of the entire ontology graph.Many nodes and links are not in here (e.g. there are ~ 100 methods)Each nodes here may have many more links to other nodes
  • Concept search – more intelligent than keyword search. It accommodates synonyms and acronyms of a term, and can looks up other relevant terms based on the hierarchical relations among concepts in the ontology. E.g. if the user is looking for tools that implement multi-criteria analysis method, the Portal will return the tools that implement any of the sub method of multi-criteria analysis method.
  • A few words about this potential SDS resource recommendation function:
  • One of our research agenda item is to add solution resource recommendation to the SDS Knowledge Portal.The user comes to the portal with a decision problem, and describes this problem based on a set of parameters pre-defined in the SDS ontology. Because the decision problem types in the ontology are linked with other types of concepts such as appropriate decision process workflows, methods, tools, etc., the Portal’s inference engine may be able to conduct inferencing and produce a subset of SDS resources suitable for the user’s decision problem.
  • The user describes various aspects about their decision problem by choose parameters values pre-defined in the ontology.
  • Completed input form where the user answered questions about their decision problem, in terms of general description, the decision context, application domain, knowledge domain, decision problem type, problem objective, etc.
  • These tool attributescan be used as part of standard parameters for registering tools in an online tools registry, and thus exposing a tool’s various attributes for the system to determined whether it could be interoperable with other tools.
  • Transcript

    • 1. SDS Knowledge Portal
    • 2.
    • 3. Complexities of spatial decision making
      Spatial decision making faces various decision complexities such as
      • Spatial nature and temporal development of phenomena and processes;
      • 4. Complex multi-dimensional and heterogeneous data describing decision situations;
      • 5. Large or extremely large data sets that include data in numerical, map, image, text, and other forms;
      • 6. Large number of available alternatives or a need to generate decision alternatives "on the fly" according to the changing situation;
      • 7. Multiple participants with different and often conflicting interests;
      • 8. Multiple categories of knowledge involved, including expert knowledge and layman knowledge.
      http://geoanalytics.net/VisA-SDS-2006/
    • 9. Definition of Spatial Decision Support (SDS)
      Spatial decision support is the computational or informational assistance for making better informed decisions about problems with a geographic or spatial component. This support assists with the development, evaluation and selection of proper policies, plans, scenarios, projects, interventions, or solution strategies.
      Spatial Decision Support Consortium, 2008
    • 10. Body of Knowledge in SDS
      Status of the field of SDS
      • Active field of research and practice
      • 11. Vast amount of information/knowledge on spatial decision support
      • 12. Countless publications on decision process, methods, etc.
      • 13. Large numbers of SDS resources (e.g. tools) developed for all kinds of applications, many for specific project needs
      The Need
      • Systematic organization/presentation of the body of knowledge in SDS
      • 14. Easy access of SDS resources and understanding of their functionalities
       Need for Spatial Decision Support Knowledge Portal
    • 15. SDS Knowledge Portal
      Objectives
      • Identify the structure underlying the body of knowledge on SDS, e.g.
      • 16. Common workflows during decision making process
      • 17. Common set of methods associated with specific stepsof these workflows during a decision process
      • 18. Tools that implement a particular method
      • 19. Organize/structure the body of knowledge in SDS on the Portal
      • 20. Establish a standardized terminology within a user community
      • 21. Facilitate access of SDS Resources
      • 22. Help improve the quality of decision making
    • SDS Knowledge Portal
      Approach
      Develop a common organizational conceptual framework– using ontologies, containing
      • SDS related concepts, organized into
      • 23. Hierarchies/graphs, with each concept defined by
      • 24. Name, synonyms, acronyms/abbreviations (  standard vocabulary)
      • 25. Description,
      • 26. Set of formally defined attributes
      • 27. Set of formally defined relations to other concepts
      The content of the SDS Portal is formalized
       it can be queried easily
    • 28. SDS Knowledge Portal
      Incremental development of the SDS portal
      Knowledge
      Portal
      - User accessing knowledge about SDS, learning about decision process, methods, etc.
      Resource
      Portal
      - User seeking resource info: tools, models, data sources, case studs, literature, etc
      Solution
      Portal
      - User seeking resource recommendations for their specific decision problem solution
    • 29. Current status of the SDS Knowledge Portal
      • Establishment of the SDS Consortium
      • 30. Developed a conceptual framework for the body of knowledge in SDS
      • 31. Systematically classified various SDS resources
      • 32. Established a standard set of terms
      • 33. Developed the SDS Knowledge Portal
      • 34. Public release in April 2009
      • 35. 2 incremental new releases since then, with one coming up soon
    • Current status of the SDS Knowledge Portal
      The SDS Conceptual framework includes > 800 concepts with natural language definition as well as formally defined logical relations among the concepts, describing
      • Spatial decision problem types
      • 36. Decision contexts
      • 37. Knowledge domains and application domains
      • 38. Decision process phase and steps
      • 39. Methods and techniques
      • 40. Various resources including tools and models, data sources, case studies, etc.
    • Current status of the SDS Knowledge Portal
      • ~ 100 methods and techniques
      • 41. > 80 SDS tools and models
      • 42. > 20 data sources
      • 43. > 20 data models
      • 44. 8 decision process workflow templates
      • 45. > 20 case studies
      • 46. > 600 literature references
      • 47. > 40 related websites
    • The conceptual framework is coded in description logic (OWL web ontology language):
      • Flexible enough for capturing the hierarchical as well as associative relations among concepts
      • 48. Rigorous enough for automatic consistency checking of the conceptual framework
      • 49. Enables semantic query with inferencing to derive implicit knowledge
    • 50. Current status of the SDS Knowledge Portal
      The way information is organized on the Portal by ontologies facilitates information retrieval on the Portal. The user can :
      • Browse the conceptual hierarchy
      • 51. Follow the hierarchical structure in the ontologies
      • 52. Follow the relation links from concept to concept
      • 53. Search for a concept in the ontologies
      • 54. Browse resources (literature, SDS tools and models, case studies, data sources, data models, etc.) Multiple ways of browsing, e.g. for tools:
      • 55. Browse by name
      • 56. Browse by decision problem types
      • 57. Browse by decision process steps
      • 58. Browse by application domain
      • 59. Browse by knowledge domain
      • 60. Etc.
      • 61. Search tools or case studies with multiple search criteria (concept search)
      • 62. Search literature – conventional bibliographical and full text keyword search, as well concept search.
    • Ongoing research on SDS Knowledge Portal
      • Further addition to the Portal content, especially in the areas of
      • 63. Decision process workflows
      • 64. Relationship between decision problem types and methods
      • 65. Collaboration decision making methods and tools
      • 66. SDS resources
      • 67. Supporting users in solving their decision problems:
      • 68. Include more relevant SDS resources
      • 69. Improve browse/search functionality on the Portal for solution resources
      • 70. Portal makes recommendation on solution resources
      • 71. User case studies inform the Portal development
      • 72. Work towards tools/models interoperability
    • Ongoing research on SDS Knowledge Portal
      • Further addition to the Portal content, especially in the areas of
      • 73. Decision process workflows
      • 74. Relationship between decision problem types and methods
      • 75. Collaboration decision making methods and tools
      • 76. SDS resources
      • 77. Supporting the user in solving their decision problems:
      • 78. Include more relevant SDS resources
      • 79. Improve browse/search functionality on the Portal for solution resources
      • 80. Portal makes recommendation on solution resources
      • 81. Clients case studies inform the Portal development
      • 82. Work towards tools/models interoperability
    • User input
      From reasoning
    • 83. User input information about their specific decision problem
    • 84. User input information about their specific decision problem
    • 85. The Portal runs an inferences engine based on
      • Existing information on the Portal
      • 86. A set of inferencing rules (implemented with SPARQL)
    • The portal derives a subset of SDS resources that may be applicable for the user’s decision problem, and recommend them to the user.
    • 87. Ongoing research on SDS Knowledge Portal
      • Further addition to the Portal content, especially in the areas of
      • 88. Decision process workflows
      • 89. Relationship between decision problem types and methods
      • 90. Collaboration decision making methods and tools
      • 91. SDS resources
      • 92. Supporting the user in solving their decision problems:
      • 93. Include more relevant SDS resources
      • 94. Improve browse/search functionality on the Portal for solution resources
      • 95. Portal makes recommendation on solution resources
      • 96. Clients case studies inform the Portal development
      • 97. Work towards tools/models interoperability
    • More efforts towards promoting tools interoperability/chaining
      The tools now have finer grained definitions in terms of:
      • The type of decision problems they address
      • 98. The steps/tasks during the decision process they support
      • 99. The knowledge domains they model
      • 100. The modeling/calculation method they implement
      • 101. The application domain they are used in
      • 102. As well as other properties related to their analysis scale, input/output requirements, systems requirement, and other general descriptions
    • Questions?
      Nathan StroutNaicong LiRedlands InstituteUniversity of Redlandsnathan_strout@spatial.redlands.edunaicong_li@spatial.redlands.eduhttp://www.spatial.redlands.edu/sdshttp://www.spatial.redlands.edu/redlandsinstitute