Spatial Decision Support Knowledge Portal institute.redlands

1. Spatial Decision Support Knowledge Portalhttp://institute.redlands.edu/sds Rob Raskin, Jet Propulsion LaboratoryNaicong Li, U. of Redlands ESIP Federation Summer 2009 Meeting

2. Spatial Decision Support (SDS) Knowledge Portal Systematic representation of body of knowledge of SDS Promotes semantic clarity of concepts commonly used in spatial decision support Organizes and facilitates access to commonly used resources that aid in solving spatial decision problems Developed and maintained by the Spatial Decision Support Consortium http://institute.redlands.edu/sds

3. 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. Often multiple stakeholders or decision makers with conflicting interests Virtually all decision problems involving Earth science data are spatial-related. Even global change is spatially dependent

4. Community Needs for Spatial Decision Support Re-usable, modular tools/services that can be put together easily to form a spatial decision support system (SDSS) most SDS systems and tools developed to date are not reusable typical approach is to develop from scratch for specific project or application many components may be common across projects, decision problem types, and application domains Evaluate existing spatial decision support systems and tools in terms of their appropriateness and interoperability Understand and formalize the fundamental granules of the spatial decision process and related phenomena Common vocabulary and framework for semantic clarity

5. Complexities of Spatial Decision Making Variability (uncertainty) of geophysical inputs Complex multi-dimensional and heterogeneous data and models describing decision situations Multiple formats, including: numerical, map, image, text, and other forms; Large number of available alternatives or a need to generate decision alternatives "on the fly" according to the changing situation Multiple participants with different and often conflicting interests; Multiple categories of knowledge involved, including expert knowledge and layman knowledge.

6. Knowledge-Base Approach Organize the knowledge in SDS to represent/formalize the meanings and relations of SDS components Enable easy access to the SDS components Improve the robustness of the framework Facilitate interoperability with other existing conceptual models through standardization of terms Spatial decision process, phases and steps, workflows SDS related concept definitions and inter-relations Describe SDS resources using concepts in the ontologies Partition of concepts into a set of ontologies

7. Canonical Decision Process Steps Decision context Assessment, planning, legal/institutional aspects Decision processes Phases Methods and techniques Algorithms Data, models, and domain knowledge Software and other technology People, participation, and collaboration Resources References, literature, case studies How is everything related?

9. Semantic Registration of a Method (algorithm) Purpose Decision type Data inputs Data outputs Decision process phases/steps Implemented by Decision maker interaction level Assumptions and limitations

12. Roles of SDS Knowledge Portal Provide awareness of resources available to assist with specific steps in decision process Support community sharing of decision support components

13. SDS Consortium Members The Redlands Institute, University of Redlands Susan Crow, PlaceMatters Brenda Faber, Fore Site Consulting, Inc. Hamid Ekbia, Indiana University Michael Goodchild, University of California, Santa Barbara Sean Gordon, Interforest LLC Piotr Jankowski, San Diego State University Karen Kemp, The Kohala Center Richard Klosterman, What if? Inc. Naicong Li, University of Redlands Jacek Malczewski, University of Western Ontario Philip Murphy, InfoHarvest, Inc. Andrew Miller, Ecological Applications Keith Reynolds, USDA Forest Service Rob Raskin, Jet Propulsion Laboratory Paul Zwick, University of Florida

14. Accomplishments and Future Work Organized the knowledge of SDS Provides a controlled vocabulary for SDS among a user community Supports browsing and searching useful resources for SDS development Registration of data and models (future) Registration of modular components that can be implemented as a workflow via web services (future)