1. Spatio-Temporal Data Sharing using XML Web Services Presented at the Workgroup Meeting on Web-based Environmental Information System for Global Emission Inventories (WEISGEI) July 15, 2002, Arlington, VA National Science Foundation Digital Government Research Program US Environmental Protection Agency Rudolf Husar Center for Air Pollution Impact and Trend Analysis (CAPITA) Washington University

2. The Researcher/Analyst’s Challenge “The researcher cannot get access to the data; if he can, he cannot read them; if he can read them, he does not know how good they are and if he finds them good he cannot merge them with other data.” Information Technology and the Conduct of Research: The Users View National Academy Press, 1989 Tech challenge: To minimize these series of resistances

3. Integration for Global-Local Activities Global Activity Local Benefit Global data & analysisSpatial context; initial analysis Analysis guidance Standardized analysis, reporting Local Activity Global Benefit Local data & analysisElucidate, expand initial analysis Identify relevant issuesResponsive, relevant global analysis Global and local activities are both needed – e.g. ‘think global, act local’ ‘Global’ and ‘Local’ here refers to relative, not absolute spatial scale

4. Possible Information Architecture Data producers maintain their own workspace and resources (data, reports, comments). Part of the resources are shared by creating a common virtual resources. Web-based integration of the resources can be across several dimensions: Spatial scale:Local – global data sharing Data content:Combination of data generated internally and externally The main benefits of sharing are data re-use, data complementing and synergy. The goal of the system is to have the benefits of sharing outweigh the costs. User Local Global Virtual Shared Resources Data, Knowledge Tools, Methods User Shared part of resources Private part of resources

5. VOYAGER Data Explorer: Architecture and Technologies Built and used Used by a Virtual Community on AerosolsVirtual Community Layered Map Time Chart ProvidersUsers Vector GIS Data XDim Data SQL Tables Web Images Voyager Web Services Publish, Find, Bind Data & Tool Catalog Uniform Access Scatter Chart S u p p o r tCoord./Cooperation T e c h n o l o g i e s Select, Overlay, Explore; Multidimensional data Maintain Distributed Data; Heterogeneous coding, access Connect providers to users; Homogenize data access

6. Web Services Components and Actions Service providers publish services to a service broker. Service users find the needed service and get access key from a service broker With the access key, users bind to the service provider The result is a dynamic binding mechanism between the service users and providers Service Broker Service Provider Publish Find Bind Service User Components:Provider – User – Broker Actions: Publish – Find - Bind

7. Web Publish HTTP, FTP Web Services Now: Data Access though a Web Service Adapter Service Broker Publish Service Consumer Find Access Ordinary web content can be delivered as a Web Service through a Proxy Server. The Adapter Service converts HTTP/FTP service to XML Web service The Adopter Service publishes the web service to the Broker The User finds the data from the broker and accesses the Adapter to get distributed data Service Adapter Web Server Service User Chain

8. Example Data Adapter: Daily TOMS Aerosol Index Map TOMS Image Metadata Geo-rectangle (65, -180; -65, 180) Image Size (640, 480) Image Margins (40, 40, 30,30) Transparent Colors (0,0,255 ) Image Access Metadata StartDate EndDate Incement DataType URL template: :ftp://jwocky.gsfc.nasa.gov/pub/eptoms/images/aerosol/YYYYY/eaYYMMDD.gif

9. Voyager Spatio-Temporal Data Browser

10. Browsing of Distributed Data from HTTP/FTP Servers Land Reflectance from SeaWiFS Project, NASA GSFC AVHRR Oceanic Aerosol CAPITA, WashU, STL Fire Pixels, Jan 1997, ESA Ionia Project

11. Quebec Fires, July 6, 2002 SeaWiFS, METAR and TOMS Index superimposed SeaWiFS satellite and METAR surface haze shown in the Voyager distributed data browser Satellite data are fetched from NASA GSFC; surface data from NWS/CAPITA servers

12. Trans-Atlantic Transport of Quebec Smoke July 11: Smoke approaching Europe July 10: Quebec smoke over Mid- Atlantic SeaWiFS Reflectance TOMS Absorbing Aerosol SeaWiFS Reflectance TOMS Absorbing Aerosol Spain E. US

13. NRL Forecast Model for Dust, Smoke and Sulfate METAR Surface Haze Real-time model and surface observations are compared spatially and temporally Dust Sulfate Smoke METAR Haze Time Selector

14. Planned: Multi-Dimensional Browser Map View Variable View Time View WebCam View The views are linked so that making a change in one view, such as selecting a different location in the map view, updates the other views.

15. 4 D Geo-Environmental Data Cube (X, Y, Z, T) Environmental data represent measurements in the physical world which has space (X, Y, Z) and time (T) as its dimensions. The specific inherent dimensions for geo- environmental data are: Longitude X, Latitude Y, Elevation Z and DateTime T. The needs for finding, sharing and integration of geo-environmental data requires that data are ‘coded’ in multidimensional data space

16. 4D Geo-Environmental Data Illustration Animated 3D Maps

17. Multi-dimensional Data Access In array notation, the granule ‘value’ is accessed as –MyGranule = My1DArray(i) –MyGranule = My2DArray(i,j) –MyGranule = MynDArray(i,j,…..n) In order to select a data granule, a controller is assigned to each data dimension 1D Dataset e.g. Time selector i j k j i Data Granule Selection i 2D Dataset e.g. Param & Time selector 3D Dataset e.g. Param, Time & Location

18. Development Team and Support Initial Design Team at CAPITA –R. Husar as architect –K. Hoijarvi as lead engineer, J. Colson as developer with support from –R. Zager and S. Raffuse Project Development Partners Include: –Bret Schichtel, CIRA, ColoState –Stefan Falke, AAAS/EPA –Rich Poirot, Vermont DEC Project Support –NSF –EPA –NOAA