O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Budhendra Bhaduri Overview of Geospatial Computing at ORNL Geographic Information Science & Technology Computational Sciences and Engineering Division

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Rich History Spanning 30 years Defining Geospatial Research Agendas Developing New Algorithms, Software, and Data Conducting Verification & Validation Studies ORNL: A pioneer in Geospatial Science and Technology

Technology Highlights  Geospatial Science  Multi-resolution data analysis  Advanced spatial modeling  Data integration and visualization  Geographic Information Systems (GIS)  Decision Support Systems (DSS)  Scientific Data Access  Collection, Distribution, and Management  Large scale data manipulation

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Ongoing Research Highlights l Analysis of surface drinking water systems and source water characterization l I2IA: Feature-based characterization of large image archives l Transportation Routing Analysis GIS (TRAGIS) l LandScan: High resolution population distribution model

l Georeferencing Community Water System (CWS) intakes to the National Hydrography Data (NHD) l Delineating upstream contributory watersheds from the intake locations l Characterizing upstream contributory watersheds with pesticide usage data Source Water Characterization

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY CWS intakes CWS Intakes  6945 intake location  6361 indexed to NHD  30% validated so far  Characterization and visualization tools developed

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Watershed Characterization  Land cover/Land use  Pesticide application  1992  1997  Total by compound  Total by crop type  By crop-compound combinations  32,000 data files as initial output

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Image-to-Intelligence Archive (I2IA) Assisting Image Analysts in Building and Using Archives  The amount of data available to an image analysts today is enormous  We are developing an agent-based system architecture that  autonomously manages a massive but dynamic image data archive  transforms that to an intelligence or information archive to aid national security needs Data Search & Retrieval Geoconformance Segmentation Feature Mapping

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Spatial Search & Filtering of Image Data Spatial Search & Filtering of Image Data Data Search & Retrieval Agents Data Processing & Integration Agents Feature Search, Filtering, & Retrieval Feature Search, Filtering, & Retrieval Modeling and Analytical Applications & Visualization for Decision Support Systems Modeling and Analytical Applications & Visualization for Decision Support Systems USGS NIMA NOAA NASA EPA Features Image Data Archive Image Data Archive IntelligenceArchive Image Analysis, Indexing, and CharacterizationIntelligenceArchive Image Analysis, Indexing, and Characterization Index Power plants Airports Hospitals Where are the power plants? ImageAnalysisIntelligentAgentsGeospatialAnalysis MonitorDistributedDataSources Geoconform Images to Create Dynamic Data Archive Create Intelligence or Information Archive

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Transportation Routing Analysis GIS Web-TRAGIS  Transportation Routing Analysis GIS (TRAGIS) model is used to examine highway, rail, and waterway routes for route campaigns or future shipments  Transportation routing model used by DOE community for routing and risk assessment studies

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY TRAGIS Rail Network  1:100K rail network development sponsored by Dahlgren NSWC Joint Program Office  Uniquely value added and topologically refined  Used for infrastructure assurance analysis  Also incorporated into Transportation Routing Analysis GIS model, funded by DOE/NTP

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY TRAGIS Routing Model  Transportation Routing Analysis GIS (TRAGIS) model is used to examine highway, rail, and waterway routes for route campaigns or future shipments  Output includes population data along routes for risk assessment models

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Innovative approach using GIS and RS techniques to produce world’s finest population distribution model, database, and tool LandScan Global Population  Accepted standard for estimating population at risk by the DoD and DOS  Federal agencies including DoD (DTRA, NGA), DHS (FEMA, TSA), DOE, USGS, NASA, EPA, and HHS (CDC, NIH) are current users of the data for research and development and routine exercises  Over 1200 non-defense registered users worldwide including WHO and the UN agencies  Used in Rand McNally’s World Goode’s Atlas and National Geographic Maps  Finest spatial resolution (<1km) global population distribution ever produced  Global coverage in consistent raster (GIS) format  Regular (yearly) updates  First to employ satellite imagery worldwide  Allows quick and easy assessment, estimation, and visualization of population at risk  Integrated with transportation, socioeconomic, and consequence assessment models (HPAC)

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY LandScan USA  Very High Spatial Resolution  3 arc second resolution (about 90 m. or 300 ft.) or finer  Finer Temporal Resolution daytime  Nighttime (residential) and daytime population distributions  High Currency  Modeled from best available Census 2000 block data  Value Added  Demographic and socio economic characteristics for easy integration with risk and impact assessment models  Compared to Census Data  Better than census resolution for most city blocks

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Space-Time Visualization

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Intelligent Consequence Management  Real time consequence analysis  Compounding effects from disasters  Dynamic traffic assignment  GIS-data integration  Sensitivity of lead time

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Near-Real Time Population Model Combining  Image processing  GIS modeling  High-performance computing