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State of CyberGIS

State of CyberGIS. Shaowen Wang CyberInfrastructure and Geospatial Information Laboratory (CIGI) Department of Geography and Geographic Information Science Department of Computer Science Department of Urban and Regional Planning National Center for Supercomputing Applications (NCSA)

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State of CyberGIS

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  1. State of CyberGIS Shaowen Wang CyberInfrastructure and Geospatial Information Laboratory (CIGI) Department of Geography and Geographic Information Science Department of Computer Science Department of Urban and Regional Planning National Center for Supercomputing Applications (NCSA) University of Illinois at Urbana-Champaign Seattle, WA, USA September 16, 2013

  2. Chair of the Science Advisory Committee • Michael Goodchild Principal Investigator • Shaowen Wang • Project Manager • Anand Padmanabhan • Co-Principal Investigators • Luc Anselin • Budhendra Bhaduri • Timothy Nyerges • Nancy Wilkins-Diehr • Project Staff • ASU: WenwenLiand Rob Pahle • ORNL: RangaRajuVatsavai • SDSC: ChoonhanYoun • UIUC: Yan Liu and AnandPadmanabhan • Graduate and undergraduate students • Senior Personnel • Michael Goodchild • Sergio Rey • Xuan Shi • Marc Snir • E. Lynn Usery NSF SI2-SSI: CyberGIS Project Team • Industrial Partner: Esri • Steve Kopp and Dawn Wright

  3. DiscoveriesQuestionsPredictionsKiller Problems?

  4. Big Spatial Data

  5. Big Spatial Simulation Image created by Eric Shook

  6. Complex Spatial Decision Making

  7. Collaborative Knowledge Discovery

  8. Geodesign Image source: http://www.esri.com/news/arcwatch/0412/a-conversation-with-carl-steinitz.html

  9. CyberGIS for What and Whom? CyberGIS Toolkit Middleware CyberGIS Gateway

  10. Theory + Experiment + Computation + Big Data • Heterogeneous • Syntactic • Semantic • Dynamic • Spatial and temporal • E.g. social media • Massive • Produced by individuals • Accessible to individuals • Large-scale • Global coverage • Fine granularity • Individual-level • High-resolution • Distributed access • Interoperability • Privacy • Security

  11. Digital Environments • Parallel • Used to be regarded as a way for speeding up GIS functions and spatial analysis • Now becoming a must for GIS and spatial analysis to be built on • Multi- and many-core • GPU (graphics processing unit) • Heterogeneous architecture • Mobile • Distributed • Service-oriented • Clouds Extreme-scale computing, information, and communication systems

  12. Computing Profile Total Peak Performance 11.61 PF Total System Memory 1.476 PB XE Compute Cabinets 237 XE Peak Performance 7.1 PF XE Compute Nodes 22,640 XE Bulldozer Cores 362,240 XE System Memory 1.382 PB XK Compute Cabinets 32 XK Peak Performance (CPU+GPU) 4.51 PF XK Compute Nodes 3072 XK Bulldozer Cores (CPU) 24,576 XK Kepler Accelerators (GPU) 3072 XK System Memory (CPU) 96 TB XK Accelerator Memory (GPU) 18 TB Online Storage Total Usable Storage 26.4 PB Aggregate I/O Bandwidth > 1 TB/s Near-line Storage Aggregate Bandwidth to tape 58 GB/s 5-year capacity 380 PB

  13. Image source: http://gigaom.com/2010/12/14/facebook-draws-a-map-of-the-connected-world/ via Mike Goodchild

  14. Spatial Computational Domain • Sufficiently coarse to ensure that the derivation and decomposition of the spatial computational domain is computationally inexpensive • Sufficiently fine to allow domain decomposition to produce a large number of sub-domains that are executed concurrently to improve computational performance Wang, S., and Armstrong, M. P. 2009. “A Theoretical Approach to the Use of Cyberinfrastructure in Geographical Analysis.” International Journal of Geographical Information Science, 23 (2): 169-193

  15. A Hierarchical Computational Framework for Agent-based Modeling Tang, W.and Wang, S. 2009 “HPABM: A Hierarchical Parallel Simulation Framework for Spatially-Explicit Agent-Based Models.” Transactions in GIS, 13 (3): 315-333

  16. Computational Intensity Question • What is the nature of computational intensity of geographic analysis? • Why spatial is special? • Comparable to • “What is the nature of computational complexity of an algorithm?”

  17. Spatial Computational Principles/Theories • Spatial • Distribution • Dependence • Integration • Representation • Uncertainty • Etc. • Computational • Complexity vs. intensity • Uncertainty vs. validity • Performance vs. reliability • Etc. SCALE

  18. Scalability

  19. Usability

  20. Interoperability

  21. Reliability

  22. Reproducibility

  23. Understanding of Scientific Processes

  24. Education and Workforce Development • CyberGIS Gateway used by hundreds of undergraduate and graduate students on multiple campuses • Graduated 6 graduate students and trained 4 postdoctoral fellows • CyberGIS’12 (http://www.cigi.illinois.edu/cybergis12/): The First International Conference on Space, Time, and CyberGIS • CyberGIS Symposium at the 2013 Annual Meeting of the Association of American Geographers – 17 sessions • Tutorials • CyberGIS, GIScience, SC, TeraGrid/XSEDE

  25. Curriculum and pedagogy • Partnerships • Open ecosystems

  26. Discovery and Innovation Apps Service Toolkit CyberGIS Portal Platform Gateway Infrastructure Cloud Grid Middleware Advanced Technologies Wang, S. 2013. “CyberGIS: Blueprint for Integrated and Scalable Geospatial Software Ecosystems.” International Journal of Geographical Information Science, 27 (11), in press

  27. Integrated Digital and Spatial Sciences Space-Time Integration & Synthesis CyberGIS Toolkit CyberGIS Gateway GISolve Middleware http://lakjeewa.blogspot.com/2011/09/what-is-cloud-computing.html www.opensciencegrid.org www.xsede.org

  28. Sustainability • Intellectual frontiers • Financial • Science challenges are long term and multidisciplinary • Reward mechanisms • Accelerate scientific discoveries • Reusability • Open • Standards • Technologies • Social and organizational • Community engagement • Partnerships • Department of Energy Oak Ridge National Laboratory • Industry • US Geological Survey

  29. CyberGIS Center for Advanced Digital and Spatial Studies Spatial Thinking Digital Thinking Arts, Emergency Management, Energy, Health, Sustainability, etc. Data-Intensive Applications and Sciences Geospatial Sciences and Technologies Spatial Computational Theories / Methods Integration and Synthesis GISolve CyberGIS Extreme-Scale Computing, NSF XSEDE, Open Science Grid Advanced Cyberinfrastructure

  30. Acknowledgments • Federal Agencies • US Geological Survey • Department of Energy’s Office of Science • National Science Foundation • BCS-0846655 • EAR-1239603 • OCI-1047916 • PHY-0621704 • PHY-1148698 • TeraGrid/XSEDE SES070004 • US Geological Survey • Industry • Environmental Systems Research Institute (Esri) • Silicon Graphics, Inc. (SGI)

  31. Acknowledgments – CIGI

  32. Thanks! • Comments / Questions? • Email: shaowen@illinois.edu

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