1. 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) University of Illinois at Urbana-Champaign Seattle, WA, USA September 16, 2013

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

3. Discoveries Questions Predictions Killer Problems? 3

4. Big Spatial Data 4

5. Big Spatial Simulation Image created by Eric Shook 5

6. Complex Spatial Decision Making 6

8. Collaborative Knowledge Discovery 8

9. Geodesign 9 Image source: http://www.esri.com/news/arcwatch/0412/a-conversation-with-carl-steinitz.htmlhttp://www.esri.com/news/arcwatch/0412/a-conversation-with-carl-steinitz.html

10. CyberGIS for What and Whom? CyberGIS Gateway CyberGIS Toolkit Middleware 10

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13. Big Spatial Data Big Spatial Simulation Complex Spatial Decision Making Collaborative Knowledge Discovery Geo- Design CyberGIS Gateway Yes Maybe Yes Maybe Yes Maybe Yes Maybe Yes Maybe CyberGIS Toolkit Yes Maybe Yes Maybe Yes Maybe Yes Maybe Yes Maybe GISolve Middleware Yes Maybe Yes Maybe Yes Maybe Yes Maybe Yes Maybe 13

14. 14

15. Heterogeneous Syntactic Semantic Dynamic Spatial and temporal E.g. social media Massive Produced by individuals Accessible to individuals 15 Large-scale Global coverage Fine granularity Individual-level High-resolution Distributed access Interoperability Privacy Security Theory + Experiment + Computation + Big Data

16. Digital Environments Parallel o Used to be regarded as a way for speeding up GIS functions and spatial analysis o 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 o Service-oriented o Clouds 16 Extreme-scale computing, information, and communication systems

17. 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 17

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

19. 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 19 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

20. 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 20

21. Computational Intensity Question What is the nature of computational intensity of geographic analysis? o Why spatial is special? Comparable to o “What is the nature of computational complexity of an algorithm?” 21

22. Spatial Computational Principles/Theories Spatial Distribution Dependence Integration Representation Uncertainty Etc. Computational Complexity vs. intensity Uncertainty vs. validity Performance vs. reliability Etc. SCALE 22

23. Scalability 23

24. Usability 24

25. Interoperability 25

27. Reliability 27

28. Reproducibility 28

29. Understanding of Scientific Processes 29

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

31. Curriculum and pedagogy Partnerships Open ecosystems 31

32. CyberGIS Discovery and Innovation Advanced Technologies Wang, S. 2013. “CyberGIS: Blueprint for Integrated and Scalable Geospatial Software Ecosystems.” International Journal of Geographical Information Science, 27 (11), in press Infrastructure Middleware Portal Gateway Platform Service Toolkit Apps Cloud Grid 32

33. www.opensciencegrid.orgwww.xsede.org http://lakjeewa.blogspot.com/20 11/09/what-is-cloud- computing.html Integrated Digital and Spatial Sciences CyberGI S Gateway CyberGIS Toolkit Space-Time Integration & Synthesis GISolve Middleware 33

34. Sustainability Intellectual frontiers Financial o Science challenges are long term and multidisciplinary o Reward mechanisms Accelerate scientific discoveries Reusability Open o Standards o Technologies Social and organizational o Community engagement o Partnerships Department of Energy Oak Ridge National Laboratory Industry US Geological Survey 34

35. CyberGIS Center for Advanced Digital and Spatial Studies CyberGIS Geospatial Sciences and Technologies Advanced Cyberinfrastructure Data-Intensive Applications and Sciences Arts, Emergency Management, Energy, Health, Sustainability, etc. GISolve Spatial Computational Theories / Methods Extreme-Scale Computing, NSF XSEDE, Open Science Grid Spatial Thinking Digital Thinking Integration and Synthesis 35

36. 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) 36

37. Acknowledgments – CIGI 37

38. Thanks! Comments / Questions? Email: shaowen@illinois.edushaowen@illinois.edu 38