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A CyberGIS Environment for Near-Real-Time Spatial Analysis of Social Media Data A CyberGIS Environment for Near-Real-Time Spatial Analysis of Social Media.

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Presentation on theme: "A CyberGIS Environment for Near-Real-Time Spatial Analysis of Social Media Data A CyberGIS Environment for Near-Real-Time Spatial Analysis of Social Media."— Presentation transcript:

1 A CyberGIS Environment for Near-Real-Time Spatial Analysis of Social Media Data A CyberGIS Environment for Near-Real-Time Spatial Analysis of Social Media Data 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 NSF-CDI Specialist Meeting Knowledge Discovery in Cyberspace and Big Data San Diego, CA August 7, 2013

2 Cyberinfrastructure – A Simplified View Data / Information ComputingCommunication People Integration Collaboration

3 Advanced Cyberinfrastructure Examples 11/09/what-is-cloud- computing.html

4 CyberGIS – A Tetrahedron View Data / Information ComputingCommunication Geo Spatial CyberGIS

5 What is special about “G” in CyberGIS? Location Place Space Spatiotemporal o Integration o Synthesis

6

7 CyberGIS FluMapper Purpose: Early and fine- spatiotemporal-scale detection of flu outbreak Hypothesis: Is such detection feasible based on social media data?

8 Demo

9 Questions – Scientific Problem Solving How to detect, represent, and communicate spatiotemporal patterns of flu risk? How to reveal spatial diffusion trajectories across various spatiotemporal scales?

10 Wang, S., Cao, G., Zhang, Z., Zhao, Y., and Padmanabhan, A “A CyberGIS Environment for Analysis of Location-Based Social Media Data.” In: Location-Based Computing and Services, 2nd Edition, ed. A. K. Hassan and H. Amin, CRC Press, pages:

11 FluMapper Components Data collection and processing o Collects, processes and stores streaming data from Twitter in near real time o Scalable services to query raw and derived data Spatiotemporal data model o Provides aggregated data and statistics at multiple scales for efficient information retrieval o At the finest scale, the conterminous United States is represented as a field of 30-arc second resolution Exploratory data analysis o Kernel density estimation (KDE) o Monte-Carlo simulations Flow mapping o Single-source flow mapping is applied to depict movement patterns

12 (May 23 ~ June 5, 2013) Spatiotemporal Data Cube

13 A 2D Illustrative Example

14 Questions – CyberGIS How to model and analyze big data that are not collected for the purpose of intended spatiotemporal analysis? How to integrate hybrid spatiotemporal analyses? How to replicate and validate such analyses? What are the key CyberGIS characteristics? What are the basic building blocks of CyberGIS?

15 NSF CyberGIS Project $4.43 million, Year: 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 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

16 Overarching Goal Establish CyberGIS as a fundamentally new software framework comprising a seamless integration of advanced cyberinfrastructure, GIS, and spatial analysis and modeling capabilities and, thus, leads to widespread scientific breakthroughs and broad societal impacts 16

17 Long Tail – CyberGIS for Whom? CyberGIS Gateway CyberGIS Toolkit GISolve 17

18 GISolve Middleware 18

19 Integration Framework 19

20 Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M. F., Liu, Y., and Nyerges, T. L. “CyberGIS Software: A Synthetic Review and Integration Roadmap.” International Journal of Geographical Information Science, DOI: /

21 CyberGIS Gateway – Broad Approach – Lowering Entry Door to CyberGIS Analytics 21

22 CyberGIS Toolkit – Deep Approach 22 Integrated with advanced cyberinfrastructure Plug and play Geo/spatial as an integration axis Open Access Community Source Service

23 Science Drivers and Applications Climate science Emergency management Geographic information science Geography and spatial sciences Hydrology Humanities Political science Public health Sustainability science 23

24 Cyber + GIS > Cyber | GIS 24 Cyber GIS

25 Education and Workforce Curriculum and pedagogy Open ecosystems o CyberGIS Gateway o CyberGIS Toolkit Partnerships 25

26 Vision Spatial Thinking Computational Thinking Cyberinfrastructure Data-Intensive Sciences and Applications CyberGI S Gateway CyberGIS Toolkit Space-Time Integration & Synthesis GISolve Middleware

27 A collaborative software framework encompassing many research fields Geo Spatial Empowering numerous applications and sciences Seamless integration of advanced cyberinfrastructure, GIS, and spatial analysis and modeling Capable of handling huge volumes of data, complex analysis and visualization required for many challenging applications Empower high-performance and collaborative geospatial problem solving Gain fundamental understanding of scalable and sustainable CyberGIS ecosystems 27

28 Acknowledgments Federal Agencies Department of Energy’s Office of Science National Science Foundation –BCS –EAR –OCI –PHY –PHY –TeraGrid/XSEDE SES Industry Environmental Systems Research Institute (Esri) Silicon Graphics, Inc. (SGI) 28

29 Acknowledgments – CIGI 29

30 Thanks! Comments/Questions?


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