1. GEOSPATIAL CYBERINFRASTRUCTURE

2. WHAT IS CYBERINFRASTRUCTURE(CI)?  A combination of data resources, network protocols, computing platforms, and computational services.  It brings people, information and computational tools together to perform science or other data-rich applications in this information-driven world.

3. WHAT IS GEOSPATIAL CI?  Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains.

4. HISTORY OF CYBERINFRASTRUCTURE  The term Cyberinfrastructure (CI) was first used in 1998.  In 2003, it was formally used by the NSF Computer & Information Science & Engineering (CISE) Directorate; an Office of CI (OCI) was established to advance the research, development, and construction of CI.  NSF has been a major driver in the development of CI and has made significant strategic investments in CI development in targeted domains (e.g. ecology, hydrology, social sciences).

5. HISTORY OF GEOSPATIAL CI (GCI)  In 1994, the US Federal Geographic Data Committee (FGDC) was established to build a cross-agency National Spatial Data Infrastructure (NSDI).  The Association of American Geographers (AAG)  The Infrastructure for Spatial Information in the European Community (INSPIRE)  The geospatial information integration and the geospatial functions distinguish GCI from other generic CIs.

6. Courtesy: Yang at al., 2010

7. CURRENT PROGRESS OF GCI  T he amount and availability of geographic information (GI) has grown exponentially.  A new dedicated GCI is needed to process and integrate GI to:  supply geospatial analysis and modeling as services;  support scientific and application problem solving across geographic regions;  provide LBS for stakeholders, such as place-based policy makers.

9. GCI RESOURCES & FRAMEWORK  GCI includes multiple categories of resources within a flexible, scalable, and expandable framework cube, consisting of 3 dimensions:  Functions: both generic CI functions (e.g. computing, networking) and those geospatial- specific  Enabling technologies that provide technological support to invent, mature, and maintain all GCI functions.  Communities that represent the virtual organizations and end-user interactions within specific research domains (e.g.Earth sciences, GIS)

10. ENABLING TECHNOLOGIES  Earth observation and sensor networks  SDI (Spatial Data Infrastructure)  D-GIP (Distributed geographic information processing)  Web computing  Open and interoperable access technologies  HPC (High-Performance Computing)

11.  Open-source software and middleware  Cross-domain sharing and collaborations  System integration architectures

12. EARTH OBSERVATION AND SENSOR NETWORKS  Passive logging systems  Intelligent sensor networks that actively send data to servers  Real-time sensor networks  An increasingly dependence on real-time information.  A hot topic in the coming decades.

13. D-GIP  D-GIP handles geospatial information for GCI using distributed computing resources across platforms.  Geospatial processing functions need to be rewritten to fit into GCI.  D-GIP research will provide a guiding methodology and principles for implementing geospatial middleware that can support geospatial processing in GCI.

14. WEB COMPUTING  Web 2.0 (3.0?) provides an important platform for GCI applications (e.g. online data searching, mapping, and utilization).  Supports uniform interfaces (e.g. Google Maps) for the exploration of scientific data.  Further advances in web computing are toward an intelligent Semantic Web.

15. OPEN AND INTEROPERABLE ACCESS  XML/GML, JavaScript, and AJAX  Enable geospatial data to be published, accessed easily, and adapted to customized applications through mashups.  Spatial Web portals and gateways have enabled access to supercomputing and information systems.

16. HPC (HIGH-PERFORMANCE COMPUTING)  Grid computing, cluster computing, and ubiquitous computing  Provides computing power for GCI users to conduct big data and computationally intensive research  Much research is needed on how to leverage HPC for geospatial information

17. OPEN-SOURCE SOFTWARE AND MIDDLEWARE  Open-source software is often used to integrate the components of data, processing, applications, and infrastructure.  Middleware technology allows for the adaption of desktop-based geospatial software to a GCI.  Challenges: how to effectively distribute, synchronize, integrate, and balance the geospatial processing or computing within a distributed environment.

18. CROSS-DOMAIN SHARING AND COLLABORATIONS  Essential for a GCI to support and leverage expertise across user communities.  A multi-domain perspective  Sufficiently expandable and flexible to support the easy plug-and-play of new functions  Service-oriented architecture (SOA)  Standards-based interoperable interfaces and open- source access.

19. LONG-TERM OBJECTIVES GCI will facilitate  building the capacity to leverage existing geospatial knowledge and resources  collaborating across geographic regions and domain turfs.  transforming how we conduct research, answer scientific questions and support applications

20. EXAMPLE OF GCI Li, W. et al. (2013). A geospatial cyberinfrastructure for urban economic analysis and spatial decision-making. ISPRS International Journal of Geo-Information This GCI provides an operational GUI, built upon a service- oriented architecture to allow ：  widespread sharing and seamless integration of distributed geospatial data  an effective way to deal with the uncertainty in fusing data from diverse sources  the decomposition of complex planning questions into atomic spatial analysis tasks  the generation of a web service chain to tackle such complex problems  capturing and representing provenance of geospatial data to trace its flow in the modeling task.

21. METHODOLOGY  Conflation: integrating data from multiple sources in order to generate a new dataset with improved spatial and attribute accuracies,  Service Chain of Geospatial Processes to support sharing of geospatial data hosted in the urban GCI  Web Map Services (WMS)  Web Feature Services (WFS)  Web Processing Service (WPS),  Data and Analytic Provenance: the ability to trace the source and flow of data throughout the process of complex geospatial analysis

22. SYSTEM ARCHITECTURE OF URBAN GCI

23. GUI FOR THE URBAN GCI

24. REFERENCES  Liu, F., Tong, J., Mao, J., Bohn, R., Messina, J., Badger, L., & Leaf, D. (2011). NIST cloud computing reference architecture. NIST special publication, 500, 292.  Li, W., Li, L., Goodchild, M. F., & Anselin, L. (2013). A geospatial cyberinfrastructure for urban economic analysis and spatial decision-making. ISPRS International Journal of Geo-Information, 2(2), 413-431.  Yang, C., Raskin, R., Goodchild, M., & Gahegan, M. (2010). Geospatial cyberinfrastructure: past, present and future. Computers, Environment and Urban Systems, 34(4), 264-277.