GEOSPATIAL CYBERINFRASTRUCTURE

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.

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.

HISTORY OF CYBERINFRASTRUCTURE  The term Cyberinfrastructure (CI) was first used in  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).

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.

Courtesy: Yang at al., 2010

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.

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)

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)

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

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.

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.

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.

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.

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

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.

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.

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

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.

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

SYSTEM ARCHITECTURE OF URBAN GCI

GUI FOR THE URBAN GCI

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),  Yang, C., Raskin, R., Goodchild, M., & Gahegan, M. (2010). Geospatial cyberinfrastructure: past, present and future. Computers, Environment and Urban Systems, 34(4),