Computing Platforms

Computing Platforms provide the computational capabilities to apply methods and models to geographic data. Computing Platforms vary in capability, price, and availability from mobile devices to advanced supercomputers and from standalone computers to complex networked infrastructures to address different user needs and data-processing workloads.

Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized

Computing Infrastructures:   Software Systems
Graphics Processing Units   Spatial Database Management Systems (DBMS)
The Cloud   Spatial MapReduce
Mobile Devices   Artificial Intelligence
Cyberinfrastructure   Software Systems
    Web GIS
Computing Approaches:    
History of Computing & GIS&T   Examples and Applications
High Performance Computing   Computational Geography
Grid Computing   Computational Social Science
Pervasive/Ubiquitous Computing   ArcGIS Online
Science Gateways   Google Earth Engine
    eScience
Networks and Services:   Jupyter Notebooks
Location-based Services    
Internet of Things    
Social Media Analytics    
Social Networks    
Security    
OGC / Web Service Standards    

 

CP-04 - Artificial intelligence
  • Describe computational intelligence methods that may apply to GIS&T
  • Exemplify the potential for machine learning to expand performance of specialized geospatial analysis functions
  • Identify artificial intelligence tools that may be useful for GIS&T
  • Describe a hypothesis space that includes searches for optimality of solutions within that space
CP-07 - Cyberinfrastructure

Cyberinfrastructure (sometimes referred to as e-infrastructure and e-science) integrates cutting-edge digital environments to support collaborative research and education for computation- and/or data-intensive problem solving and decision making (Wang 2010).

CP-29 - Enterprise GIS

Enterprise GIS is the implementation of GIS infrastructure, processes and tools at scale within the context of an organization, shaped by the prevailing information technology patterns of the day. It can be framed as an infrastructure enabling a set of capabilities, and a process for establishing and maintaining that infrastructure. Enterprise GIS facilitates the storage, sharing and dissemination of geospatial information products (data, maps, apps) within an organization and beyond. Enterprise GIS is integrated into, and shaped by the business processes, culture and context of an organization. Enterprise GIS implementations require general-purpose IT knowledge in the areas of performance tuning, information security, maintenance, interoperability, and data governance. The specific enabling technologies of Enterprise GIS will change with time, but currently the prevailing pattern is a multi-tiered services-oriented architecture supporting delivery of GIS capabilities on the web, democratizing access to and use of geospatial information products.

PD-13 - GPU Programming for GIS Applications

Graphics processing units (GPUs) are massively parallel computing environments with applications in graphics and general purpose programming. This entry describes GPU hardware, application domains, and both graphics and general purpose programming languages.

CP-06 - Graphics Processing Units (GPUs)

Graphics Processing Units (GPUs) represent a state-of-the-art acceleration technology for general-purpose computation. GPUs are based on many-core architecture that can deliver computing performance much higher than desktop computers based on Central Processing Units (CPUs). A typical GPU device may have hundreds or thousands of processing cores that work together for massively parallel computing. Basic hardware architecture and software standards that support the use of GPUs for general-purpose computation are illustrated by focusing on Nvidia GPUs and its software framework: CUDA. Many-core GPUs can be leveraged for the acceleration of spatial problem-solving.  

CP-03 - High performance computing
  • Describe how the power increase in desktop computing has expanded the analytic methods that can be used for GIS&T
  • Exemplify how the power increase in desktop computing has expanded the analytic methods that can be used for GIS&T
CP-12 - Location-Based Services

Location-Based Services (LBS) are mobile applications that provide information depending on the location of the user. To make LBS work, different system components are needed, i.e., mobile devices, positioning, communication networks, and service and content provider. Almost every LBS application needs several key elements to handle the main tasks of positioning, data modeling, and information communication. With the rapid advances in mobile information technologies, LBS have become ubiquitous in our daily lives with many application fields, such as navigation and routing, social networking, entertainment, and healthcare. Several challenges also exist in the domain of LBS, among which privacy is a primary one. This topic introduces the key components and technologies, modeling, communication, applications, and the challenges of LBS.

CP-10 - Social Media Analytics

Social media streams have emerged as new sources to support various geospatial applications. However, traditional geospatial tools and systems lack the capacities to process such data streams, which are generated dynamically in extremely large volumes and with versatile contents. Therefore, innovative approaches and frameworks should be developed to detect an emerging event discussed over the social media, understand the extent, consequences of the event, as well as it time-evolving nature, and eventually discover useful patterns. In order to harness social media for geospatial applications, this entry introduces social media analytics technologies for harvesting, managing, mining, analyzing and visualizing the spatial, temporal, text, and network information of social media data.

CP-21 - Social Networks

This entry introduces the concept of a social network (SN), its components, and how to weight those components. It also describes some spatial properties of SNs, and how to embed SNs into GIS. SNs are graph structures that consists of nodes and edges that traditionally exist in Sociology and are newer to GIScience. Nodes typically represent individual entities such as people or institutions, and edges represent interpersonal relationships, connections or ties. Many different mathematical metrics exist to characterize nodes, edges and the larger network. When geolocated, SNs are part of a class of spatial networks, more specifically, geographic networks (i.e. road networks, hydrological networks), that require special treatment because edges are non-planar, that is, they do not follow infrastructure or form a vector on the earth’s surface. Future research in this area is likely to take advantage of 21st Century datasets sourced from social media, GPS, wireless signals, and online interactions that each evidence geolocated personal relationships.

CP-01 - Software systems
  • Describe the major geospatial software architectures available currently, including desktop GIS, server-based, Internet, and component-based custom applications
  • Describe non-spatial software that can be used in geospatial applications, such as databases, Web services, and programming environments
  • Compare and contrast the primary sources of geospatial software, including major and minor commercial vendors and open-source options
  • List the major functionality needed from off-the-shelf software based on a requirements report
  • Identify software options that meet functionality needs for a given task or enterprise
  • Evaluate software options that meet functionality needs for a given task or enterprise

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