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   Key-Value Stores / MapReduce
Mobile Devices   Artificial Intelligence
Cyber infrastructure   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-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-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
CP-05 - Technology transfer
  • Explain how an understanding of use of current and proposed technology in other organizations can aid in implementing a GIS
CP-02 - User interfaces
  • Design an application-level software/user interface based on user requirements
  • Create user interface components in available development environments
CP-14 - Web GIS

Web GIS allows the sharing of GIS data, maps, and spatial processing across private and public computer networks. Understanding web GIS requires learning the roles of client and server machines and the standards and protocols around how they communicate to accomplish tasks. Cloud computing models have allowed web-based GIS operations to be scaled out to handle large jobs, while also enabling the marketing of services on a per-transaction basis.

A variety of toolkits allow the development of GIS-related websites and mobile apps. Some web GIS implementations bring together map layers and GIS services from multiple locations. In web environments, performance and security are two concerns that require heightened attention. App users expect speed, achievable through caching, indexing, and other techniques. Security precautions are necessary to ensure sensitive data is only revealed to authorized viewers.

Many organizations have embraced the web as a way to openly share spatial data at a relatively low cost. Also, the web-enabled expansion of spatial data production by nonexperts (sometimes known as “neogeography”) offers a rich field for alternative mappings and critical study of GIS and society.