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 linked directly to either their original (2006) or revised entries; 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 portals/WebGIS
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    
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-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