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)
Spatial Cloud Computing Spatial MapReduce
Mobile Devices Artificial Intelligence Tools and Platforms for GIS
Cyberinfrastructure Geospatial Technology Transfer
eScience Web GIS
Computing Approaches Enterprise GIS
Origins of Computing & GIS&T: a Computer Systems Perspective   
Origins of Computing & GIS&T: a Perspective on the Role of Peripheral Devices Examples and Applications 
High Throughput Computing and GIS Google Earth Engine
High Performance Computing and GIS ArcGIS Online
GIS&T and Grid Computing GIS&T and Computational Notebooks
Science Gateways GIS&T and Amazon Web Services
  Apache Spark
Social Media and Location-based Services  
Location-based Services  
GIS& the Internet of Things  
Social Media Analytics  
Social Networks  
GIS&T Web Services  

 

CP-04 - Artificial Intelligence Tools and Platforms for GIS

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

CP-13 - 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.

CP-05 - Geospatial Technology Transfer Opportunities, and a Case Study of the Taghreed System

The technology transfer process moves research ideas from preliminary stages in research labs and universities to industrial products and startup companies. Such transfers significantly contribute to producing new computing platforms, services, and geospatial data products based on state-of-the-art research. To put technology transfer in perspective, this entry highlights key lessons learned through the process of transferring the Taghreed System from a research and development (R&D) lab to an industrial product. Taghreed is a system that supports scalable geospatial data analysis on social media microblogs data. Taghreed is primarily motivated by the large percentage of mobile microblogs users, over 80%, which has led to greater availability of geospatial content in microblogs beyond anytime in the digital data history. Taghreed has been commercialized and is powering a startup company that provides social media analytics based on full Twitter data archive.

CP-23 - Google Earth Engine

Google Earth Engine (GEE) is a cloud-based platform for planetary scale geospatial data analysis and communication.  By placing more than 17 petabytes of earth science data and the tools needed to access, filter, perform, and export analyses in the same easy to use application, users are able to explore and scale up analyses in both space and time without any of the hassles traditionally encountered with big data analysis.  Constant development and refinement have propelled GEE into one of the most advanced and accessible cloud-based geospatial analysis platforms available, and the near real time data ingestion and interface flexibility means users can go from observation to presentation in a single window.

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-15 - Mobile Devices

Mobile devices refer to a computing system intended to be used by hand, such as smartphones or tablet computers. Mobile devices more broadly refer to mobile sensors and other hardware that has been made for relatively easy transportability, including wearable fitness trackers. Mobile devices are particularly relevant to Geographic Information Systems and Technology (GIS&T) in that they house multiple locational sensors that were until recently very expensive and only accessible to highly trained professionals. Now, mobile devices serve an important role in computing platform infrastructure and are key tools for collecting information and disseminating information to, from, and among heterogeneous and spatially dispersed audiences and devices. Due to the miniaturization and the decrease in the cost of computing capabilities, there has been widespread social uptake of mobile devices, making them ubiquitous. Mobile devices are embedded in Geographic Information Science (GIScience) meaning GIScience is increasingly permeating lived experiences and influencing social norms through the use of mobile devices. In this entry, locational sensors are described, with computational considerations specifically for mobile computing. Mobile app development is described in terms of key considerations for native versus cross-platform development. Finally, mobile devices are contextualized within computational infrastructure, addressing backend and frontend considerations.

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