All Topics

A B C D E F G H I J K L M N O P R S T U V W
DC-04 - Social Media Platforms

Social media is a group of interactive Web 2.0 Internet-based applications that allow users to create and exchange user-generated content via virtual communities. Social media platforms have a large user population who generate massive amounts of digital footprints, which are valuable data sources for observing and analyzing human activities/behavior. This entry focuses on social media platforms that provide spatial information in different forms for Geographic Information Systems and Technology (GIS&T) research. These social media platforms can be grouped into six categories: microblogging sites, social networking sites, content sharing sites, product and service review sites, collaborative knowledge sharing sites, and others. Four methods are available for capturing data from social media platforms, including Web Application Programming Interfaces (Web APIs), Web scraping, digital participant recruitment, and direct data purchasing. This entry first overviews the history, opportunities, and challenges related to social media platforms. Each category of social media platforms is then introduced in detail, including platform features, well-known platform examples, and data capturing processes.

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
FC-07 - Space
  • Differentiate between absolute and relative descriptions of location
  • Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)
  • Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon
  • Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)
  • Select appropriate spatial metaphors and models of phenomena to be represented in GIS
  • Develop methods for representing non-cartesian models of space in GIS
  • Discuss the advantages and disadvantages of the use of cartesian/metric space as a basis for GIS and related technologies
  • Differentiate between common-sense, Cartesian/metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space
FC-37 - Spatial Autocorrelation

The scientific term spatial autocorrelation describes Tobler’s first law of geography: everything is related to everything else, but nearby things are more related than distant things. Spatial autocorrelation has a:

  • past characterized by scientists’ non-verbal awareness of it, followed by its formalization;
  • present typified by its dissemination across numerous disciplines, its explication, its visualization, and its extension to non-normal data; and
  • an anticipated future in which it becomes a standard in data analytic computer software packages, as well as a routinely considered feature of space-time data and in spatial optimization practice.

Positive spatial autocorrelation constitutes the focal point of its past and present; one expectation is that negative spatial autocorrelation will become a focal point of its future.

AM-32 - Spatial autoregressive models
  • Explain Anselin’s typology of spatial autoregressive models
  • Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function
  • Justify the choice of a particular spatial autoregressive model for a given application
  • Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters
  • Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model
  • Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models
CP-08 - Spatial Cloud Computing

The scientific and engineering advancements in the 21st century pose grand computing challenges in managing big data, using complex algorithms to extract information and knowledge from big data, and simulating complex and dynamic physical and social phenomena. Cloud computing emerged as new computing model with the potential to address these computing challenges. This entry first introduces the concept, features and service models of cloud computing. Next, the ideas of generalized architecture and service models of spatial cloud computing are then elaborated to identify the characteristics, components, development and applications of spatial cloud computing for geospatial sciences. 

DM-60 - Spatial Data Infrastructures

Spatial data infrastructure (SDI) is the infrastructure that facilitates the discovery, access, management, distribution, reuse, and preservation of digital geospatial resources. These resources may include maps, data, geospatial services, and tools. As cyberinfrastructures, SDIs are similar to other infrastructures, such as water supplies and transportation networks, since they play fundamental roles in many aspects of the society. These roles have become even more significant in today’s big data age, when a large volume of geospatial data and Web services are available. From a technological perspective, SDIs mainly consist of data, hardware, and software. However, a truly functional SDI also needs the efforts of people, supports from organizations, government policies, data and software standards, and many others. In this chapter, we will present the concepts and values of SDIs, as well as a brief history of SDI development in the U.S. We will also discuss the components of a typical SDI, and will specifically focus on three key components: geoportals, metadata, and search functions. Examples of the existing SDI implementations will also be discussed.  

DC-21 - Spatial data sharing among organizations
  • Describe the rationale for and against sharing data among organizations
  • Describe the barriers to information sharing
  • Describe methods used by organizations to facilitate data sharing
AM-107 - Spatial Data Uncertainty

Although spatial data users may not be aware of the inherent uncertainty in all the datasets they use, it is critical to evaluate data quality in order to understand the validity and limitations of any conclusions based on spatial data. Spatial data uncertainty is inevitable as all representations of the real world are imperfect. This topic presents the importance of understanding spatial data uncertainty and discusses major methods and models to communicate, represent, and quantify positional and attribute uncertainty in spatial data, including both analytical and simulation approaches. Geo-semantic uncertainty that involves vague geographic concepts and classes is also addressed from the perspectives of fuzzy-set approaches and cognitive experiments. Potential methods that can be implemented to assess the quality of large volumes of crowd-sourced geographic data are also discussed. Finally, this topic ends with future directions to further research on spatial data quality and uncertainty.

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