2020 QUARTER 04

A B C D E F G H I J K L M N O P R S T U V W
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.

GS-25 - Spatial Decision Support

It has been estimated that 80% of all datasets include geographic references. Since these data often factor into preparing important decisions, we can assume that a significant proportion of all decisions have a geospatial aspect to them. Therefore, spatial decision support is an intrinsic component of societal decision-making. It is thus necessary for current and aspiring analysts, and for decision-makers and other stakeholders, to understand the fundamental concepts, techniques, and challenges of spatial decision support. This GIS&T topic explores the unique nature and basic concepts of spatial decision support, discusses the relationship between Spatial Decision Support Systems (SDSS) and Geographic Information Systems (GIS), and briefly introduces Multi-Criteria Decision Analysis (MCDA) as a decision support technique. The impact of Web-based and mobile information technology, ever-increasing accessibility of geospatial data, and participatory approaches to decision-making are touched upon and additional resources for further reading provided.

AM-33 - Spatial filtering
  • Identify modeling situations where spatial filtering might not be appropriate
  • Demonstrate how spatial autocorrelation can be “removed” by resampling
  • Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem
  • Explain how the Getis and Tiefelsdorf-Griffith spatial filtering techniques incorporate spatial component variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals
  • Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset
  • Describe the relationship between factorial kriging and spatial filtering
DM-66 - Spatial Indexing

A spatial index is a data structure that allows for accessing a spatial object efficiently. It is a common technique used by spatial databases.  Without indexing, any search for a feature would require a "sequential scan" of every record in the database, resulting in much longer processing time. In a spatial index construction process, the minimum bounding rectangle serves as an object approximation. Various types of spatial indices across commercial and open-source databases yield measurable performance differences. Spatial indexing techniques are playing a central role in time-critical applications and the manipulation of spatial big data.

AM-10 - Spatial Interaction

Spatial interaction (SI) is a fundamental concept in the GIScience literature, and may be defined in numerous ways. SI often describes the "flow" of individuals, commodities, capital, and information over (geographic) space resulting from a decision process. Alternatively, SI is sometimes used to refer to the influence of spatial proximity of places on the intensity of relations between those places. SI modeling as a separate research endeavor developed out of a need to mathematically model and understand the underlying determinants of these flows/influences. Proponents of SI modeling include economic geographers, regional scientists, and regional planners, as well as climate scientists, physicists, animal ecologists, and even some biophysical/environmental researchers. Originally developed from theories of interacting particles and gravitational forces in physics, SI modeling has developed through a series of refinements in terms of functional form, conceptual representations of distances, as well as a range of analytically rigorous technical improvements.
 

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