All Topics

A B C D E F G H I K L M N O P R S T U V W
DM-65 - 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.

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.
 

CP-07 - Spatial MapReduce

MapReduce has become a popular programming paradigm for distributed processing platforms. It exposes an abstraction of two functions, map and reduce, which users can define to implement a myriad of operations. Once the two functions are defined, a MapReduce framework will automatically apply them in parallel to billions of records and over hundreds of machines. Users in different domains are adopting MapReduce as a simple solution for big data processing due to its flexibility and efficiency. This article explains the MapReduce programming paradigm, focusing on its applications in processing big spatial data. First, it gives a background on MapReduce as a programming paradigm and describes how a MapReduce framework executes it efficiently at scale. Then, it details the implementation of two fundamental spatial operations, namely, spatial range query and spatial join. Finally, it gives an overview of spatial indexing in MapReduce systems and how they can be combined with MapReduce processing.

PD-18 - SpatialMPI: Message Passing Interface for GIS Applications

MPI (Message Passing Interface) is a widely used message passing library for writing parallel programs. The goal of MPI is to establish a portable, efficient, and flexible standard for message passing that will be widely used for writing message passing programs. This chapter motivates the need for using MPI for implementing GIS applications and introduces MPI data types and communication functions. Then, it presents new spatial data types and operations on them using MPI. Finally, it presents pseudocode for parallelizing a range query problem and spatial domain decomposition in GIS.

CV-17 - Spatiotemporal Representation

Space and time are integral components of geographic information. There are many ways in which to conceptualize space and time in the geographic realm that stem from time geography research in the 1960s. Cartographers and geovisualization experts alike have grappled with how to represent spatiotemporal data visually. Four broad types of mapping techniques allow for a variety of representations of spatiotemporal data: (1) single static maps, (2) multiple static maps, (3) single dynamic maps, and (4) multiple dynamic maps. The advantages and limitations of these static and dynamic methods are discussed in this entry. For cartographers, identifying the audience and purpose, medium, available data, and available time to design the map are vital aspects to deciding between the different spatiotemporal mapping techniques. However, each of these different mapping techniques offers its own advantages and disadvantages to the cartographer and the map reader. This entry focuses on the mapping of time and spatiotemporal data, the types of time, current methods of mapping, and the advantages and limitations of representing spatiotemporal data.

PD-17 - SQL Languages for GIS

SQL (Structured Query Language) is a declarative programming language that is closely linked to the relational database model. It is an accessible and widely adopted language used for query, data modification, and data definition—that is, defining data structures (tables) and other database objects. Important additions to the SQL standard include SQL/PSM, which adds control flow, local variables, and other procedural language features; and SQL/MM Part 3, which adds spatial support. Many complex geoprocessing workflows typically implemented in desktop GIS or scripting languages can easily be implemented in spatial SQL.

KE-03 - Strategic Planning for GIS Design

Geographic Information Systems (GIS) are pervasive and have become an essential feature in many professional disciplines. Prior to adoption, implementation or use of any GIS, a system must be properly designed to meet its organizational goals, and this requires comprehensive strategic planning to take place ahead of the design. In this article, we discuss methods for strategic planning in GIS design, drawing from literature in Information Systems and GIS research and practice, and business management. We present a four-step approach toward planning for GIS design that will ensure the system is well-suited to further an organization’s long-term functions, applications, and users’ needs.

CV-08 - Symbolization and the Visual Variables

Maps communicate information about the world by using symbols to represent specific ideas or concepts. The relationship between a map symbol and the information that symbol represents must be clear and easily interpreted. The symbol design process requires first an understanding of the underlying nature of the data to be mapped (e.g., its spatial dimensions and level of measurement), then the selection of symbols that suggest those data attributes. Cartographers developed the visual variable system, a graphic vocabulary, to express these relationships on maps. Map readers respond to the visual variable system in predictable ways, enabling mapmakers to design map symbols for most types of information with a high degree of reliability.

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