All Topics

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.

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.

AM-14 - Spatial process models
  • Discuss the relationship between spatial processes and spatial patterns
  • Differentiate between deterministic and stochastic spatial process models
  • Describe a simple process model that would generate a given set of spatial patterns
FC-13 - Spatial queries
  • Demonstrate the syntactic structure of spatial and temporal operators in SQL
  • State questions that can be solved by selecting features based on location or spatial relationships
  • Construct a query statement to search for a specific spatial or temporal relationship
  • Construct a spatial query to extract all point objects that fall within a polygon
  • Compare and contrast attribute query and spatial query
AM-26 - Spatial sampling for statistical analysis
  • List and describe several spatial sampling schemes and evaluate each one for specific applications
  • Differentiate between model-based and design-based sampling schemes
  • Design a sampling scheme that will help detect when space-time clusters of events occur
  • Create spatial samples under a variety of requirements, such as coverage, randomness, and transects
  • Describe sampling schemes for accurately estimating the mean of a spatial data set
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.

DM-18 - Spatio-temporal GIS
  • Describe extensions to relational DBMS to represent temporal change in attributes
  • Evaluate the advantages and disadvantages of existing space-time models based on storage efficiency, query performance, ease of data entry, and ability to implement in existing software
  • Create a GIS database that models temporal information
  • Utilize two different space-time models to characterize a given scenario, such as a daily commute
  • Describe the architecture of data models (both field and object based) to represent spatio-temporal phenomena
  • Differentiate the two types of temporal information to be modeled in databases: database (or transaction) time and valid (or world) time
  • Identify whether it is important to represent temporal change in a particular GIS application
  • Describe SQL extensions for querying temporal change
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.