2016 QUARTER 02

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

Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques
Hypothesize the causes of a pattern in the spatial distribution of a phenomenon
Differentiate among distributions in space, time, and attribute
Identify influences of scale on the appearance of distributions
Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute
Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality

Perform an analysis using the geographically weighted regression technique
Discuss the appropriateness of GWR under various conditions
Describe the characteristics of the spatial expansion method
Explain the principles of geographically weighted regression
Compare and contrast GWR with universal kriging using moving neighborhoods
Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity
Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results

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

Describe the ways in which a spatial perspective enables the synthesis of different subjects (e.g., climate and economy)
Describe the common constraints on spatial integration
Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)

State the classic formalization of the interaction model
Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial interaction model
Explain how dynamic, chaotic, complex, or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models
Explain the concept of competing destinations, describing how traditional spatial interaction model forms are modified to account for it
Create a matrix that shows spatial interaction
Differentiate between the gravity model and spatial interaction 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

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

Design point, transect, and area sampling strategies for given applications
Differentiate between situations in which one would use stratified random sampling and systematic sampling
Differentiate among random, systematic, stratified random, and stratified systematic unaligned sampling strategies

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

Search form