2016 QUARTER 03

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

Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)
Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model
Discuss the degree to which these models can be implemented using current technologies
Design data models for specific applications based on these comprehensive general models
Illustrate major integrated models of geographic information, such as Peuquet’s triad, Mennis’ pyramid, and Yuan’s three-domain

Appraise the possible improvement of integrating GeoAlgebra, Graph-Based Cellular Automata, or agent-based models to overcome the fixed-grid limitations of CA models
Explain the potential contribution of integrating data mining into CA models
Compare and contrast the analysis of a process using a CA with the analysis of the same process in a GIS using map algebra and similar raster operations

Define alternatives to the Tietz and Bart heuristic
Outline the Tietz and Bart interchange heuristic
Describe the process whereby an element within a random solution is exchanged, and if it improves the solution, it is accepted, and if not, it is rejected and another element is tried until no improvement occurs in the objective function value

Explain how the process to break out local optima can be based on a probability function
Outline the TABU heuristic

Identify the spatial concepts that are assumed in different interpolation algorithms
Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting, and kriging
Differentiate between trend surface analysis and deterministic spatial interpolation
Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem
Design an algorithm that interpolates irregular point elevation data onto a regular grid
Outline algorithms to produce repeatable contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting
Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package
Describe how surfaces can be interpolated using splines
Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations
Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)
Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)

Define “intervisibility”
Outline an algorithm to determine the viewshed (area visible) from specific locations on surfaces specified by DEMs
Perform siting analyses using specified visibility, slope, and other surface related constraints
Explain the sources and impact of errors that affect intervisibility analyses

Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential
Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted
Explain why and how density estimation transforms point data into a field representation
Explain why, in some cases, an adaptive bandwidth might be employed
Create density maps from point datasets using kernels and density estimation techniques using standard software
Differentiate between kernel density estimation and spatial interpolation

Explain how spatial data mining techniques can be used for knowledge discovery
Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query
Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets

Describe the relationship between the semi-variogram and kriging
Explain why it is important to have a good model of the semi-variogram in kriging
Explain the concept of the kriging variance, and describe some of its shortcomings
Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support)
Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping
Outline the basic kriging equations in their matrix formulation
Conduct a spatial interpolation process using kriging from data description to final error map
Explain why kriging is more suitable as an interpolation method in some applications than others

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