2016 QUARTER 02

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

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

Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)
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)

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

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

Compare and contrast co-kriging, log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging, and universal kriging
Interpret the results of universal kriging
Apply universal kriging to appropriate data sets

Identify the positions necessary to design and implement a GIS
Discuss the advantages and disadvantages of outsourcing elements of the implementation of a geospatial system, such as data entry
Evaluate the labor needed in past cases to build a new geospatial enterprise
Create a budget of expected labor costs, including salaries, benefits, training, and other expenses

Describe how Lagrangian relaxation can provide approximate solutions to complex problems

Distinguish between GIS, LIS, and CAD/CAM in the context of land records management
Evaluate the difference in accuracy requirements for deeds systems versus registration systems
Exemplify and compare deed descriptions in terms of how accurately they convey the geometry of a parcel
Distinguish between topological fidelity and geometric accuracy in the context of a plat map

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