2016 QUARTER 02

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

Compare and contrast the prototypical corporate cultures of a MIS department and a GIS department
Describe the issues to consider when integrating with MIS in relation to personnel, hardware, software, and data
Draw conclusions from previous cases of GIS&T and MIS integration, including successes and failures
Make a business case for or against integrating GIS&T and MIS in the context of a particular organization
Compare and contrast the readiness of GIS&T professionals to learn MIS skills versus the readiness of MIS professionals to learn GIS&T skills

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

Discuss the roles traditionally performed by software vendors in developing professionals in GIS&T
Describe how inter-organization GIS portals may impact issues related to social equity, privacy, and data access
Discuss the mission, history, constituencies, and activities of user conferences hosted by software vendors
Describe the advantages and disadvantages to an organization in using GIS portal information from other organizations or entities (private, public, non-profit)
Discuss the history of major geospatial-centric companies, including software, hardware, and data vendors

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

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

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