2019 QUARTER 01

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

Discuss appropriate applications of the various datum transformation options
Explain the difference between NAD 27 and NAD 83 in terms of ellipsoid parameters
Outline the historical development of horizontal datums
Explain the difference in coordinate specifications for the same position when referenced to NAD 27 and NAD 83
Explain the rationale for updating NAD 27 to NAD 83
Explain why all GPS data are originally referenced to the WGS 84 datum
Identify which datum transformation options are available and unavailable in a GIS software package
Define “horizontal datum” in terms of the relationship between a coordinate system and an approximation of the Earth’s surface
Describe the limitations of a Molodenski transformation and in what circumstances a higher parameter transformation such as Helmert may be appropriate
Determine the impact of a datum transformation from NAD 27 to NAD 83 for a given location using a conversion routine maintained by the U.S. National Geodetic Survey
Explain the methodology employed by the U.S. National Geodetic Survey to transform control points from NAD 27 to NAD 83
Perform a Molodenski transformation manually
Use GIS software to perform a datum transformation

Compare and contrast the impacts of different conversion approaches, including the effect on spatial components
Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)
Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity (e.g., converting a digital elevation model into a TIN)

Discuss the importance of planning for implementation as opposed to “winging it”
Discuss pros and cons of different implementation strategies (e.g., spiral development versus waterfall development) given the needs of a particular system
Create a budget for the resources needed to implement the system
Create a schedule for the implementation of a geospatial system based on a complete design

Describe the advantages and disadvantages to an organization in using GIS portal information from other organizations
Describe how inter-organization GIS portals may impact or influence issues related to social equity, privacy and data access
Discuss how distributed GIS&T may affect the nature of organizations and relationships among institutions
Suggest the possible societal and ethical implications of distributed GIS&T

Select two effective methods of overcoming resistance to change
Illustrate how methods for overcoming resistance to change can aid implementation of a GIS
Explain how resistance to change and the need to standardize operations when trying to incorporate GIS&T can promote inclusion into existing job classifications

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

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