2017 QUARTER 01

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

Describe the need for user-centered requirements analysis
Create requirements reports for individual potential applications in terms of the data, procedures, and output needed
Assess the relative importance and immediacy of potential applications
Synthesize the needs of individual users and tasks into enterprise-wide needs
Differentiate between the responsibilities of the proposed system and those that remain with the user
Illustrate how a business process analysis can be used to identify requirements during a GIS implementation
Describe how spatial data and GIS&T can be integrated into a workflow process
Evaluate how external spatial data sources can be incorporated into the business process
Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, and/or joint application development (JAD)
Document existing and potential tasks in terms of workflow and information flow

Illustrate and explain the distinction between resolution, precision, and accuracy
Discuss the implications of the sampling theorem (? = 0.5 d) to the concept of resolution
Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument
Explain how resampling affects the resolution of image data
Discuss the advantages and potential problems associated with the use of minimum mapping unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps
Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution
Illustrate the impact of grid cell resolution on the information that can be portrayed
Relate the concept of grid cell resolution to the more general concept of “support” and granularity
Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software
Evaluate the ease of measuring resolution in different types of tessellations

Identify the fundamental principle of the sampling theorem for specifying a sampling rate or interval
Discuss what sampling intervals should be used to investigate some of the temporal patterns encountered in oceanography
Propose a sampling strategy considering a variable range in autocorrelation distances for a variable

Determine the minimum number and distribution of point samples for a given study area and a
Determine minimum homogeneous ground area for a particular application
Describe how spatial autocorrelation influences selection of sample size and sample statistics
Assess the practicality of statistically reliable sampling in a given situation
given statistical test of thematic accuracy

Explain why the reduction of map scale sometimes results in the need for mapped features to be reduced in size and moved
Identify mapping tasks that require each of the following: smoothing, aggregation, simplification, and displacement
Illustrate specific examples of feature elimination and simplification suited to mapping at smaller scales
Apply appropriate selection criteria to change the display of map data to a smaller scale
Discuss the limitations of current technological approaches to generalization for mapping purposes
Explain how generalization of one data theme can and must be reflected across multiple themes (e.g., if the river moves, the boundary, roads and towns also need to move)
Explain how the decisions for selection and generalization are made with regard to symbolization in mapping

Differentiate among the concepts of scale (as in map scale), support, scope, and resolution
Discuss the implications of tradeoff between data detail and data volume
Select a level of data detail and accuracy appropriate for a particular application (e.g., viewshed analysis, continental land cover change)
Defend or refute the statement “GIS data are scaleless”
Determine the mathematical relationships among scale, scope, and resolution, including Töpfer’s radical law

List the possible sources of error in a selected and fitted model of an experimental semi-variogram
Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
Explain the necessity of defining a semi-variogram model for geographic data
Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets
Describe some commonly used semi-variogram models

Identify situations in which shape affects geometric operations
Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull
Develop an algorithm to determine the skeleton of polygons
Find centroids of polygons under different definitions of a centroid and different polygon shapes
Calculate several different shape indices for a polygon dataset
Compare and contrast different shape indices, include examples of applications to which each could be applied
Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data
Exemplify situations in which the centroid of a polygon falls outside its boundary
Explain why the shape of an object might be important in analysis

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