2016 QUARTER 02

A B C D E F G H I K L M N O P R S T U V W
DM3-6 - 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
GC2-8 - Rule learning
  • Describe how a neural network may use training rules to learn from input data
GD9-3 - Sample intervals
  • 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
GD9-1 - Sample size selection
  • 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
DN2-1 - Scale and generalization
  • 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
GD8-3 - Scanning and automated vectorization techniques
  • Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention
AM8-3 - Semi-variogram modeling
  • 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
AM2-1 - Set theory
  • Describe set theory
  • Explain how logic theory relates to set theory
  • Perform a logic (set theoretic) query using GIS software
  • Explain how set theory relates to spatial queries
AM3-3 - Shape
  • 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
GC4-4 - Simulated annealing
  • Outline the rationale for and usefulness of simulated annealing

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