## 2016 QUARTER 03

##### DC-08 - 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
##### DC-06 - 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
##### FC-23 - 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
##### CV-04 - Scale and Generalization
• 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
##### DC-05 - 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
##### AM-28 - 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
##### FC-11 - 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
##### FC-15 - 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
##### AM-76 - Simulated annealing
• Outline the rationale for and usefulness of simulated annealing
##### AM-84 - Simulation modeling
• Conduct an experiment using simulation techniques from an activity perspective
• Explain how a simulation from an activity perspective can be used in transportation
• Discuss important computational laboratory tools for creating new models and visualizing model simulations and model outcomes
• Discuss whether, when prior information is absent, repeatedly generating random synthetic datasets can be used to provide statistical significance
• Discuss Monte Carlo simulation use in GIS&T
• Discuss effective scientific use of supervisory genetic algorithms with agent-based simulation models
• Describe how supervisory search and optimization methods can be used to analyze key characteristics of initial conditions and results and to optimize results based on systematic targeted search through the parameter and random seed spaces