2016 QUARTER 03

A B C D E F G H I K L M N O P R S T U V W
DC-18 - Algorithms and processing
  • Differentiate supervised classification from unsupervised classification
  • Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
  • Compare pixel-based image classification methods with segmentation techniques
  • Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
  • Describe an application of hyperspectral image data
  • Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
  • Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
  • Describe a situation in which filtered data are more useful than the original unfiltered data
  • Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
AM-02 - Analytical approaches
  • Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling
  • Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations
  • Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis
  • Differentiate between geostatistics and spatial statistics
  • Discuss situations when it is desirable to adopt a spatial approach to the analysis of data
  • Explain what is added to spatial analysis to make it spatio-temporal analysis
  • Explain what is special (i.e., difficult) about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems
  • Outline the sequence of tasks required to complete the analytical process for a given spatial problem
  • Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis
AM-11 - Analyzing multidimensional attributes
  • Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space
  • Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions
  • Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem
  • Produce plots in several data dimensions using a data matrix of attributes
  • Assemble a data matrix of attributes
KE-04 - Application user assessment
  • Identify current and potential users of geospatial technology in an enterprise
  • Identify new geographic tasks or information that align with institutional missions and goals
  • Educate potential users on the value of geospatial technology
  • Classify potential users as casual or professional, early adopters or reluctant users
  • Recognize geographic tasks and geographic information that already exist in an enterprise
  • Evaluate the potential for using geospatial technology to improve the efficiency and/or effectiveness of existing activities
  • Differentiate the concepts of efficiency and effectiveness in application requirements
DA-02 - Applications and settings
  • Describe how sea surface temperatures are mapped
  • Explain how sea surface temperature maps are used to predict El Niño events
  • Outline a plausible workflow used by MDA Federal (formerly EarthSat) to create the high-resolution GEOCOVER global imagery and GEOCOVER-LC global land cover datasets.
  • Outline a plausible workflow for habitat mapping, such as the benthic habitat mapping in the main Hawaiian Islands as part of the NOAA Biogeography program
DA-07 - Applications in federal government
  • List and describe the types of data maintained by federal governments
  • Explain how geospatial information might be used in a taking of private property through a government’s claim of its right of eminent domain
  • Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications
  • Explain the concept of a “spatial decision support system”
DA-05 - Applications in local government
  • List and describe the types of data maintained by local governments
  • Explain how geospatial information might be used in a taking of private property through a government’s claim of its right of eminent domain
  • Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications
  • Explain the concept of a “spatial decision support system”
DA-06 - Applications in state government
  • List and describe the types of data maintained by state governments
  • Explain how geospatial information might be used in a taking of private property through a government’s claim of its right of eminent domain
  • Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications
  • Explain the concept of a “spatial decision support system”
AM-62 - Approaches to point, line, and area generalization
  • Describe the basic forms of generalization used in applications in addition to cartography (e.g., selection, simplification)
  • Explain why areal generalization is more difficult than line simplification
  • Explain the logic of the Douglas-Poiker line simplification algorithm
  • Explain the pitfalls of using data generalized for small scale display in a large scale application
  • Design an experiment that allows one to evaluate the effect of traditional approaches of cartographic generalization on the quality of digital data sets created from analog originals
  • Evaluate various line simplification algorithms by their usefulness in different applications
  • Discuss the possible effects on topological integrity of generalizing data sets
DM-44 - Approximating the Earth's shape with geoids
  • Explain why gravity varies over the Earth’s surface
  • Explain how geoids are modeled
  • Explain the role that the U.S. National Geodetic Survey plays in maintaining and developing geoid models
  • Explain the concept of an equipotential gravity surface (i.e., a geoid)

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