2016 QUARTER 02

A B C D E F G H I K L M N O P R S T U V W
GD6-4 - Precision
  • Calculate, in terms of ground area, the uncertainty associated with decimal coordinates specified to three, four, and five decimal places
  • Explain, in general terms, the difference between single and double precision and impacts on error propagation
  • Explain the concept of error propagation
GD6-5 - Primary and secondary sources
  • Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data
  • Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use
AM8-4 - Principles of kriging
  • Describe the relationship between the semi-variogram and kriging
  • Explain why it is important to have a good model of the semi-variogram in kriging
  • Explain the concept of the kriging variance, and describe some of its shortcomings
  • Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support)
  • Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping
  • Outline the basic kriging equations in their matrix formulation
  • Conduct a spatial interpolation process using kriging from data description to final error map
  • Explain why kriging is more suitable as an interpolation method in some applications than others
AM8-2 - Principles of semi-variogram construction
  • Identify and define the parameters of a semi-variogram (range, sill, nugget)
  • Demonstrate how semi-variograms react to spatial nonstationarity
  • Construct a semi-variogram and illustrate with a semi-variogram cloud
  • Describe the relationships between semi-variograms and correlograms, and Moran’s indices of spatial association
AM9-1 - Principles of spatial econometrics
  • Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics
  • Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models
  • Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models
  • Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis
  • Describe the general types of spatial econometric models
GS1-4 - Privacy
  • Discuss the status of the concept of privacy in the U.S. legal regime
  • Explain how conversion of land records data from analog to digital form increases risk to personal privacy
  • Compare and contrast geographic information technologies that are privacy-invasive, privacy-enhancing, and privacy-sympathetic
  • Explain the argument that human tracking systems enable “geoslavery”
  • Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau
OI1-2 - Private sector origins
  • Identify some of the key commercial activities that provided an impetus for the development of GIS&T
  • Differentiate the dominant industries using geospatial technologies during the 1980s, 1990s, and 2000s
  • Describe the contributions of McHarg and other practitioners in developing geographic analysis methods later incorporated into GIS
  • Evaluate the correspondence between advances in hardware and operating system technology and changes in GIS software
  • Describe the influence of evolving computer hardware and of private sector hardware firms such as IBM on the emerging GIS software industry
  • Discuss the emergence of the GIS software industry in terms of technology evolution and markets served by firms such as ESRI, Intergraph, and ERDAS
DA2-1 - Problem definition
  • Recognize the challenges of implementing and using geospatial technologies
  • Create a charter or hypothesis that defines and justifies the mission of a GIS to solve existing problems
  • Define an enterprise GIS in terms of institutional missions and goals
  • Identify geographic tasks for which particular geospatial technologies are not adequate or sufficient
  • Identify what is typically needed to garner support among managers for designing and/or creating a GIS
GC8-6 - Problems of currency, source, and scale
  • Describe the problem of conflation associated with aggregation of data collected at different times, from different sources, and to different scales and accuracy requirements
  • Explain how geostatistical techniques might be used to address such problems
AM10-1 - Problems of large spatial databases
  • Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence (e.g., expert systems, artificial neural networks, genetic algorithms, and software agents)
  • Explain how to recognize contaminated data in large datasets
  • Outline the implications of complexity for the application of statistical ideas in geography
  • Explain what is meant by the term “contaminated data,” suggesting how it can arise
  • Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity

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