2018 QUARTER 04

A B C D E F G H I K L M N O P R S T U V W
KE-23 - GIS&T workforce development
  • Describe issues that may hinder implementation and continued successful operation of a GIS if effective methods of staff development are not included in the process
  • Outline methods (programs or processes) that provide effective staff development opportunities for GIS&T
AM-22 - Global measures of spatial association
  • Describe the effect of the assumption of stationarity on global measures of spatial association
  • Justify, compute, and test the significance of the join count statistic for a pattern of objects
  • Compute the K function
  • Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends
  • Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
  • Compute Moran’s I and Geary’s c for patterns of attribute data measured on interval/ratio scales
  • Explain how the K function provides a scale-dependent measure of dispersion
DC-03 - Global Positioning System
  • Explain how GPS receivers calculate coordinate data
  • Discuss the relationship of GPS to the Global Satellite Navigation System
  • Explain “selective availability,” why it was discontinued in 2000, and what alternatives are available to the U.S. Department of Defense
  • Explain the relationship of the U.S. Global Positioning System with comparable systems sponsored by Russia and the European Union and the Global Navigation Satellite System
  • Discuss the role of GPS in location-based services (LBS)
  • Specify the features of a GPS receiver that is able to achieve geometric accuracies on the order of centimeters without post-processing
  • Explain the relevance of the concept of trilateration to both GPS positioning and control surveying
  • Perform differential correction of GPS data using reference data from a CORS station
  • List, define, and rank the sources of error associated with GPS positioning
  • Distinguish between horizontal and vertical accuracies when using coarse acquisition codes/standard positioning service (C-codes) and precision acquisition codes/precise positioning service (P-codes)
PD-13 - GPU Programming for GIS Applications

Graphics processing units (GPUs) are massively parallel computing environments with applications in graphics and general purpose programming. This entry describes GPU hardware, application domains, and both graphics and general purpose programming languages.

CP-06 - Graphics Processing Units (GPUs)

Graphics Processing Units (GPUs) represent a state-of-the-art acceleration technology for general-purpose computation. GPUs are based on many-core architecture that can deliver computing performance much higher than desktop computers based on Central Processing Units (CPUs). A typical GPU device may have hundreds or thousands of processing cores that work together for massively parallel computing. Basic hardware architecture and software standards that support the use of GPUs for general-purpose computation are illustrated by focusing on Nvidia GPUs and its software framework: CUDA. Many-core GPUs can be leveraged for the acceleration of spatial problem-solving.  

DC-19 - Ground verification and accuracy assessment
  • Evaluate the thematic accuracy of a given soils map
  • Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset
DM-11 - Hierarchical data models
  • Illustrate the quadtree model
  • Describe the advantages and disadvantages of the quadtree model for geographic database representation and modeling
  • Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, rtrees, pyramids)
  • Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data
  • Implement a format for encoding quadtrees in a data file
CP-03 - High performance computing
  • Describe how the power increase in desktop computing has expanded the analytic methods that can be used for GIS&T
  • Exemplify how the power increase in desktop computing has expanded the analytic methods that can be used for GIS&T
DM-52 - Horizontal datums
  • Discuss appropriate applications of the various datum transformation options
  • Explain the difference between NAD 27 and NAD 83 in terms of ellipsoid parameters
  • Outline the historical development of horizontal datums
  • Explain the difference in coordinate specifications for the same position when referenced to NAD 27 and NAD 83
  • Explain the rationale for updating NAD 27 to NAD 83
  • Explain why all GPS data are originally referenced to the WGS 84 datum
  • Identify which datum transformation options are available and unavailable in a GIS software package
  • Define “horizontal datum” in terms of the relationship between a coordinate system and an approximation of the Earth’s surface
  • Describe the limitations of a Molodenski transformation and in what circumstances a higher parameter transformation such as Helmert may be appropriate
  • Determine the impact of a datum transformation from NAD 27 to NAD 83 for a given location using a conversion routine maintained by the U.S. National Geodetic Survey
  • Explain the methodology employed by the U.S. National Geodetic Survey to transform control points from NAD 27 to NAD 83
  • Perform a Molodenski transformation manually
  • Use GIS software to perform a datum transformation
AM-56 - Impacts of transformations
  • Compare and contrast the impacts of different conversion approaches, including the effect on spatial components
  • Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)
  • Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity (e.g., converting a digital elevation model into a TIN)

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