2016 QUARTER 03

A B C D E F G H I K L M N O P R S T U V W

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

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

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

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

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

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”

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”

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”

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

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)

Search form