2017 QUARTER 01

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

Describe techniques for managing long transactions in a multi-user environment
Describe techniques for handling version control in spatial databases
Define a set of rules for modeling changes in spatial databases
Explain why logging and rollback techniques are adequate for managing “short transactions”

Identify GIS application domains in which true 3-D models of natural phenomena are necessary
Illustrate the use of Virtual Reality Modeling Language (VRML) to model landscapes in 3-D
Explain how octatrees are the 3-D extension of quadtrees
Explain how voxels and stack-unit maps that show the topography of a series of geologic layers might be considered 3-D extensions of field and vector representations respectively
Explain how 3-D models can be extended to additional dimensions
Explain the use of multi-patching to represent 3-D objects
Explain the difficulties in creating true 3-D objects in a vector or raster format
Differentiate between 21/2-D representations and true 3-D models

Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML
Create E-R and UML diagrams of database designs
Create conceptual, logical, and physical data models using automated software tools

Differentiate among modeling uncertainty for entire datasets, for features, and for individual data values
Describe SQL extensions for querying uncertainty information in databases
Describe extensions to relational DBMS to represent different types of uncertainty in attributes, including both vagueness/fuzziness and error-based uncertainty
Discuss the role of metadata in representing and communicating dataset-level uncertainty
Create a GIS database that models uncertain information
Identify whether it is important to represent uncertainty in a particular GIS application
Describe the architecture of data models (both field- and object-based) to represent feature-level and datum-level uncertainty
Evaluate the advantages and disadvantages of existing uncertainty models based on storage efficiency, query performance, ease of data entry, and ability to implement in existing software

Describe recent models of the benefits of GIS&T applications
Explain how profit considerations have shaped the evolution of GIS&T
Outline the elements of a business case that justifies an organization’s investment in an enterprise geospatial information infrastructure
Discuss the extent to which external costs and benefits enhance the economic case for GIS

Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation
Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis
Differentiate between contributing factors and constraints in a multi-criteria application
Explain the legacy of multi-criteria evaluation in relation to cartographic modeling
Determine which method to use to combine criteria (e.g., linear, multiplication)
Create initial weights using the analytical hierarchy process (AHP)
Calibrate a linear combination model by adjusting weights using a test data set

Explain the concepts of spatial resolution, radiometric resolution, and spectral sensitivity
Draw and explain a diagram that depicts the bands in the electromagnetic spectrum at which Earth’s atmosphere is sufficiently transparent to allow high-altitude remote sensing
Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)
Describe an application that requires integration of remotely sensed data with GIS and/or GPS data
Explain the concept of “data fusion” in relation to remote sensing applications in GIS&T
Draw and explain a diagram that depicts the key bands of the electromagnetic spectrum in relation to the magnitude of electromagnetic energy emitted and/or reflected by the Sun and Earth across the spectrum

Discuss the role of Voronoi polygons as the dual graph of the Delaunay triangulation
Explain how Voronoi polygons can be used to define neighborhoods around a set of points
Outline methods that can be used to establish non-overlapping neighborhoods of similarity in raster datasets
Create proximity polygons (Thiessen/Voronoi polygons) in point datasets
Write algorithms to calculate neighborhood statistics (minimum, maximum, focal flow) using a moving window in raster datasets
Explain how the range of map algebra operations (local, focal, zonal, and global) relate to the concept of neighborhoods

Define different interpretations of “cost” in various routing applications
Describe networks that apply to specific applications or industries
Create a data set with network attributes and topology
Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle

Understand how some machine learning methods might be more adept at modeling or representing such distributions
Define non-linear and non-Gaussian distributions in a geospatial data environment
Exemplify non-linear and non-Gaussian distributions in a geospatial data environment

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