2018 QUARTER 02

AM-53 - Identifying and designing analytical procedures
  • Identify the sequence of operations and statistical/mathematical methods (a procedure) appropriate for a particular application (e.g., multi-criteria evaluation for site suitability)
  • Implement a pre-defined procedure for a sample dataset
  • Develop a planned analytical procedure to solve a new unstructured problem (e.g., long-term business strategy)
  • Critique the necessity of the operations used in a pre-defined procedure for a particular application (e.g., suitability analysis)
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)
KE-12 - Implementation planning
  • Discuss the importance of planning for implementation as opposed to “winging it”
  • Discuss pros and cons of different implementation strategies (e.g., spiral development versus waterfall development) given the needs of a particular system
  • Create a budget for the resources needed to implement the system
  • Create a schedule for the implementation of a geospatial system based on a complete design
PD-05 - Implementation tasks
  • Explain the rationale for piloting and prototyping new systems
  • Plan a formal quality assurance procedure
  • Construct an effective database structure in a selected GIS or database software based on the physical model
  • Acquire data from primary and secondary sources
  • Transfer data from primary and secondary sources into the database
  • Create customized programs and scripts based on an application design
GS-22 - Implications of distributed GIS&T
  • Describe the advantages and disadvantages to an organization in using GIS portal information from other organizations
  • Describe how inter-organization GIS portals may impact or influence issues related to social equity, privacy and data access
  • Discuss how distributed GIS&T may affect the nature of organizations and relationships among institutions
  • Suggest the possible societal and ethical implications of distributed GIS&T
KE-26 - Incorporating GIS&T into existing job classifications
  • Select two effective methods of overcoming resistance to change
  • Illustrate how methods for overcoming resistance to change can aid implementation of a GIS
  • Explain how resistance to change and the need to standardize operations when trying to incorporate GIS&T can promote inclusion into existing job classifications
PD-02 - Integer programming
  • Explain why integer programs are harder to solve than linear programs
  • Differentiate between a linear program and an integer program
DM-24 - Integrated models
  • Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)
  • Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model
  • Discuss the degree to which these models can be implemented using current technologies
  • Design data models for specific applications based on these comprehensive general models
  • Illustrate major integrated models of geographic information, such as Peuquet’s triad, Mennis’ pyramid, and Yuan’s three-domain
AM-74 - Interchange heuristics
  • Define alternatives to the Tietz and Bart heuristic
  • Outline the Tietz and Bart interchange heuristic
  • Describe the process whereby an element within a random solution is exchanged, and if it improves the solution, it is accepted, and if not, it is rejected and another element is tried until no improvement occurs in the objective function value
AM-75 - Interchange with probability
  • Explain how the process to break out local optima can be based on a probability function
  • Outline the TABU heuristic