## 2018 QUARTER 04

##### DC-09 - Field data technologies
• Identify the measurement framework that applies to moving object tracking
• Explain the advantage of real-time kinematic GPS in field data collection
• Describe an application of hand-held computing or personal digital assistants (PDAs) for field data collection
• Considering the measurement framework applied to moving object tracking, identify which of the dimensions of location, attribute, and time is fixed, which is controlled, and which is measured
• Describe a real or hypothetical application of a sensor network in field data collection
• Outline a combination of positioning techniques that can be used to support location-based services in a given environment
##### DM-23 - Fields in space and time
• Define a field in terms of properties, space, and time
• Formalize the notion of field using mathematical functions and calculus
• Recognize the influences of scale on the perception and meaning of fields
• Evaluate the field view’s description of “objects” as conceptual discretizations of continuous patterns
• Identify applications and phenomena that are not adequately modeled by the field view
• Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields
• Relate the notion of field in GIS to the mathematical notions of scalar and vector fields
• Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)
##### AM-41 - Flow modeling
• Describe practical situations in which flow is conserved while splitting or joining at nodes of the network
• Apply a maximum flow algorithm to calculate the largest flow from a source to a sink, using the edges of the network, subject to capacity constraints on the arcs and the conservation of flow
• Explain how the concept of capacity represents an upper limit on the amount of flow through the network
• Demonstrate how capacity is assigned to edges in a network using the appropriate data structure
##### FC-05 - From concepts to data
• Define the following terms: data, information, knowledge, and wisdom
• Describe the limitations of various information stores for representing geographic information, including the mind, computers, graphics, and text
• Transform a conceptual model of information for a particular task into a data model
##### KE-11 - Funding
• Identify potential sources of funding (internal and external) for a project or enterprise GIS
• Create proposals and presentations to secure funding
• Analyze previous attempts at funding to identify successful and unsuccessful techniques
##### AM-88 - Fuzzy aggregation operators
• Compare and contrast Boolean and fuzzy logical operations
• Compare and contrast several operators for fuzzy aggregation, including those for intersect and union
• Exemplify one use of fuzzy aggregation operators
• Describe how an approach to map overlay analysis might be different if region boundaries were fuzzy rather than crisp
• Describe fuzzy aggregation operators
##### DM-41 - Fuzzy logic
• Describe how linear functions are used to fuzzify input data (i.e., mapping domain values to linguistic variables)
• Support or refute the statement by Lotfi Zadeh, that “As complexity rises, precise statements lose meaning and meaningful statements lose precision,” as it relates to GIS&T
• Explain why fuzzy logic, rather then Boolean algebra models, can be useful for representing real world boundaries between different tree species
##### DM-27 - Genealogical relationships: lineage, inheritance
• Describe ways in which a geographic entity can be created from one or more others
• Discuss the effects of temporal scale on the modeling of genealogical structures
• Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena
• Determine whether it is important to represent the genealogy of entities for a particular application
##### AM-78 - Genetic algorithms and artificial genomes
• Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
• Describe a cluster in a way that could be represented in a genome
• Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
• Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
• Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
##### AM-77 - Genetic algorithms and global solutions
• Describe the difficulty of finding globally optimal solutions for problems with many local optima
• Explain how evolutionary algorithms may be used to search for solutions
• Explain the important advantage that GA methods may offer to find diverse near-optimal solutions
• Explain how a GA searches for solutions by using selection proportional to fitness, crossover, and (very low levels of) mutation to fitness criteria and crossover mutation to search for a globally optimal solution to a problem
• Compare and contrast the effectiveness of multiple search criteria for finding the optimal solution with a simple greedy hill climbing approach