2019 QUARTER 04

A B C D E F G H I K L M N O P R S T U V W
CV-31 - Flow Maps

Flow mapping is a cartographic method of representing movement of phenomena. Maps of this type often depict the vector movement of entities (imports and exports, people, information) between geographic areas, but the general method also encompasses a range of graphics illustrating networks (e.g., transit and communications grids) and dynamic systems (e.g., wind and water currents). Most flow maps typically use line symbols of varying widths, lengths, shapes, colors, or speeds (in the case of animated flow maps) to show the quality, direction, and magnitude of movements. Aesthetic considerations for flow maps are numerous and their production is often done manually without significant automation. Flow maps frequently use distorted underlying geography to accommodate the placement of flow paths, which are often dramatically smoothed/abstracted into visually pleasing curves or simply straight lines. In the extreme, such maps lack a geographic coordinate space and are more diagrammatic, as in Sankey diagrams, alluvial diagrams, slope graphs, and circle migration plots. Whatever their form, good flow maps should effectively visualize the relative magnitude and direction of movement or potential movement between a one or more origins and destinations.

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-77 - Genetic Algorithms and Evolutionary Computing

Genetic algorithms (GAs) are a family of search methods that have been shown to be effective in finding optimal or near-optimal solutions to a wide range of optimization problems. A GA maintains a population of solutions to the problem being solved and uses crossover, mutation, and selection operations to iteratively modify them. As the population evolves across generations, better solutions are created and inferior ones are selectively discarded. GAs usually run for a fixed number of iterations (generations) or until further improvements do not obtain. This contribution discusses the fundamental principles of genetic algorithms and uses Python code to illustrate how GAs can be developed for both numerical and spatial optimization problems. Computational experiments are used to demonstrate the effectiveness of GAs and to illustrate some nuances in GA design.

AM-78 - Genetic Algorithms and Evolutionary Computing

Genetic algorithms (GAs) are a family of search methods that have been shown to be effective in finding optimal or near-optimal solutions to a wide range of optimization problems. A GA maintains a population of solutions to the problem being solved and uses crossover, mutation, and selection operations to iteratively modify them. As the population evolves across generations, better solutions are created and inferior ones are selectively discarded. GAs usually run for a fixed number of iterations (generations) or until further improvements do not obtain. This contribution discusses the fundamental principles of genetic algorithms and uses Python code to illustrate how GAs can be developed for both numerical and spatial optimization problems. Computational experiments are used to demonstrate the effectiveness of GAs and to illustrate some nuances in GA design.

DM-47 - Geographic coordinate system
  • Distinguish between various latitude definitions (e.g., geocentric, geodetic, astronomic latitudes)
  • Explain the angular measurements represented by latitude and longitude coordinates
  • Calculate the latitude and longitude coordinates of a given location on the map using the coordinate grid ticks in the collar of a topographic map and the appropriate interpolation formula
  • Mathematically express the relationship between Cartesian coordinates and polar coordinates
  • Calculate the uncertainty of a ground position defined by latitude and longitude coordinates specified in decimal degrees to a given number of decimal places
  • Use GIS software and base data encoded as geographic coordinates to geocode a list of address-referenced locations
  • Locate on a globe the positions represented by latitude and longitude coordinates
  • Write an algorithm that converts geographic coordinates from decimal degrees (DD) to degrees, minutes, seconds (DMS) format
FC-22 - Geometric primitives
  • Identify the three fundamental dimensionalities used to represent points, lines, and areas
  • Describe the data models used to encode coordinates as points, lines, or polygons
  • Critique the assumptions that are made in representing the world as points, lines, and polygons
  • Evaluate the correspondence between geographic phenomena and the shapes used to represent them

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