2018 QUARTER 03

A B C D E F G H I K L M N O P R S T U V W
FC-32 - Learning from experience
  • Explain how knowledge of the history of the development of enterprise GIS can aid in an implementation process
  • Evaluate case studies of past GISs to identify factors leading to success and failure
  • Discuss the evolution of isolated GIS projects to enterprise GIS
AM-40 - Least-cost (shortest) path analysis
  • Describe some variants of Dijkstra’s algorithm that are even more efficient
  • Discuss the difference of implementing Dijkstra’s algorithm in raster and vector modes
  • Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network
  • Compute the optimum path between two points through a network with Dijkstra’s algorithm
  • Explain how a leading World Wide Web-based routing system works (e.g., MapQuest, Yahoo Maps, Google)
GS-23 - Legal mechanisms for sharing geospatial information
  • Describe contracts, licenses, and other mechanisms for sharing geospatial data
  • Outline the terms of a licensing agreement with a local engineering consulting firm that a manager of a county government GIS office would employ if charged to recoup revenue through sale and licensure of county data
GS-03 - Liability
  • Describe the nature of tort law generally and nuisance law specifically
  • Describe strategies for managing liability risk, including disclaimers and data quality standards
  • Describe cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests
  • Differentiate among contract liability, tort liability, and statutory liability
PD-01 - Linear programming
  • Explain the role of constraint functions using the simplex method
  • Explain the role of objective functions in linear programming
  • Describe the structure of linear programs
  • Explain the role of constraint functions using the graphical method
  • Implement linear programs for spatial allocation problems
DM-16 - Linear referencing
  • Discuss dynamic segmentation as a process for transforming between linear and planar coordinate systems
  • Construct a data structure to contain point or linear geometry for database record events that are referenced by their position along a linear feature
  • Explain how linear referencing allows attributes to be displayed and analyzed that do not correspond precisely with the underlying segmentation of the network features
  • Describe how linear referencing can eliminate unnecessary segmentation of the underlying network features due to attribute value changes over time
  • Demonstrate how linear referenced locations are often much more intuitive and easy to find in the real world than geographic coordinates
DM-50 - Linear referencing systems
  • Describe an application in which a linear referencing system is particularly useful
  • Explain how the datum associated with a linear referencing system differs from a horizontal or vertical datum
  • Identify several different linear referencing methods (e.g., mileposts, reference posts, link and node) and compare them to planar grid systems
  • Identify the characteristics that all linear referencing systems have in common Unit GD4 Datums (core unit) “Horizontal” datums define the geometric relationship between a coordinate system grid and the Earth’s surface, where the Earth’s surface is approximated by an ellipsoid or other figure. “Vertical” datums are elevation reference surfaces, such as mean sea level.
  • Explain how a network can be used as the basis for reference as opposed to the more common rectangular coordinate systems
  • Discuss the magnitude and cause of error generated in the transformation from linear to planar coordinate systems
AM-23 - Local measures of spatial association
  • Describe the effect of non-stationarity on local indices of spatial association
  • Decompose Moran’s I and Geary’s c into local measures of spatial association
  • Compute the Gi and Gi* statistics
  • Explain how geographically weighted regression provides a local measure of spatial association
  • Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association
  • Compare and contrast global and local statistics and their uses
GS-04 - Location Privacy

How effective is this fence at keeping people, objects, or sensitive information inside or outside? Location Privacy is concerned with the claim of individuals to determine when, how, and to what extent information about themselves and their location is communicated to others. Privacy implications for spatial data are growing in importance with growing awareness of the value of geo-information and the advent of the Internet of Things, Cloud-Based GIS, and Location Based Services.  

In the rapidly changing landscape of GIS and public domain spatial data, issues of location privacy are more important now than ever before. Technological trailblazing tends to precede legal safeguards. The development of GIS tools and the work of the GIS&T research and user community have typically occurred at a much faster rate than the establishment of legislative frameworks governing the use of spatial data, including privacy concerns. Yet even in a collaborative environment that characterizes the GIS&T community, and despite progress made, the issue of location privacy is a particularly thorny one, occurring as it does at the intersection of geotechnology and society.

AM-46 - Location-allocation modeling

Location-allocation models involve two principal elements: 1) multiple facility location; and 2) the allocation of the services or products provided by those facilities to places of demand. Such models are used in the design of logistic systems like supply chains, especially warehouse and factory location, as well as in the location of public services. Public service location models involve objectives that often maximize access and levels of service, while private sector applications usually attempt to minimize cost. Such models are often hard to solve and involve the use of integer-linear programming software or sophisticated heuristics. Some models can be solved with functionality provided in GIS packages and other models are applied, loosely coupled, with GIS. We provide a short description of formulating two different models as well as discuss how they are solved.

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