All Topics

A B C D E F G H I K L M N O P R S T U V W
DA-05 - Applications in local government
  • List and describe the types of data maintained by local governments
  • Explain how geospatial information might be used in a taking of private property through a government’s claim of its right of eminent domain
  • Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications
  • Explain the concept of a “spatial decision support system”
DA-06 - Applications in state government
  • List and describe the types of data maintained by state governments
  • Explain how geospatial information might be used in a taking of private property through a government’s claim of its right of eminent domain
  • Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications
  • Explain the concept of a “spatial decision support system”
AM-62 - Approaches to point, line, and area generalization
  • Describe the basic forms of generalization used in applications in addition to cartography (e.g., selection, simplification)
  • Explain why areal generalization is more difficult than line simplification
  • Explain the logic of the Douglas-Poiker line simplification algorithm
  • Explain the pitfalls of using data generalized for small scale display in a large scale application
  • Design an experiment that allows one to evaluate the effect of traditional approaches of cartographic generalization on the quality of digital data sets created from analog originals
  • Evaluate various line simplification algorithms by their usefulness in different applications
  • Discuss the possible effects on topological integrity of generalizing data sets
DM-44 - Approximating the Earth's shape with geoids
  • Explain why gravity varies over the Earth’s surface
  • Explain how geoids are modeled
  • Explain the role that the U.S. National Geodetic Survey plays in maintaining and developing geoid models
  • Explain the concept of an equipotential gravity surface (i.e., a geoid)
FC-16 - Area and Region
  • List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes
  • Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation
  • Outline an algorithm to find the area of a polygon using the coordinates of its vertices
  • Explain how variations in the calculation of area may have real world implications, such as calculating density
  • Delineate regions using properties, spatial relationships, and geospatial technologies
  • Exemplify regions found at different scales
  • Explain the relationship between regions and categories
  • Identify the kinds of phenomena commonly found at the boundaries of regions
  • Explain why general-purpose regions rarely exist
  • Differentiate among different types of regions, including functional, cultural, physical, administrative, and others
  • Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)
  • Use established analysis methods that are based on the concept of region (e.g., landscape ecology)
  • Explain the nature of the Modifiable Areal Unit Problem (MAUP)
CP-04 - Artificial intelligence
  • Describe computational intelligence methods that may apply to GIS&T
  • Exemplify the potential for machine learning to expand performance of specialized geospatial analysis functions
  • Identify artificial intelligence tools that may be useful for GIS&T
  • Describe a hypothesis space that includes searches for optimality of solutions within that space
GS-10 - Balancing data access, security, and privacy
  • Assess the effect of restricting data in the context of the availability of alternate sources of data
  • Exemplify areas where post-9/11 changes in policies have restricted or expanded data access
GS-21 - Balancing security and open access to geospatial information
  • Discuss the way that a legal regime balances the need for security of geospatial data with the desire for open access
AM-25 - Bayesian methods
  • Define “prior and posterior distributions” and “Markov-Chain Monte Carlo”
  • Explain how the Bayesian perspective is a unified framework from which to view uncertainty
  • Compare and contrast Bayesian methods and classical “frequentist” statistical methods
CV-19 - Big Data Visualization

As new information and communication technologies have altered so many aspects of our daily lives over the past decades, they have simultaneously stimulated a shift in the types of data that we collect, produce, and analyze. Together, this changing data landscape is often referred to as "big data." Big data is distinguished from "small data" not only by its high volume but also by the velocity, variety, exhaustivity, resolution, relationality, and flexibility of the datasets. This entry discusses the visualization of big spatial datasets. As many such datasets contain geographic attributes or are situated and produced within geographic space, cartography takes on a pivotal role in big data visualization. Visualization of big data is frequently and effectively used to communicate and present information, but it is in making sense of big data – generating new insights and knowledge – that visualization is becoming an indispensable tool, making cartography vital to understanding geographic big data. Although visualization of big data presents several challenges, human experts can use visualization in general, and cartography in particular, aided by interfaces and software designed for this purpose, to effectively explore and analyze big data.

Pages