All Topics

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
DM-01 - Basic data structures
  • Define basic data structure terminology (e.g., records, field, parent/child, nodes, pointers)
  • Analyze the relative storage efficiency of each of the basic data structures
  • Implement algorithms that store geospatial data to a range of data structures
  • Discuss the advantages and disadvantages of different data structures (e.g., arrays, linked lists, binary trees) for storing geospatial data
  • Differentiate among data models, data structures, and file structures
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.

CV-12 - Bivariate and Multivariate Maps
  • Differentiate the interpretation of a series of three maps and a single multivariate map, each representing the same three related variables
  • Design a single map symbol that can be used to symbolize a set of related variables
  • Create a map that displays related variables using different mapping methods (e.g., choropleth
  • and proportional symbol, choropleth and cartogram) Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)
  • Design a map series to show the change in a geographic pattern over time
  • Detect a multivariate outlier using a combination of maps and graphs
  • Explain the relationship among several variables in a parallel coordinate plot
KE-20 - Budgeting for GIS management
  • Describe various approaches to the long-term funding of a GIS in an organization
  • Describe methods to evaluate the return on investment (ROI) of a GIS within an organization
  • Develop a budget for ongoing re-design and system improvement
  • Discuss the advantages and disadvantages of maintenance contracts for software, hardware, and data across an enterprise
  • Evaluate the adequacy of current investments in capital (e.g., facilities, hardware, software) and labor for a GIS
  • Justify changes to the investment in an enterprise GIS, including both cutbacks and increased expenses
AM-03 - Buffers

This short article introduces the definition of buffer and explains how buffers are created for single or multiple geographic features of different geometric types. It also discusses how buffers are generated differently in vector and raster data models and based on the concept of cost.