All Topics

AM-15 - Calculating surface derivatives
  • List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe
  • Outline how higher order derivatives of height can be interpreted
  • Explain how slope and aspect can be represented as the vector field given by the first derivative of height
  • Explain why the properties of spatial continuity are characteristic of spatial surfaces
  • Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each
  • Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model
  • Outline a number of different methods for calculating slope from a DEM
KE-10 - Capital: facilities and equipment
  • Identify the hardware and space that will be needed for a GIS implementation
  • Compare and contrast the relative merits of housing GISs within IT (information technology) and MIS (management information system) facilities versus keeping them separate
  • Collaborate effectively with various units in an institution to develop efficient hardware and space solutions
  • Hypothesize the ways in which capital needs for GIS may change in the future
AM-12 - Cartographic modeling
  • Describe the difference between prescriptive and descriptive cartographic models
  • Develop a flowchart of a cartographic model for a site suitability problem
  • Discuss the origins of cartographic modeling with reference to the work of Ian McHarg
CV-01 - Cartography and Science
  • Discuss the perspectives of Brian Harley and others on the political motivation for the development of certain kinds of maps
  • Discuss the Swiss influence on map design and production, highlighting Imhof’s contributions
  • Outline the development of some of the major map projections (e.g., Mercator, Gnomonic, Robinson)
  • Explain how Bertin has influenced trends in cartographic symbolization
  • Explain how technological changes have affected cartographic design and production
  • Explain the impact of advances in visualization methods on the evolution of cartography
  • Compare and contrast cartographic developments in various countries and world regions such as Switzerland, France, China, the Middle East, and Greece
  • Discuss the influence of some cartographers of the 16th and 17th centuries (Mercator, Ortelius, Jansson, Homann and others)
  • Describe how compilation, production, and distribution methods used in map-making have evolved
  • Describe how symbolization methods used in map-making have evolved
  • Describe the contributions by Robinson, Jenks, Raisz, and others to U.S. academic cartography
  • Discuss the relationship between the history of exploration and the development of a more accurate map of the world
CV-02 - Cartography and Technology
  • Discuss the impact that mapping on the Web via applications such as Google Earth have had on the practice of cartography
  • Explain how emerging technologies in related fields (e.g., the stereoplotter, aerial and satellite imagery, GPS and LiDAR, the World Wide Web, immersive and virtual environments) have advanced cartography and visualization methods
  • Explain how MacEachren’s Cartography-cubed (C3) concept can be used to understand the evolving role of cartography and visualization
  • Explain how software innovations such as Synagraphic Mapping System (SYMAP), Surfer, and automated contouring methods have affected the design of maps
  • Evaluate the advantages and limitations of various technological approaches to mapping
  • Select new technologies in related fields that have the most potential for use in cartography and visualization
DM-25 - Categories
  • Explain the human tendency to simplify the world using categories
  • Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)
  • Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures
  • Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)
  • Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories
  • Describe the contributions of category theory to understanding the internal structure of categories
  • Document the personal, social, and/or institutional meaning of categories used in GIS applications
  • Create or use GIS data structures to represent categories, including attribute columns, layers/themes, shapes, and legends
  • Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes
  • Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time
AM-69 - Cellular automata (CA) modeling
  • Analyze the advantages and limitations of CA geospatial representations
  • Explain how the use of CA to represent a geographical region relates to how places in a region are interconnected
  • Describe how CA might represent a geographical region
  • Describe how local and global transitional rules are handled in CA
  • Describe how the rules of the Game of Life typically result in a continuously evolving pattern
  • Explain two geographical processes that could be effectively represented using CA
  • Explain two geographical processes that could not be effectively represented using CA
  • Describe classic CA transition rules
  • Describe the challenges of calibrating CA models
  • Explain how temporal concepts are implemented in CA models
  • Describe error sources of CA models
GS-24 - Citizen Science with GIS&T

Figure 1. Participant in a BioBlitz records bird observation (Source: Jo Somerfield)


Citizen Science is defined as the participation of non-professional volunteers in scientific projects (Dickson et al, 2010) and has experienced rapid growth over the past decade. The projects that are emerging in this area range from contributory projects, co-created projects, collegiate projects, which are initiated and run by a group of people with shared interest, without any involvement of professional scientists.  

In many citizen science projects, GIS&T is enabling the collection, analysis, and visualisation of spatial data to affect decision-making. Some examples may include:

  • Recording the location of invasive species or participating in a BioBlitz to record local biodiversity (Figure 1).
  • Measuring air quality or noise over a large area and over time to monitor local conditions and address them
  • Using tools to educate on and increase access to local resources,  improving community resilience

Such projects have the opportunity to empower or disempower members of the public, depending upon access to and understanding of technology. Citizen Science projects using GIS&T may help communities influence decision makers and support the gathering of large-scale scientific evidence on a range of issues. This may also renew people’s interests in the sciences and foster continued and lifelong learning. 


DM-14 - Classic vector data models
  • Illustrate the GBF/DIME data model
  • Describe a Freeman-Huffman chain code
  • Describe the relationship of Freeman-Huffman chain codes to the raster model
  • Discuss the impact of early prototype data models (e.g., POLYVRT and GBF/DIME) on contemporary vector formats
  • Describe the relationship between the GBF/DIME and TIGER structures, the rationale for their design, and their intended primary uses, paying particular attention to the role of graph theory in establishing the difference between GBF/DIME and TIGER files
  • Discuss the advantages and disadvantages of POLYVRT
  • Explain what makes POLYVRT a hierarchical vector data model
AM-09 - Cluster analysis
  • Identify several cluster detection techniques and discuss their limitations
  • Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests
  • Perform a cluster detection analysis to detect “hot spots” in a point pattern
  • Discuss the characteristics of the various cluster detection techniques