All Topics

FC-31 - Academic origins
  • Identify the key academic disciplines that contributed to the development of GIS&T
  • Evaluate the role that the Quantitative Revolution in geography played in the development of GIS
  • Describe the major research foci in GIS and related fields in the 1970s, 1980s, 1990s, and 2000s
  • Evaluate the importance of the NCGIA and UCGIS in coalescing GIScience as a sub-field of GIS&T
  • Discuss the contributions of early academic centers of GIS&T research and development (e.g., Harvard Laboratory for Computer Graphics, UK Experimental Cartography Unit)
AM-81 - Adaptive agents
  • Describe different approaches to represent the effects of agent adaptation in the context of a specific agent-based model
  • Explain the effects of agent adaptation in the context of a specific agent-based model 
FC-18 - Adjacency and connectivity
  • List different ways connectivity can be determined in a raster and in a polygon dataset
  • Explain the nine-intersection model for spatial relationships
  • Demonstrate how adjacency and connectivity can be recorded in matrices
  • Calculate various measures of adjacency in a polygon dataset
  • Create a matrix describing the pattern of adjacency in a set of planar enforced polygons
  • Describe real world applications where adjacency and connectivity are a critical component of analysis
DM-64 - Adoption of standards
  • Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs
  • Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards
  • Identify organizations that focus on developing standards related to GIS&T
  • Identify standards that are used in GIS&T
  • Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS
DC-12 - Aerial photography image interpretation
  • Use photo interpretation keys to interpret features on aerial photographs
  • Calculate the nominal scale of a vertical aerial image
  • Calculate heights and areas of objects and distances between objects shown in a vertical aerial image
  • Produce a map of land use/land cover classes using a vertical aerial image
  • Describe the elements of image interpretation
KE-16 - Agency, organizational, and individual perspectives
  • Describe perspectives on the nature and scope of system benefits among agency officials, organizational personnel, and citizens
  • Discuss implications of unequal economic power on the kinds of organizations that use, and benefit from, GIS&T
AM-79 - Agent-based models
  • Compare and contrast agent-based models and cellular automata as approaches for modeling spatial processes
  • Describe how agent-based models use object-oriented programming constructs of inheritance and encapsulation to represent the behavior of heterogeneous and interactive and adaptive actors
  • Describe how multiple, different types of agents in a given system behave and interact with each other and their environment
  • Generate multiple, different types of agents in a given system
  • Describe how multiple parameters (e.g., number of agents, variability of agents, random number seeds for different series of random events or choices during each simulation) can be set within an agent-based model to change the model behavior
  • Explain how agent behaviors can be used to represent the effects actors have on each other and on their environment
  • Design simple experiments with an agent-based model
  • Design and implement a simple agent-based model using appropriate commercial or open source development tools
  • Conduct simple experiments with an agent-based model, analyze results, and evaluate their statistical significance with respect to degrees of freedom, sensitivity analyses, and uncertainty in the model
  • Describe how measurements on various inputs and outputs of a model can be used to describe model behavior and to relate model outcomes to various initial conditions
  • Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
  • Determine if an agent-based model has been run enough times with enough different random number seeds for rigorous inference of its results
GS-20 - Aggregation of spatial entities
  • Demonstrate the relationship between district size (resolution/support) and patterns in aggregate data
  • Demonstrate how changing the geometry of regions changes the data values (e.g., voting patterns before and after redistricting)
  • Discuss the potential pitfalls of using regions to aggregate geographic information (e.g., census data)
  • Explain the nature and causes of the Modifiable Areal Unit Problem (MAUP)
  • Attempt to design aggregation regions that overcome MAUP
  • Discuss the conditions that require individual spatial entities to be aggregated (e.g., privacy, security, proprietary interests, data simplification)
  • Summarize the attributes of individuals within regions using spatial joins
DC-18 - Algorithms and processing
  • Differentiate supervised classification from unsupervised classification
  • Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
  • Compare pixel-based image classification methods with segmentation techniques
  • Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
  • Describe an application of hyperspectral image data
  • Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
  • Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
  • Describe a situation in which filtered data are more useful than the original unfiltered data
  • Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
AM-02 - Analytical approaches
  • Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling
  • Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations
  • Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis
  • Differentiate between geostatistics and spatial statistics
  • Discuss situations when it is desirable to adopt a spatial approach to the analysis of data
  • Explain what is added to spatial analysis to make it spatio-temporal analysis
  • Explain what is special (i.e., difficult) about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems
  • Outline the sequence of tasks required to complete the analytical process for a given spatial problem
  • Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis