All Topics

A B C D E F G H I K L M N O P R S T U V W
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 Modeling

Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.

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

Pages