All Topics

A B C D E F G H I K L M N O P R S T U V W
AM-36 - Data mining approaches
  • Describe how data mining can be used for geospatial intelligence
  • Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component
  • Demonstrate how cluster analysis can be used as a data mining tool
  • Interpret patterns in space and time using Dorling and Openshaw’s geographical analysis machine (GAM) demonstration of disease incidence diffusion
  • Differentiate between data mining approaches used for spatial and non-spatial applications
  • Explain how spatial statistics techniques are used in spatial data mining
  • Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction
DM-02 - Data retrieval strategies
  • Analyze the relative performance of data retrieval strategies
  • Implement algorithms that retrieve geospatial data from a range of data structures
  • Describe the particular advantages of Morton addressing relative to geographic data representation
  • Discuss the advantages and disadvantages of different data structures (e.g., arrays, linked lists, binary trees, hash tables, indexes) for retrieving geospatial data
  • Compare and contrast direct and indirect access search and retrieval methods
KE-18 - Data sharing among public and private agencies, organizations, and individuals
  • Describe formal and informal arrangements that promote geospatial data sharing (e.g., FGDC, ESDI, memoranda of agreements, informal access arrangements, targeted funding support)
  • Describe a situation in which politics interferes with data sharing and exchange
DM-59 - Data warehouses
  • Differentiate between a data warehouse and a database
  • Describe the functions that gazetteers support
  • Differentiate the retrieval mechanisms of data warehouses and databases
  • Discuss the appropriate use of a data warehouse versus a database
DM-62 - Database administration
  • Describe how using standards can affect implementation of a GIS
  • Explain how validation and verification processes can be used to maintain database integrity
  • Summarize how data access processes can be a factor in development of an enterprise GIS implementation
  • Describe effective methods to get stakeholders to create, adopt, or develop and maintain metadata for shared datasets
DM-37 - Database change
  • Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software
  • Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database
  • Explain how one would establish the criteria for monitoring the periodic changes in a real GIS database
  • Discuss the implication of “long transactions” on database integrity
  • Modify spatial and attribute data while ensuring consistency within the database
FC-24 - Definitions within a conceptual model of uncertainty
  • Describe a stochastic error model for a natural phenomenon
  • Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects, and fields or discord and non-specificity
  • Explain how the familiar concepts of geographic objects and fields affect the conceptualization of uncertainty
PD-03 - Development environments for geospatial applications
  • Develop a geospatial application using the most appropriate environment
  • Compare and contrast the relative merits of available environments for geospatial applications, including desktop software scripting (e.g., VBA), graphical modeling tools, geospatial components in standard environments, and “from-scratch” development in standard environments
DM-20 - Discrete entities
  • Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities
  • Compare and contrast differing epistemological and metaphysical viewpoints on the “reality” of geographic entities
  • Identify the types of features that need to be modeled in a particular GIS application or procedure
  • Identify phenomena that are difficult or impossible to conceptualize in terms of entities
  • Describe the difficulties in modeling entities with ill-defined edges
  • Describe the difficulties inherent in extending the “tabletop” metaphor of objects to the geographic environment
  • Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities
  • Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities
  • Model “gray area” phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects
  • Evaluate the influence of scale on the conceptualization of entities
  • Describe the perceptual processes (e.g., edge detection) that aid cognitive objectification
  • Describe particular entities in terms of space, time, and properties
FC-14 - Distance, Length, and Direction
  • Describe several different measures of distance between two points (e.g., Euclidean, Manhattan, network distance, spherical)
  • Explain how different measures of distance can be used to calculate the spatial weights matrix
  • Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length
  • Explain how fractal dimension can be used in practical applications of GIS
  • Explain the differences in the calculated distance between the same two places when data used are in different projections
  • Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas
  • Estimate the fractal dimension of a sinuous line
  • Describe operations that can be performed on qualitative representations of direction
  • Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections
  • Compute the mean of directional data
  • Compare and contrast how direction is determined and stated in raster and vector data
  • Define “direction” and its measurement in different angular measures

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