Uncertainty

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
FC-26 - Problems of scale and zoning
  • Describe the concept of ecological fallacy, and comment on its relationship with the MAUP
  • Describe the MAUP and its affects on correlation, regression, and classification
  • Describe the modifiable areal unit problem (MAUP) associated with aggregation of data collected at different scales and its affect on spatial autocorrelation
FC-25 - Error
  • Compare and contrast how systematic errors and random errors affect measurement of distance
  • Describe the causes of at least five different types of errors (e.g., positional, attribute, temporal, logical inconsistency, and incompleteness)
CV-18 - Representing Uncertainty

Using geospatial data involves numerous uncertainties stemming from various sources such as inaccurate or erroneous measurements, inherent ambiguity of the described phenomena, or subjectivity of human interpretation. If the uncertain nature of the data is not represented, ill-informed interpretations and decisions can be the consequence. Accordingly, there has been significant research activity describing and visualizing uncertainty in data rather than ignoring it. Multiple typologies have been proposed to identify and quantify relevant types of uncertainty and a multitude of techniques to visualize uncertainty have been developed. However, the use of such techniques in practice is still rare because standardized methods and guidelines are few and largely untested. This contribution provides an introduction to the conceptualization and representation of uncertainty in geospatial data, focusing on strategies for the selection of suitable representation and visualization techniques.

AM-85 - Propagation of error in geospatial modeling
  • Compare and contrast error propagation techniques (e.g., Taylor, Monte Carlo)
  • Explain how some operations can exacerbate error while others dampen it (e.g., mean filter)
AM-87 - Problems of currency, source, and scale
  • Describe the problem of conflation associated with aggregation of data collected at different times, from different sources, and to different scales and accuracy requirements
  • Explain how geostatistical techniques might be used to address such problems
AM-86 - Theory of error propagation
  • Describe stochastic error models
  • Exemplify stochastic error models used in GIScience
FC-26 - Problems of scale and zoning
  • Describe the concept of ecological fallacy, and comment on its relationship with the MAUP
  • Describe the MAUP and its affects on correlation, regression, and classification
  • Describe the modifiable areal unit problem (MAUP) associated with aggregation of data collected at different scales and its affect on spatial autocorrelation
FC-25 - Error
  • Compare and contrast how systematic errors and random errors affect measurement of distance
  • Describe the causes of at least five different types of errors (e.g., positional, attribute, temporal, logical inconsistency, and incompleteness)
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

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