##### AM-86 - Theory of error propagation

- Describe stochastic error models
- Exemplify stochastic error models used in GIScience

- Describe stochastic error models
- Exemplify stochastic error models used in GIScience

- 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)

- 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

- 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)

- 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

- 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

- Describe stochastic error models
- Exemplify stochastic error models used in GIScience

- 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)

- 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

## AM-87 - Problems of currency, source, and scale