##### AM-02 - Analytical approaches

New abstract will go here.

This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.

Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain __only__ Learning Objectives). Entries that have been **updated and expanded are in bold. **Forthcoming, *future topics are italicized*.

New abstract will go here.

- Describe the basic forms of generalization used in applications in addition to cartography (e.g., selection, simplification)
- Explain why areal generalization is more difficult than line simplification
- Explain the logic of the Douglas-Poiker line simplification algorithm
- Explain the pitfalls of using data generalized for small scale display in a large scale application
- Design an experiment that allows one to evaluate the effect of traditional approaches of cartographic generalization on the quality of digital data sets created from analog originals
- Evaluate various line simplification algorithms by their usefulness in different applications
- Discuss the possible effects on topological integrity of generalizing data sets

- Define “prior and posterior distributions” and “Markov-Chain Monte Carlo”
- Explain how the Bayesian perspective is a unified framework from which to view uncertainty
- Compare and contrast Bayesian methods and classical “frequentist” statistical methods

- List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe
- Outline how higher order derivatives of height can be interpreted
- Explain how slope and aspect can be represented as the vector field given by the first derivative of height
- Explain why the properties of spatial continuity are characteristic of spatial surfaces
- Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each
- Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model
- Outline a number of different methods for calculating slope from a DEM

- Describe the difference between prescriptive and descriptive cartographic models
- Develop a flowchart of a cartographic model for a site suitability problem
- Discuss the origins of cartographic modeling with reference to the work of Ian McHarg

## 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.