- Conduct an experiment using simulation techniques from an activity perspective
- Explain how a simulation from an activity perspective can be used in transportation
- Discuss important computational laboratory tools for creating new models and visualizing model simulations and model outcomes
- Discuss whether, when prior information is absent, repeatedly generating random synthetic datasets can be used to provide statistical significance
- Discuss Monte Carlo simulation use in GIS&T
- Discuss effective scientific use of supervisory genetic algorithms with agent-based simulation models
- Describe how supervisory search and optimization methods can be used to analyze key characteristics of initial conditions and results and to optimize results based on systematic targeted search through the parameter and random seed spaces
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 linked directly to either their original (2006) or revised entries; forthcoming, future topics are italicized.