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AM-84 - Simulation Modeling

Advances in computational capacity have enabled dynamic simulation modeling to become increasingly widespread in scientific research. As opposed to conceptual or physical models, simulation models enable numerical experimentation with alternative parametric assumptions for a given model design. Numerous design choices are made in model development that involve continuous or discrete representations of time and space. Simulation modeling approaches include system dynamics, discrete event simulation, agent-based modeling, and multi-method modeling. The model development process involves a shift from qualitative design to quantitative analysis upon implementation of a model in a computer program or software platform. Upon implementation, model analysis is performed through rigorous experimentation to test how model structure produces simulated patterns of behavior over time and space. Validation of a model through correspondence of simulated results with observed behavior facilitates its use as an analytical tool for evaluating strategies and policies that would alter system behavior.

DA-16 - GIS&T and Forestry

GIS applications in forestry are as diverse as the subject itself. Many foresters match a common stereotype as loggers and firefighters, but many protect wildlife, manage urban forests, enhance water quality, provide for recreation, and plan for a sustainable future.  A broad range of management goals drives a broad range of spatial methods, from adjacency functions to zonal analysis, from basic field measurements to complex multi-scale modeling. As such, it is impossible to describe the breadth of GIS&T in forestry. This review will cover core ways that geospatial knowledge improves forest management and science, and will focus on supporting core competencies.  

AM-69 - Cellular Automata

Cellular automata (CA) are simple models that can simulate complex processes in both space and time. A CA consists of six defining components: a framework, cells, a neighborhood, rules, initial conditions, and an update sequence. CA models are simple, nominally deterministic yet capable of showing phase changes and emergence, map easily onto the data structures used in geographic information systems, and are easy to implement and understand. This has contributed to their popularity for applications such as measuring land use changes and monitoring disease spread, among many others.