Spatially explicit simulation models may involve abstract or empirically-based GIS landscapes. For example, Tobler (1970) demonstrated how differential equation models could be simulated in a spatially interconnected array for the case of urban population dynamics. These spatial simulation models can be used to model how the dynamics in one place can influence the dynamics in the surrounding areas. This spatial framework may be used to embed dynamic models in each grid cell as a spatial array of replicated models, each operating in parallel and interacting with neighboring models, such as through migration. The grid-based cellular automata model, in which the state of each cell in a spatial array is updated at discrete time steps based on a particular set of rules, is perhaps the most widely-applied spatial simulation model (for more on cellular automata, see Cellular Automata. Spatial simulation modeling can be used to explore the emergence of spatial patterns over time (O’Sullivan and Perry, 2013). As a scientific endeavor, simulation modeling is particularly useful for the study of complex systems, as the interconnections, feedback mechanisms, nonlinearities, and emergent behavior that characterize these systems are well suited for insights that arise from exploring various “what if”? questions over the course of successive simulation experiments. Some researchers use the virtual world of simulation modeling to improve understanding of complex social and environmental systems or to discover or formalize theories about the real world. While prediction is, perhaps, the benefit most commonly associated with simulation models, researchers also employ simulation for other reasons, such as exploration, theory development, or even optimization of conditions to achieve desired outcomes. The insight gained through the use of simulation modeling takes many forms.
While simulation models are generally used to study dynamic problems, with the goal of examining how system structure produces patterns of behavior for key variables over time, they may also be rendered spatially explicit to facilitate the study of such patterns over space as well as time. Simulation is the execution of a sequence of model operations to produce output based on these model assumptions (Robinson, 2004 Gilbert and Troitzsch, 2005). This virtual experimentation is enabled by implementation of a model using a computer program or software platform to specify model structure and parameters that govern the boundary conditions of the model. In simulation modeling, computers provide a virtual world with which to experiment and thus gain insight into real-world problems and questions. By attempting to mimic the factual, simulation models may be used to explore the counterfactual (Morrison, 2015). Simulation modeling involves the use of computers to conduct virtual experimentation with alternative assumptions arising from “what if” questions about a dynamic problem of interest. 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.
Upon implementation, model analysis is performed through rigorous experimentation to test how model structure produces simulated patterns of behavior over time and space. 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. Simulation modeling approaches include system dynamics, discrete event simulation, agent-based modeling, and multi-method modeling. Numerous design choices are made in model development that involve continuous or discrete representations of time and space. As opposed to conceptual or physical models, simulation models enable numerical experimentation with alternative parametric assumptions for a given model design.
Advances in computational capacity have enabled dynamic simulation modeling to become increasingly widespread in scientific research.
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