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.
GIS-based computational models are explored. While models vary immensely across disciplines and specialties, the focus is on models that simulate and forecast geographical systems and processes in time and space. The degree and means of integration of the many different models with GIS are covered, and the critical phases of modeling: design, implementation, calibration, sensitivity analysis, validation and error analysis are introduced. The use of models in simulations, an important purpose for implementing models within or outside of GIS, is discussed and the context of scenario-based planning explained. To conclude, a survey of model types is presented, with their application methods and some examples, and the goals of modeling are discussed.
Monte Carlo Simulation (MCS) is a computational technique that applies random sampling to model complex systems under uncertainty. In Geographic Information Science and Technology (GIS&T), MCS enables probabilistic analysis of spatial phenomena affected by incomplete data, stochastic processes, or measurement error. Unlike traditional deterministic models that obscure uncertainty, MCS explicitly propagates input variability through simulation, offering robust statistical insights into spatial outcomes. Grounded in statistical principles, MCS supports a wide range of geospatial applications, including flood risk mapping, land use change modeling, satellite image classification, and more. Practical implementation typically involves defining models, characterizing uncertainty through probability distributions, executing simulations, and analyzing the resulting distributions of outcomes. Though there are some limitations, the flexibility and compatibility of modern computing environments have made MCS increasingly accessible, making it a foundational tool for addressing spatial uncertainty and guiding evidence-based policy and analysis.