Differentiate between deterministic and probabilistic modeling approaches in GIS&T.
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.