Geographical models are simplified representations of real-world systems that are used to study and understand the interactions between different factors. They are not intended to predict future events or test hypotheses, but rather to provide a simplified framework for understanding complex systems.

Variables, parameters, and assumptions are all common components of geographical models. Algorithms are not typically used in geographical models, as they are more commonly associated with computer programming.

Variables in a geographical model are allowed to vary, while parameters are fixed. This means that variables can take on different values, while parameters remain constant.

Assumptions are used in geographical models to simplify the model and make it more manageable. By making assumptions, modelers can reduce the number of variables and parameters that need to be considered, which makes the model easier to understand and analyze.

Deterministic models are based on fixed relationships between variables, while stochastic models are based on random relationships between variables. This means that the output of a deterministic model is always the same for a given set of inputs, while the output of a stochastic model can vary for a given set of inputs.

Validation is the process of ensuring that a geographical model is accurate. This is done by comparing the model's output to real-world data. If the model's output is consistent with the real-world data, then the model is considered to be accurate.

Calibration is the process of adjusting the model's parameters to improve its accuracy. Validation is the process of assessing the model's accuracy by comparing its output to real-world data.

Sensitivity analysis is used in geographical modeling to identify the most sensitive parameters in the model. This is done by varying the values of the parameters and observing the effect on the model's output. The parameters that have the greatest effect on the model's output are considered to be the most sensitive.

Uncertainty analysis is used in geographical modeling to assess the model's uncertainty. This is done by considering the uncertainty in the model's inputs and parameters and propagating this uncertainty through the model to the model's output. The result is a measure of the uncertainty in the model's output.

Spatial models consider the location of features, while non-spatial models do not. This means that spatial models can be used to analyze the relationships between features that are located in different places, while non-spatial models cannot.

Raster models represent space as a grid of cells, while vector models represent space as a collection of points, lines, and polygons. This means that raster models are better suited for representing continuous data, while vector models are better suited for representing discrete data.

Deterministic models are based on fixed relationships between variables, while stochastic models are based on random relationships between variables. This means that the output of a deterministic model is always the same for a given set of inputs, while the output of a stochastic model can vary for a given set of inputs.

Dynamic models represent change over time, while static models do not. This means that dynamic models can be used to simulate the behavior of a system over time, while static models can only represent the state of a system at a single point in time.

Distributed models represent the spatial distribution of variables, while lumped models do not. This means that distributed models can be used to analyze the spatial variability of a system, while lumped models can only represent the average behavior of a system.