__CLASSIFICATION OF GIS MODELS__

GIS models have been classified by purpose, methodology and logic although the boundary of their classification criteria has not been clear.

- A GIS model may be
**descriptive**or**prescriptive**: A descriptive model describes existing conditions of spatial data whereas a prescriptive models predicts what the conditions could be or should be. As an example, a__vegetation map__represents a__descriptive model__as it shows existing vegetation, while a__potential natural vegetation map__represents a__prescriptive model__as it predicts the site that could be used for vegetation without disturbance. - A GIS model may be
**deterministic**or**stochastic**: Both deterministic and stochastic models are mathematical equations represented with parameters and variables. While a stochastic model considers presence of some randomness in one or more of its parameters, a deterministic model does not. Hence the predictions of a stochastic model can have a certain amount of error. - A GIS model can be
**static**or**dynamic**: A dynamic model emphasizes the changes of spatial data and interaction between variables whereas a static model deals with the state of spatial data at a given time. Time is important to show the process of changes in a dynamic model. Simulation is a technique that can generate different states of spatial data over time. Many environmental models such as water distribution have been effectively understood using dynamic models - A GIS model may be
**deductive**or**inductive:**A deductive model represents the conclusion derived from a set of premises. These premises are often based on scientific theories or physical laws. An inductive model represents the conclusion derived from empirical data or observations. For example, to assess the damage potential of a flood, a deductive model based on the laws of hydrology, geology, etc may be used or an inductive model based on recorded data from past floods can relied upon. - A
**Binary**Model is a GIS model that uses**logical expressions**to select features from a**composite feature layer**or**multiple rasters** **Index model**is a GIS model that uses the**index value**calculated from a**composite feature layer**or**multiple rasters**to produce a**layer with ranked data**- A
**process model**is a GIS model that**integrates existing knowledge**into a set of relationships and equations for**quantifying the physical processes** - A
**Regression model**is a GIS model that uses a dependent variable and a number of independent variables in a**regression equation**for**prediction or estimation**