Monday, October 12, 2015

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.
  1. 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.
  2. 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.
  3. 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
  4. 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.
  5. A Binary Model is a GIS model that uses logical expressions to select features from a  composite feature layer or multiple rasters
  6. 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
  7. A process model is a GIS model that integrates existing knowledge into a set of relationships and equations for quantifying the physical processes
  8. 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