# GIS Modeling: Model Elements and Types of GIS Models (Especially for GATE-Geospatial 2022)

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GIS modeling refers to the use of GIS in building analytical models with spatial data. Models using geographically referenced data are usually called “spatially explicit models.”

GIS models can be vector-based or raster-based. A raster-based model is preferred if the modeling involves intense and complex computation; for this reason, process models and to a lesser degree, regression models are usually raster-based.

## GIS Model Elements

There are two basic of GIS model elements:

- A set of selected spatial variables.
- Functional/mathematical relationship between variables.

GIS Model can be related to exploratory data analysis, data visualization and DB management.

GIS Model can work together with or across GIS software package and other computer programs.

## Types of GIS Models

GIS models have been classified by purpose, methodology and logic:

- Descriptive or Prescriptive
- Deterministic vs. Stochastic
- Static vs. Dynamic
- Deductive vs. Inductive
- Binary Model, Index Model, Process Model, vs. Regression Model

## Descriptive vs. Prescriptive

A GIS model may be descriptive or prescriptive: A descriptive model describes existing conditions of spatial data whereas a prescriptive model 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.

## Deterministic vs. Stochastic

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.

## Static vs. Dynamic

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

## Deductive vs. Inductive

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.

## Binary Model, Index Model, Process Model, vs. Regression Model

- A
**binary model**uses logical expressions to select features from a composite feature layer or multiple rasters. **Index model**uses the index value calculated from a composite feature layer or multiple rasters to produce a layer with ranked data.- A
**process model**integrates existing knowledge into a set of relationships and equations for quantifying the physical processes. - A
**regression model**uses a dependent variable and several independent variables in a regression equation for prediction or estimation.