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GWR Model

Performs Geographically Weighted Regression (GWR), a local form of linear regression used to model spatially varying relationships. Can currently estimate Gaussian, Poisson, and logistic models(built on a GLM framework). More details can be found at here.

Note: The input table should not contain missing values. You can use the Missing Value node to replace them.

Options

Geometry column: Select the geometry column to use.
Dependent variable: The column containing the dependent variable to use for the calculation of Geographically Weighted Regression.
Independent variables: The columns containing the independent variables to use for the calculation of Geographically Weighted Regression.
Search method: Bw search method: ‘golden’, ‘interval’
Bandwith min: Min value used in bandwidth search
Kernel: Type of kernel function used to weight observations; available options: ‘gaussian’ ‘bisquare’ ‘exponential’

Input Ports

: Input Table for Geographically Weighted Regression

Output Ports

: Model Coefficients Table for Geographically Weighted Regression
: Model for Geographically Weighted Regression

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Model Summary: Model Summary for Geographically Weighted Regression

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Installation

To use this node in KNIME, install the extension Geospatial Analytics Extension for KNIME from the below update site following our NodePit Product and Node Installation Guide:

v4.7

A zipped version of the software site can be downloaded here.

Plugin provider: SDL, Harvard, Cambridge US

Plugin version: 1.1.1.202306211527

On NodePit since: 2022-12-12

Last update: 2026-01-01

KNIME versions: v5.1, v4.7

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