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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.
Fixed bandwidth: True for distance-based kernel function and False for adaptive (nearest neighbor) kernel function (default)
Kernel: Type of kernel function used to weight observations; available options: ‘gaussian’, ‘bisquare’, ‘exponential’.
Search method: Bw search method: ‘golden’, ‘interval’.
Bandwidth min: Min value used in bandwidth search.

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:

v5.2

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

Plugin provider: SDL, Harvard, Cambridge US

Plugin version: 1.3.0.202412051033

On NodePit since: 2023-12-06

Last update: 2024-12-18

KNIME versions: From v5.2 to v5.2

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