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

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Input Table for Geographically Weighted Regression

Output Ports

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Model Coefficients Table for Geographically Weighted Regression

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Model for Geographically Weighted Regression

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Views

Model Summary
Model Summary for Geographically Weighted Regression

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Links

Developers

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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: 1.2.0.202311151543
On NodePit since: 2023-12-06
Last update: 2024-04-29
KNIME versions: Since v5.2

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