MGWR Model

Multiscale Geographically Weighted Regression estimation. More details can be found at 1. A. Stewart Fotheringham, Wenbai Yang, and Wei Kang. Multiscale geographically weighted regression (mgwr). Annals of the American Association of Geographers, 107(6):1247–1265, 2017. URL: http://dx.doi.org/10.1080/24694452.2017.1352480, arXiv:http://dx.doi.org/10.1080/24694452.2017.1352480, doi:10.1080/24694452.2017.1352480. and Hanchen Yu, Alexander Stewart Fotheringham, Ziqi Li, Taylor Oshan, Wei Kang, and Levi John Wolf. Inference in multiscale geographically weighted regression. Geographical Analysis, 2019. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/gean.12189, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/gean.12189, doi:10.1111/gean.12189. 2. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/how-multiscale-geographically-weighted-regression-mgwr-works.htm

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 contains the dependent variable to use for the calculation of Multiscale Geographically Weighted Regression.
Independent variables: The columns containing the independent variables to use for the calculation of Multiscale 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’. Golden Search— Determines either the number of neighbors or distance band for each explanatory variable using the Golden Search algorithm. This method searches multiple combinations of values for each explanatory variable between a specified minimum and maximum value. Intervals— Determines the number of neighbors or distance band for each explanatory variable by incrementing the number of neighbors or distance band from a minimum value.
Bandwidth min: Min value used in bandwidth search.
Bandwidth Max: Max value used in bandwidth search.
Interval: Interval used in bandwidth search.
Criterion: Criterion used in bandwidth search: ‘AICc’, ‘AIC’, ‘BIC’, ‘CV’.

Advanced Settings

Advanced Setting

Sigma2 v1: specify form of corrected denominator of sigma squared to use for model diagnostics; Acceptable options are: ‘True’: n-tr(S) (default) ‘False’: n-2(tr(S)+tr(S’S))
Constant: True to include intercept (default) in model and False to exclude intercept.
Spherical: True for spherical coordinates (long-lat), False for projected coordinates (default).
Hat Matrix: True to store full n by n hat matrix, False to not store full hat matrix to minimize memory footprint (default).

Input Ports

: The input table containing the data to use for the calculation of Multiscale Geographically Weighted Regression.

Output Ports

: The output table containing the model coefficients for Multiscale Geographically Weighted Regression.

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Model Summary: Model Summary for Multiscale 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: 1.2.0.202311151543

On NodePit since: 2023-12-06

Last update: 2024-04-29

KNIME versions: Since v5.2

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