# 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 containing 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.

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.

Bandwith min

Min value used in bandwidth search

Kernel

type of kernel function used to weight observations; available options: ‘gaussian’ ‘bisquare’ ‘exponential’

## 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’.

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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## Views

Model Summary
Model Summary for Multiscale Geographically Weighted Regression

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