Algorithm Description

Geographically Weighted Regression (GWR) is a spatial regression technique used to evaluate a local model of the variable or process by fitting a regression equation to every feature in the dataset. Generally, it is a local form of linear regression used to model spatially varying relationships.

Input Layer

Select the input layer containing all the fields used to build local regression model.

Input Parameters

Select only one dependent variable and one or multiple explanatory variables (a.k.a. independent variables).

Spatial Kernel Type

Select the kernel type, either fixed or adaptive, usually depending on how observations distribute in the region of interest.

Spatial Kernel Function

Select one kernel function to calculate weight matrix for each observation.

Note: A potential issue with the Gaussian and Exponential kernel functions is that all observations retain non-zero weight, regardless of their distance from the calibration location. This means that even faraway observations can remain influential for moderate-to-large bandwidth parameters.

To avoid the issue above, the default kernel function is set to bi-square kernel. Moreover, bi-square kernel has a much more intuitive interpretation, where the bandwidth parameter is the distance or number of nearest neighbors away in space so that the remaining observations will not affect in searching optimal bandwidth parameters.

Bandwidth Searching

Specify which method to use in order to search for an optimal bandwidth.

Bandwidth Searching Criterion

Specify how the extent of the kernel should be determined.

Plugin Authors: Gao & Song

Algorithm Version: 1.2