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.
Select the input layer containing all the fields used to build local regression model.
Select only one dependent variable and one or multiple explanatory variables (a.k.a. independent variables).
Select the kernel type, either fixed or adaptive, usually depending on how observations distribute in the region of interest.
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.
Specify which method to use in order to search for an optimal bandwidth.
Golden Section Search Method: The golden section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. In GWR this method helps searching for the optimal bandwidth.
Equal Interval Method: The equal interval is an intuitive method to search for the optimal bandwidth since it simply checks each bandwidth between the predefined minimum and maximum bandwidth based on a fixed user-input interval. Therefore, if user chooses to use equal interval method, it is mandatory to fill the three optional fields:
Important Note: Pay attention to the units of bandwidth, meters for fixed kernel type, number of nearest neighbors for adaptvie one.
Specify how the extent of the kernel should be determined.
Plugin Authors: Gao & Song
Algorithm Version: 1.2