Instructions for Use

Geographically weighted regression is a local form of linear regression that detects the non-stationary nature of spatial relationships by embedding spatial structures into linear regression models. Through regression analysis, we can model, examine, and explore spatial relationships, as well as explain the many factors behind the observed spatial patterns. You can refer to the description of [Geographically Weighted Regression] (../../../Features/Analysis/SpatialStatisticalAnalysis/GeographWeightedRegression/). Return a summary of the result dataset and geographic weighted regression model, as follows: *MeanSquaredError: The mean square error, the square of the error between the predicted value and the true value. *RootMeanSquaredError: The root mean square error, the mean square root of the error between the predicted value and the true value. *Mean absolute error: the mean of the absolute value of the error between the predicted value and the true value. *R2: Determination coefficient. Based on the value of r2, the quality of the model can be judged, with a value range of [0,1]. Generally speaking, a larger r2 indicates a better fitting effect of the model. R2 reflects approximately how accurate it is, because as the number of samples increases, r2 will inevitably increase, and cannot truly quantitatively explain the accuracy level. It can only be roughly quantified. *Explained Variance: Explained variance.

Parameter Description