A Hybrid Spatial Model for Prediction and Capturing Spatial
Variation in the Data
Description
It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically
Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify
the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial
prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000)..This hybrid spatial model aims to
improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly
useful for modeling spatially varying relationships and improving the accuracy of spatial predictions.