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The geographically weighted regression (GWR) is an essential tool forestimating the spatial variation of relationships between dependent andindependent variables in geographical contexts. However, GWR suffers from theproblem that classical linear regressions, which compose the GWR model, aremore prone to be underfitting, especially for significant volume and complexnonlinear data, causing inferior comparative performance. Nevertheless, someadvanced models, such as the decision tree and the support vector machine, canlearn features from complex data more effectively while they cannot provideexplainable quantification for the spatial variation of localizedrelationships. To address the above issues, we propose a geographicallygradient boosting weighted regression model, GWRBoost, that applies thelocalized additive model and gradient boosting optimization method to alleviateunderfitting problems and retains explainable quantification capability forspatially-varying relationships between geographically located variables.Furthermore, we formulate the computation method of the Akaike informationscore for the proposed model to conduct the comparative analysis with theclassic GWR algorithm. Simulation experiments and the empirical case study areapplied to prove the efficient performance and practical value of GWRBoost. Theresults show that our proposed model can reduce the RMSE by 18.3% in parameterestimation accuracy and AICc by 67.3% in the goodness of fit.

GWRBoost:A geographically weighted gradient boosting method for explainable quantification of spatially-varying relationships