Abstract: Geographically weighted regression (GWR) serves as a potent approach to discern spatially continuous heterogeneity, premised on the assumption of proximity correlation. However, in practical applications, particularly within the socio-economic domain, the presence of “near-heterogeneous” spatial discrete is frequently observed. Consequently, the challenge of concurrently detecting spatially discrete and continuous heterogeneity to enhance the estimative precision of GWR warrants further investigation. In this study, we introduce a regionally geographic weighted regression (RGWR) analysis methodology, which effectively filters observation points by devising a regional spatial weight computation strategy, refining the spatial kernel function, optimizing spatial weights, and mitigating the impact of “near-heterogeneous” observation points. We utilize housing sales prices in Wuhan as the empirical case, examining the data from three perspectives: regional factor effectiveness, model performance, and model fit. The findings reveal that incorporating regional impact factors considerably enhances model accuracy under both fixed and adaptive bandwidths. Specifically, the regional impact factors stemming from educational determinants yield the most substantial improvement in model accuracy. Simultaneously, under fixed bandwidth conditions, the model's R² value increases by 21.84%, while the MSE rises by 37.09%. This evidence underscores the model's heightened accuracy upon considering regional influence factors, thereby substantiating the effectiveness of the proposed method.

Key words: geographically weighted regression, spatial kernel function, spatial weight, regional impact factor