Simulating the Spatial Heterogeneity of Housing Prices in Wuhan, China, by Regionally Geographically Weighted Regression

Geographically weighted regression (GWR) is an effective method for detecting spatial non-stationary features based on the hypothesis of proximity correlation. In reality, especially in the social and economic fields, research objects not only have spatial non-stationary characteristics, but also spatial discrete heterogeneity characteristics. Therefore, how to improve the accuracy of GWR estimation in this case is worth studying. In this paper, a regionally geographically weighted regression (RGWR) is proposed. Using incoming dummy variables, the zoning discrimination is added to the spatial kernel function of GWR, the spatial kernel function is modified, the spatial weight is optimized, and the influence of near heterogeneous observation points is reduced. In this paper, the residential sale price in Wuhan City is taken as an example in the analysis of three aspects: model performance, fitting effect and influencing factors. The results show that the introduction of a zoning dummy variables can significantly improve the model accuracy of a fixed bandwidth and adaptive bandwidth. Under a fixed bandwidth, compared with the GWR model, RGWR increases R-2 and R(2)adj from 0.6776 and 0.6732 to 0.777 and 0.7746, respectively, and the Akaike information criterion, corrected (AICc) standard decreases by 37.4006 compared with GWR, which proves the effectiveness of the method.

Automated summary

This paper proposes a regionally geographically weighted regression (RGWR) method, which improves the accuracy of GWR estimation by introducing zoning dummy variables and optimizing spatial weights, especially for research objects in social and economic fields with spatial discrete heterogeneity characteristics.