Drive Pattern on the Spatial Heterogeneity of Residential Land Price in Urban District: A Comparison of Spatial Expansion Method and GWR Model

Based on the spatial properties of the database and the final retained Jiangning District Residential land transfer data from 2004 to 2011, spatial expansion method and geographically weighted regression(GWR) model are applied to simulate the spatial heterogeneity of residential land market in urban district.The influencing factors of residential land price were also tested and analyzed. The results show the following aspects.1)Spatial expansion method and geographically weighted regression(GWR) model can be well applied to simulate spatial heterogeneity of land market in target area. The model could respectively explain 63% of the price changes of residential land and 61% of the price changes of residential land. The interpreting abilities improve significantly than that based on global regression model(47%). Both explanations capacity increased by 16%, 14% and spatial expansion method is slightly better than geographically weighted regression(GWR)model. 2) Spatial expansion method can effectively characterize the spatial structure of the overall trend, which is reflected from the explanatory variables and their interaction term effects on residential land. Geographically weighted regression(GWR) model has advantages in terms of the local parameter estimation. It can make the mode of action of each variable premium visualization by means of GIS. This is a strong rebuttal of the traditional assumptions that hedonic price model has coefficient stability. Overall, the spatial expansion model fits relatively better results. Compared with spatial expansion method, geographically weighted regression(GWR)model can more effectively depict spatial non-stationarity of the influencing factors. In the geographically weighted regression(GWR) model, the order of the average marginal contribution on the land premium from high to low is the distance from water, subway, college and CBD, facility, hospital. Additionally, two variables, the distance from facility and hospital, have the directional difference. 3) The distance from subway, water, university and CBD all have positive effect on marginal residential land price in the entire sample area.They are the key driving factors of residential land price.Each of the affecting patterns has a unique land premium space mode of action. Therefore it can provide scientific basis for segmentation of residential land market in target area. Marginal price effect of waters in residential areas of rapid urbanization is generally greater than industrial areas surrounding. The construction of the subway greatly contributed to construction land expansion of Crisscross and upgrading of residential land price. The construction of University City is also an important strategy for urban development in Urban District, The higher the density of University City, the more significant Its role in promoting residential land price. The marginal price effect of entral business dstrict(CBD) on residential land is progressively decreasing trend from the periphery inward City, but it still has upgrading effect on the surrounding residential land price.