SCIENTIA GEOGRAPHICA SINICA ›› 2015, Vol. 35 ›› Issue (5): 615-621.

• Orginal Article •

### An Improved Spatiotemporally Weighted Regression Analysis Based on Spatiotemporal Windows: A Case Study of Housing Price of Huangshi City, Hubei Province

Xin-gang WANG(), Yun-feng KONG

1. College of Environment and Planning, Henan University, Kaifeng, Henan 475001, China
• Received:2014-01-18 Revised:2014-03-20 Online:2015-05-20 Published:2015-05-20

Abstract:

Geographically weighted regression (GWR) is a useful technique for exploring spatial nonstationarity by calibrating a regression model which allows different relationships to exist at different points in space. However, spatial autocorrelation can invalidate the model assumption and sometimes may result in residual dependency. This article aims to improve the spatiotemporal weighted regression (GTWR) by introducing additional variables based on spatiotemporal windows. The size parameters for defining spatiotemporal windows are estimated by spatial and temporal statistics of all the sample data. The new window variables are calculated by averaging the explained variables which are located in its spatio-temporal window. The new variables are added in GTWR as an improved regression (IGTWR) model. Huangshi City, a resource-dependent, land-scarce and multi-center city in Hubei Province, is selected as the study area. 19 300 commercial housing units and 398 buildings in 2007-2012 are collected as sample data. Based on general and spatial statistics, the number of building floors, the plot ratio, the greening ratio, the level of property management, the distance to region center, and the year of sale are selected as explanatory variables. The sample data are analyzed by four regression models respectively: ordinary linear regression (OLS), GWR, GTWR and IGTWR. The optimum size and number of spatiotemporal window are estimated by the Moran's I index and the correlation coefficients between temporal sequences. Modeling results indicate that both the IGTWR model and its variables pass the statistical test at the significant level 0.05. The spatial distribution of the variable coefficients can be explained reasonably. The comparison of all modeling results shows that GWR is better than OLS, GTWR is better than GWR, and IGTWR is better than GTWR, in terms of the measure of goodness of fit (R2), the Akaike information criterion (AICc) , the residual sum of squares (RSS) and the mean squared error(MSE). In the case study, compared with GTWR, the R2, AICc, RSS and MSE from IGTWR are improved from 0.877 to 0.919, 6 226 to 6 206, 49 996 201 to 32 327 472 and 354.427 to 284.969 respectively. The case study indicates that the IGTWR model is effective for temporal and spatial analysis of urban housing price. By introducing window based indicators in GTWR model as new variables, the IGTWR model may estimate the impact of spatial and temporal autocorrelation between geographic data, and thus is able to reduce the model error and increase the model accuracy.

CLC Number:

• P208