2015/03/09 by A. Stewart Fotheringham, Ricardo Crespo, Jing Yao · 610 citations
Economics, Econometrics and Finance · Environmental Science · Mathematics · #Econometrics #Geographically Weighted Regression #Geography #Land Use and Ecosystem Services #Mathematics #Regional Economics and Spatial Analysis #Regression #Regression analysis #Spatial and Panel Data Analysis #Statistics
paper · doi:10.1111/gean.12071
published in Geographical Analysis 47(4), 431-452 (Wiley)
openalex publication_date 2015/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19-year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling.