Geographical and Temporal Weighted Regression (GTWR)

TLDR

Spatiotemporal analysis is essential across GIScience, environmental science, hydrology, and epidemiology, yet integrating time into spatial models remains challenging. This study aims to model local nonstationary processes by extending geographically weighted regression to include temporal effects. The authors develop a geographically and temporally weighted regression (GTWR) model and propose an efficient calibration approach. Applying GTWR to 19 years of London house price data shows it outperforms traditional GWR, confirming the value of temporally explicit spatial modeling.

Abstract

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.