A two-step local smoothing approach for exploring spatio-temporal patterns with application to the analysis of precipitation in the mainland of China during 1986–2005

There has been a growing interest on using local modelling techniques for the analysis of spatio-temporal data because of their powerfulness in extracting the underlying local patterns in the data. In this study, we propose a two-step local smoothing approach to explore spatial patterns and temporal trends of spatio-temporal data via combining the geographically weighted regression and the local polynomial smoothing procedure. The proposed method incorporates both spatial and temporal information into the calibration process and makes it easier to implement visualization of the results. A simulation experiment is conducted to assess the performance of the proposed method and the results show that the method works satisfactorily. A real-world spatio-temporal data set is analyzed to demonstrate the practical usefulness of the method.