GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity

Abstract

Spatiotemporal regression is a crucial method in geography for discerning spatiotemporal nonstationarity in geographical relationships and has found widespread application across diverse research domains. This study implements two innovative spatiotemporal intelligent regression models, i.e., Geographically Neural Network Weighted Regression (GNNWR) and Geographically and Temporally Neural Network Weighted Regression (GTNNWR), which use neural networks to estimate spatiotemporal nonstationarity. Due to the higher accuracy and generalization ability, these models have been widely used in various fields of scientific research. To facilitate the application of GNNWR and GTNNWR in addressing spatiotemporal nonstationary processes, the Python-based package GNNWR has been developed. This article details the implementation of these models and introduces the GNNWR package, enabling users to efficiently apply these cutting-edge techniques. Validation of the package is conducted through two case studies. The first case involves the verification of GNNWR using air quality data from China, while the second employs offshore dissolved silicate concentration data from Zhejiang Province to validate GTNNWR. The results of the case studies underscore the effectiveness of the GNNWR package, yielding outcomes of notable accuracy. This contribution anticipates a significant role for the developed package in supporting future research that will leverage big data and spatiotemporal regression techniques.