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Abstract. Spatiotemporal regression is a crucial method in geography for discerning spatiotemporal non-stationarity in geographical relationships, which has found widespread application across diverse research domains. This study implements two innovative spatiotemporal intelligent regression models, namely geographically neural network weighted regression (GNNWR) and geographically and temporally neural network weighted regression (GTNNWR), integrating the spatiotemporal weighted framework and neural networks. Demonstrating superior accuracy and generalization capabilities in large-scale data environments compared to traditional methods, these models have emerged as prominent tools. To facilitate the seamless application of GNNWR and GTNNWR in addressing spatiotemporal non-stationary processes, a 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 leverages big data and spatiotemporal regression techniques.
Yin et al. (Wed,) studied this question.