Spatial-temporal analysis of the international trade network - Donghai Liu; Ziwen Yang; Kun Qin; Kai Li (Geo-spatial Information Science)
With the support of spatial-temporal data analysis technologies and network science, the International Trade Network (ITN) research has made significant progress, demonstrating broad application prospects in mining market evolution and predicting trade dynamics. Based on all ITN research cases from 2003 to 2023, this paper presents a research framework for ITN analysis, reviewing its advancements in data collection, visualization, topology analysis, structure prediction, and correlation analysis, where the spatial-temporal data analysis technologies have provided crucial methodologies and insights. A multilevel scenario construction theory for complex networks is proposed, highlighting the great significance of spatial regression models and system dynamics models in identifying global mechanisms; the unique value of temporal network analysis under the support of time-series information is discussed. Given the existing limitations, the development of more granular and reliable datasets utilizing big data technologies, as well as the construction of richer spatial-temporal scenarios, are considered as future research agendas.