This VCO presents work completed by the Team 5 project at the 2023 I-GUIDE Summer School “Convergence Science in Action”. This team analyzed the spatiotemporal dynamics of a meta-coupled world using international trade data.

The world is increasingly interconnected, both environmentally and socioeconomically, through various flows (e.g., movement of goods, services, people, and capital). These flows have enormous impacts on biodiversity, ecosystem services, and sustainable development but little research has been systematically conducted on such flows across multiple scales (global, regional, national). This team utilized international trade data from United Nation Comtrade to visualize trade flows of crops and medication and build trade network communities among different countries in different areas of the world from 2019 to 2021. This team's research outcomes include the circos maps that illustrate the trade volume proportions occupied by each country or subregion at three distinct time points, the identification of global and regional trade networks/communities for medications and crops, and the examination of trade flow dynamics in key countries. These outcomes can be used to investigate the drivers of international trade dynamics, assess the impacts of these flows on sustainable development goals and biodiversity, identify the effects of disruptions such as COVID-19 and conflict on trade flows, and suggest actionable solutions through policy and practice adjustments.

Kate Brandt is a PhD student at the University of North Carolina at Chapel Hill.

Maryam Torkashvand is a Ph.D. student at the University of Iowa Department of Geography and Sustainability Science, studying GIScience.  My main interest is to apply the geographic approach and advanced geospatial data science and visualization techniques to help solve real-world challenges. I am currently working on an NSF-funded project analyzing the complex relationship between kinship networks and migration in the US over 135 years using the largest connected family tree in the world, which connects 40 million individuals in a single pedigree. Besides this, during my master's study, I gained experience and published papers on the application of GIS models in physical geography. I used GIS, multi-criteria decision methods, and optimization algorithms to study hydrological networks and their connection with groundwater vulnerability.

Jinwen Xu is a research associate in Florida International University. His specialized field lies in disaster resilience and geospatial data science, with research interests spanning community resilience, CyberGIS, machine learning, and GeoAI. He holds a PhD in Geography and Environmental Science and Policy (GEP) from University of South Florida and a Master of Urban and Environmental Planning (MUEP) from Arizona State University. His current research is focused on modeling community resilience from disturbed human activities during disasters and social and environmental justice issues underlying the disruptions.