On March 21, CIRANO’s Pole on Data Science for Trade and Intermodal Transportation held its annual conference on the theme of Data Science for St. Lawrence – Great Lakes: Innovation and Collaboration. The event brought together stakeholders from the government, industry, and academia, from both Canada and the US. The speakers presented and discussed how data science could help solve key trade and transportation issues in the binational St. Lawrence – Great Lakes (SLGL) region.
The changing geopolitics, recovery from the COVID-19 pandemic and the rapidly evolving effects of climate change have enveloped the pace of globalization and its networks with tremendous uncertainty. With a combined current-dollar GDP of over $7.9 trillion in 2022, the SLGL region is an economic powerhouse with significant potential for sustained growth.
The region offers policymakers an enticing opportunity for cross-border collaboration in delivering a stable and prosperous business environment in an increasingly challenging global landscape. Strengthening the resiliency of supply chains and devising a plan of action for disruptions to the region’s multimodal transportation network are essential steps toward this goal.
The SLGL dataHub, an analytics platform and database, is being designed and developed to fulfill this very need. Our team presented an early version of the SLGL dataHub to the audience in attendance, showcasing the capabilities of digital twins in breaking down complexity with the use of data science.
Using real-time data to create a digital projection of the bi-national economy, the SLGL dataHub will offer users granular firm-level insights and analysis. We study this region through four dimensions of international business literature which includes global value chains (GVCs), economic clusters, economic complexity and the gravity model.
The digital twin uses advanced machine learning models to offer predictive modelling and risk analysis. By simulating scenarios such as blockades at important routes of international trade or port infrastructure being rendered inaccessible due to rising water levels, the SLGL dataHub will be able to effectively quantify impact on trade and transportation networks from such disruptions.
Access to high quality and relevant data is a key factor influencing the efficacy of these simulations, which we continuously seek as we develop the SLGL dataHub.
The SLGL dataHub will be made available to users in the coming weeks.
