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Creating a digital twin
The SLGL dataHub is a digital twin of the St. Lawrence and Great Lakes region’s economy, which digitally projects trade networks of this macro region. A digital twin exploits real-time big data to offer a visual representation of a real-life system or process.
By mimicking the characteristics and behaviours of the real-life economy of the SLGL region onto the virtual realm, we enable a dynamic study of the underlying systemic interactions of actors connected through economic activities. With a comprehensive and real-time view of the economy, stakeholders will gain insights into the performance, efficiency and challenges facing the economy, helping them better decide on the future course of their actions.
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An inductive approach of studying economies
The pace of globalization is under tremendous uncertainty with the shifting deadlocks in geopolitics, the undergoing recovery from the COVID-19 pandemic and the rapidly advancing consequences of climate change. Supply chains are required to be optimized and resilient to these ever increasing challenges, making a change in perspective imperative.
For far too long, we have used tools and models that have inherently been designed with biases introduced by a hypothetico-deductive research approach. The market works beyond the confines of academic concepts, definitions and arbitrary geographic delineations. For economic analysis to be effective, new variables that explain the organization of markets need to be identified. Not only does data science offer the discovery of such new insights through an inductive approach to research but it also helps us frame better questions.
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Combining powerful datasets
Digital data lies at the heart of the SLGL dataHub, as it is a foundational requirement of both its architecture and our inductive research approach. The digital twin uses high quality geospatial data on trade and transportation from powerful datasets that have been meshed together and made real-time. This process brings together relevant and high-quality data that has so far existed in siloes, making these datasets interoperable. Our dataset serves as a one-stop shop for trade and transport data on the SLGL region, reducing information asymmetries for stakeholders with an interest in this region.
The World Input-Output Database (WIOD) maintained by the Groningen Growth and Development Centre (GGDC) of the University of Groningen acts as our primary dataset. While this type of data has revolutionized the study of GVCs by making it possible to study value added by an industry in a specific country, the GGDC has such data only up to 2014. We aim to update this dataset and contextualize it to the SLGL region while also enhancing its granularity by increasing the frequency of data.
The following table provides a current list of the datasets used.
| Dataset | Description | Domain | Geographic Coverage | Time Period | Unit | Frequency | Source |
|---|---|---|---|---|---|---|---|
| Trade in Embodied CO2 (TeCO2) | Estimates on embodied carbon in final demand emitted anywhere in the world along global production chains | Climate | Global | 1995-2018 | metric tons of CO2 | Annual | OECD |
| Canadian International Merchandise Trade Web Application | Trade between Canada (at the provincial level) and the rest of the world – Also includes trade within Canada | Economic | Global | 1988-Present | Dollar values | Monthly | Statistics Canada |
| Inter-Country Input-Output (ICIO) | IOTs for 76 countries (and the rest of the world) | Economic | Global | 1995-2020 | Dollar values | Annual | OECD |
| TradeStats Express | Trade between US (at the state level) and the rest of the world | Economic | Global | 2009-2023 | Dollar values | Annual | U.S. Census Bureau |
| Registre des entreprises | Information on active and inactive enterprises in Quebec | Economic | Quebec | 19XX-Present | Firm | Bi-weekly | Gouvernement du Québec |
| StateIO/stateior | IOTs for states in the US | Economic | US | 2012-2020 | Dollar values | Annual | U.S. Environmental Protection Agency |
| Canadian Freight Analysis Framework | Data on freight flows by commodity and mode of transport within Canada | Transport | Canada | 2011-2017 | Volume and dollar values | Annual | Statistics Canada |
| Grain Supply Chain Dashboard | Movement of grain by rail in Canada at the station and corridor level | Transport | Canada | 2016-Present | Volume | Daily | Statistics Canada |
| Air Carriers: T-100 Segment (US Carriers Only) | Time taken by planes between O/D pairs | Transport | Canada/US | 1990-Present | Time | Monthly | Bureau of Transportation Statistics |
| Border Crossing Entry Data | Number of trucks, trains and containers entering the US through border entry points with Canada | Transport | Canada/US | 1996-Present | Volume | Daily | Bureau of Transportation Statistics |
| Great Lakes St. Lawrence Seaway System Intermodal Map | Major ports in the St. Lawrence – Great Lakes Seaway | Transport | Canada/US | NA | Port | NA | Great Lakes St. Lawrence Seaway System |
| Global Shipping Lanes | Shipping routes geo-referenced from CIA’s Map of the World Oceans | Transport | Global | NA | NA | NA | Benden, P. (2022) |
| Spillover Simulator | Maritime capacity at risk of facing delays due to port disruptions affecting outgoing vessel movement | Transport | Global | 2022 (base year) | Dollar values and time | NA | IMF PortWatch |
| Trade-and-Transport Dataset | Cost of transportation by mode and commodity between O/D pairs | Transport | Global | 2016-2021 | Dollar values | Annual | UNCTAD, World Bank |
| Interstate Truck Trips by Origin and Destination | Provides the annual number of interstate trips undertaken by freight carrying trucks in the US | Transport | US | 2020-2022 | Trips | Annual | Bureau of Transportation Statistics |