The centerpiece of our work is a Decision Support System (DSS) that makes Spatial System Dynamics usable in practice. System dynamics models are powerful at representing feedback in time — but they are blind to space. Geographic Information Systems (GIS) capture space precisely — but cannot simulate feedback. Our DSS connects the two worlds. The simulation engine and GIS share a common database, exchanging inputs and results.
Technically, the DSS is built as microservices — independent components for data management, simulation, and visualization that communicate through standard interfaces (REST APIs). Models are stored in XMILE, an open XML standard for system dynamics, so they remain readable, versionable, and tool-independent. The result is a tool that is methodologically rigorous, technically scalable, and open by design: the same architecture demonstrated here for Montreal urban food systems can be applied to energy or water models tomorrow.
System Dynamics
System dynamics is a methodological approach used to understand, model, and analyze complex systems over time. It focuses on the interactions between various components within a system, capturing feedback loops, delays, and nonlinear relationships. By using causal loop and stock-and-flow diagrams, system dynamics helps to predict how changes in one part of the system impact the whole.
This methodological approach has a broad range of applications across fields, including economics, environmental science, healthcare, and urban planning. It has been used to study issues like resource management, policy interventions, and sustainability. System dynamics enables decision-makers to test scenarios and develop strategies for long-term systemic improvements.
Geospatial Information Systems
Geographic Information Systems (GIS) are powerful tools that collect, analyze, and visually represent spatial data. This allows users to understand geographic patterns and relationships. GIS supports decision-making in urban planning and related fields, including environmental management, disaster response, transportation, energy, food, and water planning. Users can overlay data to identify trends and optimize resource management. GIS also plays a key role in policy-making by providing data-driven insights for sustainable development. The ongoing advancements in artificial intelligence (AI) and real-time analytics are further enhancing the capabilities of GIS, enabling organizations and governments to make more informed decisions, leading to improved planning and sustainability.
From Data to Decisions
Every simulation is only as good as its data. Urban data management is essential for optimizing the use of energy, buildings, food systems, and transportation within urban areas. Efficient data collection, storage, integration, and analysis ensure informed decision-making and facilitate sustainable urban planning. For the Montréal case study, we integrated food establishment records from the city’s open data portal, follow-up data on food aid requests from the 211 helpline, the Statistics Canada 2021 Census, OpenStreetMap layers, and building data. Documented ETL pipelines clean, geocode, and map these sources into the shared database; population-weighted aggregation brings them to the borough level; and every processing step is published so results can be reproduced — and the workflow recalibrated for other cities.
