Abstract

Urban food insecurity persists despite the growing availability of food in cities, disproportionately affecting vulnerable populations with limited access to healthy, plant-based diets. This thesis presents a Decision Support System (DSS) and a Spatial System Dynamics (SSD) model designed to evaluate food insecurity in urban food systems and to support spatially differentiated policy interventions.
The research integrates geospatial analysis with system dynamics simulation to investigate (i) the role of urban food production in shaping food insecurity, (ii) the spatial distribution of food availability and accessibility, and (iii) the dynamic impacts of policy measures targeting food security. The Design Science Research Methodology (DSRM) structures the development of innovative IT artifacts for urban and regional decision-makers.
The resulting artifacts comprise a high-level Causal Loop Diagram capturing reinforcing and balancing mechanisms of food insecurity, stock-and-flow subsystems modeling accessibility, household finances, and household circumstances, and a microservices-based DSS that couples system dynamics with GIS through a shared database. Documented data workflows ensure transparency and reproducibility.
The model is applied to the urban agglomeration of Montréal. Baseline results estimate household food insecurity (HFI) at 12.8%, closely aligning with published values for Québec. Scenario and extreme-condition analyses establish plausible bounds for the system and reveal pronounced spatial heterogeneity across boroughs. Results demonstrate that uniform policy interventions yield non-uniform effects, with some areas experiencing substantial improvements while others show limited or adverse responses. The thesis underscores the analytical and decision-making value of integrating system dynamics with spatial data for urban food policy assessment.