The primary scientific goal of the RESALI is to study the food networks and system. Feeding cities, particularly large urban agglomerations, both in quantity and quality, represents a significant challenge for the future of urban environments, especially in the context of sustainability and food justice. At the scale of urban food systems, there is a need for diagnostics to systematically understand the relationships between consumption hubs, food supply, and dietary behaviors. The RESALI project aims to test methods and tools for a detailed analysis of the organization of urban food systems, exploring the connections and disconnections between food supply and demand—essentially, between food resources and certain consumption basins, including the most marginalized and least informed populations.

The project brings together geographers, computer scientists, and practitioners involved in Lyon’s food systems to develop an innovative information system for the Lyon-St-Etienne metropolitan area. This system will integrate structural data (such as census and commerce data) and large-scale individual data, including consumption data and VGI (Volunteered Geographic Information) from user-generated Web 2.0 content.

Methodology:

The project is based on a combination of three innovative methodological approaches:

1. Developing an information system to identify imbalances in the distribution of food resources, access to food, and food consumption behaviors. This will be contextualized within the socio-spatial inequalities of the region.

2. Advancing spatial data mining methods to extract insights about individual practices from geo-located web data (notably from social media platforms like Twitter). This exploratory objective operates under the hypothesis that big data processing approaches in computer science can benefit from integrating spatial analysis models. This aspect will form the core of a doctoral thesis in data science.

3. Incorporating conceptual and systemic reflection into the empirical approach to model the food system. The goal is to use this model to develop scenarios targeting proximity, based on the hypothesis that the supply and demand for local products within a food system not only encourage behavioral diversification but may also create new forms of food injustice.

Expected Outcomes:

Grounded in an interdisciplinary approach, the scientific contributions of the RESALI project will be significant for each of the involved disciplines. These contributions will span methodologies (data mining, enhancement of mining models with spatial analysis models, and spatial analysis using geonumeric data) and issues related to food justice.

The project’s findings on the territorial architecture of food resources and consumption practices—particularly as seen through the lens of social media—will be submitted and discussed at the scale of the Lyon-St-Etienne metropolitan area or specific neighborhoods/sub-spaces within it.

The project aims to demonstrate that food resources are critical sites for ecological, social, and territorial innovation in urban contexts and must be factored into the planning and development of future urban environments.