NutriNet explores how data and learning algorithms can improve the access to food in cities, its distribution and foster social interaction between different communities.

The research explores how urban topography hides notions of food inequalities. Large datasets on food consumption, diversity and deprivation score mapped at national and local scale constitute the input of a deep learning algorithm that guides the development of a strategy to manage the phenomenon of food deserts amidst the dense metropolis. The new dynamic landscape created, expands throughout London starting from smaller clusters to eventually form a new urban network designed around food.

NutriNet engages human and digital spatial cognition, employing its own growth network algorithm, capable of analysing and optimising growth patterns based on spatial and real-time data. This algorithmic toolkit predicts the optimal positions and functions for the growth of a cultivation network that promotes innovative social public spaces. The project focused on the design of one of these hubs located in Battersea. Through a network of productive areas and public spaces, NutriNet offers a new perspective on interpreting the city’s potential to be autonomous and minimise its footprint on the hinterlands.