Mapping Urban Local Metabolism to Support Building Integrated Agriculture

Urban farming is a potential solution for cities to achieve multiple objectives that may not be inherently connected. From a systemic perspective, urban farming can improve urban living conditions by combining aspects related to climate change mitigation, circular economy, food sovereignty, and social inclusion. This study outlines a methodology for mapping urban metabolism characteristics at the neighbourhood and group of buildings scale using open-source Geographic Information Systems (GIS) tools. The aim is to support the design of Building Integrated Agriculture (BIA) solutions and promote sustainable food production strategies, with a focus on carbon-neutral and cost-effective urban farming systems in urban areas. The investigation examines production capacities for new and existing buildings in Milan. To ensure a systematic analysis of the context and definition of potential scenarios, GIS-based tools have been recognized as a viable decision-support mechanism. GIS tools facilitate the consolidation of relevant information on a single platform, providing insights into the thermal behaviour of buildings and inflows/outflows of water and nutrients. In addition, they facilitate the evaluation of how well-mapped resources align with the needs of productive plants, considering factors such as sun exposure, shadows, air temperature, relative humidity, water, and nutrients. To ensure that the method can be replicated and implemented, open-source tools, available open data, and survey data obtained directly were used. The first phase of the work focuses on mapping microclimatic variables, with a specific emphasis on solar radiation, alongside the characterization of water flows and nutrient distribution. In this phase, the proposed methodology is tested in the Italian context, to be later applied and implemented in the Singapore context. In the Italian case, an appropriate geographical area was identified within the census blocks (CBs). CBs are chosen to combine demographic data with other mapped variables, and therefore associate analysis activities with data useful to identify intervention priorities based on socio-economic indicators. Focusing specifically on the possibility of tools to support design activities, the CB constitutes the minimum geographical area suitable for conducting energy and material balances. This allows for the identification and utilization of different dynamics that have been mapped. For this purpose, the data collected and subsequently processed on open-source GIS articulate information starting from the main variables that affect the applicability of production strategies in the context of urban agriculture. These variables include, for example, the availability of solar energy, relative humidity, air temperature, water, and nutrient availability.