Urban Citizen Learning proposes a novel consulting service based on building an urban data model used to evaluate and simulate public policies inside a digital twin through a participatory process in which citizens train AI algorithms. The project’s main goal is to put citizens at the centre of city governance through a process -fuelled by collective participation and data visualization- that enables us to include intangible aspects in international urban agendas (ODS, European Green Deal, the Renovation Wave or the New European Bauhaus).
We are now at a turning point for these strategic public policies’ implementation in the framework of urgent decarbonisation. If these policies imply the radical transformation of urban environments from the point of view of sustainability, they also promote liveable and healthy environments (including beauty and quality of the environment as a driver). Hence, there is an urgent need to evaluate their implementation comprehensively.
The project consists of a digital data model (describing the characteristics of the urban environment); a prioritisation methodology of the data model through a ranking tool which collects the citizens’ criteria about the quality and liveability of the urban environment and supports a clustering to evaluate urban characteristics; and a data model acting as a digital twin to evaluate and simulate policy implementation.
In short, we generate a new flow of assessment of urban environments (based on the collective perception and knowledge of citizens to complement public data infrastructure) while supporting transforming evidence-based policy instruments, offering diagnostic, predictive and evaluation tools to support policy making. In addition, we enhance the value of public data structures (empowering local authorities in data governance processes together with citizenship) and engage citizens in the development of public policies that deal with complex urban challenges (social, environmental, economic) such as climate change, sustainable mobility or reduction of inequality.