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A presentation about a social science experiment where citizens train an AI algorithm to design more liveable cities.

About this Event

This is the fourth event in the programme FRONT LINES, BACK YARDS

– Monthly online events exploring the local frontlines of our multiple crises and drawing on the innovative forms of social and cultural mapping emerging from the backyards created during lockdown.

Today, we are at a crucial moment for urbanism as a result of both the technological context, the new era of mass information (big data, open data, IoT) and a greater desire for transparency and participation on the part of all agents involved in urban planning and design processes. In this context, the Mercè project embodies a new line of research that applies novel machine learning techniques (one of the branches of artificial intelligence) to the disciplines of urban planning, geography, sociology, economics and urban health with the goal of building objective knowledge and open data about urban environments.

Join Mar Santamaria Varas (Architect and Urban Planner and 300.000 Km/s co-founder) for a presentation about the Mercè project, a citizen social science experiment developed by 300,000 Km/s where citizens train an AI algorithm to design more liveable cities. The project incorporated the perceptions of many individuals about what makes cities habitable into knowledge for use in urban planning.

Mar will be joined by two discussants: Nora Rathzel (Professor of Sociology at Umeå University and Barcelona exurb resident) and Paul Watt (Professor of Urban Sociology at Birkbeck College, University of London).

• The Mercè Project

The experiment aims to measure cities’ liveability, a concept that precisely summarises the ultimate objective of urban planning. In addition to quantifying and measuring liveability, the experiment looks at which urban parameters are the most influential in the quantification process.

Instead of trying to construct a single possible description of what a liveable city looks like, Mercè proposes a collective description through the participation of a large number of citizens. Everyone has their own opinions; what one person likes may not appeal to others in the same way. However, if we are able to collect a very large number of different points of view, we can arrive at commonalities that transcend the individual differences. From a large volume of responses we can extract the prevailing characteristics present in all of them.

To do this, the project challenges citizens to evaluate a series of pairs of photographs of different streets in the city. Each photograph has been characterised ahead of time in terms of urban parameters such as building density, land uses, the geometry of buildings, their age, their constructive quality and other more complex values such as the mixtures between these values. We also know the width of the streets, their hierarchy within the urban fabric, the economic activities existing on the ground floor, and the demographics of the area.

By means of an automated learning algorithm we can detect patterns in the training dataset, which consisted of more than 3,000 streets that were rated by citizens. Subsequently, we can classify the remaining city streets (which were not evaluated during the experiment) and, potentially, streets in any city that can apply the same data model that was used. On the other hand, we can determine which characteristics are most important when classifying the streets and, ultimately, in defining liveability.

The final result of the Mercè experiment is a map of the liveability of the city. It shows us which streets are the most liveable, from very low to very high liveability. As the Mercè experiment shows, although liveability is subjective, it is also quantifiable. In other words, thanks to artificial intelligence, we are able to build a consensus about the underlying variables and values in individual perceptions and to synthesise them using an algorithm that turns them into measurable and actionable parameters.

The relevance and innovative nature of the Mercè project comes from applying information and communication technologies to different transversal fields of knowledge, where the common denominator is the city. The project develops a workflow based on public/open data and machine learning techniques to promote algorithmic transparency in data science and citizen participation with a broad social impact. In addition, Mercè provides a solid structure to bridge the gap between subjective and objective knowledge thanks to the use of cutting-edge technologies and the existence of a legal framework (promoted by Europe through transparency laws). At the same time, it positions Spanish cities (with a long tradition in urban planning, including pioneers like Ildefons Cerdà or Arturo Soria) as a training ground for other areas of the planet that do not currently have sufficient data to carry out this type of experiment by openly offering up the generated knowledge and infrastructure.

With a strong participatory component, it also involves citizens in scientific processes and data collection, empowering them in the construction of data sovereignty structures and in the participation in decision-making at the local level. With regard to public institutions, the project proposes a system to inform and evaluate urban planning and policies at the European level, providing a body of knowledge that can be exchanged and compared between cities.

Livingmaps Network is an independent not for profit organisation, we receive no core funding. Our main income comes from live events which we have been unable to organise this year. We are asking for donations of £3 – £5 from people who wish to attend our online events to help us cover our running costs. We greatly appreciate your support.

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