Understand citizen's needs and adapt cities' main squares
Creation of a survey app, a comfort index and a model that can be replicated to other cities' main squares
// OUTCOMES & KEY RESULTS
The model developped to understand and adapt cities' main square is based on 3 key components: analyze data from actual usage through sensors and open data, analyze sentiment and opinions based on a survey conducted through an app and on the spot, and the identification of zones of stress and comfort based on these data.
The experimentation took place in Paris' Place de la Nation.
// PARTNERS INVOLVED
Partners provided over 60 data sets including: data from sensors (flows of people, air quality, noise levels), user feedback via touch-screens, open data (weather, bike shares) as well as solar registry, Orthophoto mapping, etc. The selected startup – Qucit – uses AI to solve urban issues such as bike sharing, car parking or pedestrian comfort.
// PAIN POINT & OPPORTUNITY
The City of Paris began the renovation of seven large Parisian squares with the goal of reorienting their use in favor of pedestrians. The objective is to renovate them with citizen's perspective in mind.
The micro-local level of precision of the analysis is ideal to carry out the type of experimentation whose methodology could be extrapolated to other cities in France and worldwide.