Abstract
The ease of deployment of digital technologies and the Internet of Things, enables the collection of vast amounts of data from our cities that are now becoming increasingly connected and smart. This study aims to investigate the interaction of citizens with the urban green areas, with a view to improve wellbeing through targeted, financial-efficient interventions. A field experiment has been carried out in Sheffield, UK involving 1,870 participants. We collected both objective and subjective data, using a tailor-made smartphone app. Location tracking was activated as soon as people entered any of the publicly accessible green areas, to automatically determine type of activity and interaction. This was complemented by textual and photographic information that users could insert spontaneously or when prompted. We could thus establish interaction patterns and map these to demographics, using data science methods to find out the particular features that affected wellbeing, for different categories. Using text and image analysis, we found a distinctive clustering around age and gender, showing that different categories are interested in specific features. The younger age group (18-35 years) prioritized the presence of urban features in parks, whereas the older group (54-72 years) paid more attention to nature. Pinpointing the particular elements that improve human interaction with the urban green spaces, is the starting point for improving city management and planning using technology and science.