Analysing objective and subjective data in social sciences: Implications for Smart Cities

The ease of deployment of digital technologies and the Internet of Things, gives us the opportunity to carry out large scale social studies and to collect vast amounts of data from our cities. In this work we investigate a novel way of analysing data from social sciences studies by employing machine learning and data science techniques. This enables us to maximise the insight gained from this type of studies by fusing both objective (sensor information) and subjective data (direct input from the users). The pilot study is concerned with better understanding the interactions between citizens and urban green spaces. A field experiment was carried out in Sheffield, UK, involving 1870 participants for two different time periods (7 and 30 days). With the help of a smartphone app, both objective and subjective data was collected. Location tracking was recorded as people entered any of the publicly accessible green spaces. This was complemented by textual and photographic information that users could insert spontaneously or when prompted (when entering a green space). By employing data science and machine learning techniques, we identify the main features observed by the citizens through both text and images. Furthermore, we analyse the time spent by people in parks, as well as the top interaction areas. The study allows us to gain an overview about certain patterns and the behaviour of the citizens within their surroundings and it proves the capabilities of integrating technology into large-scale social studies.