Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities

Erhan, Laura; Ndubuaku, Maryleen; Ferrara, Enrico; Richardson, Miles; Sheffield, David; Ferguson, Fiona J.; Brindley, Paul; Liotta, Antonio

Abstract

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 paper, we investigate a novel way of analyzing data from social sciences studies by employing machine learning and data science techniques. This enables us to maximize 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, U.K., involving 1870 participants for two different time periods (7 and 30 days). With the help of a smartphone app, both objective and subjective data were 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 analyze the time spent by people in parks as well as the top interaction areas. This paper allows us to gain an overview of certain patterns and the behavior of the citizens within their surroundings and it proves the capabilities of integrating technology into large-scale social studies.

Citation

Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., Brindley, P., & Liotta, A. (2019). Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/access.2019.2897217

• Journal Article Type: Article
• Acceptance Date: Jan 5, 2019
• Online Publication Date: Feb 4, 2019
• Publication Date: Feb 19, 2019
• Deposit Date: Jul 29, 2019
• Publicly Available Date: Jul 30, 2019
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 19890-19906
• DOI: https://doi.org/10.1109/access.2019.2897217
• Keywords: Data analysis, data science, smart cities, social science, urban analytics, urban planning
• Public URL: http://researchrepository.napier.ac.uk/Output/2006186
• Contract Date: Jul 29, 2019