Research Output
An AI approach to Collecting and Analyzing Human Interactions with Urban Environments
  Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens’ quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens’ well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41% acceleration factor and a 75% reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizen’s well-being projects, and sociology, to mention but a few cases.

  • Type:


  • Date:

    25 September 2019

  • Publication Status:


  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    Edinburgh Napier Funded


Ferrara, E., Fragale, L., Fortino, G., Song, W., Perra, C., di Mauro, M., & Liotta, A. (2019). An AI approach to Collecting and Analyzing Human Interactions with Urban Environments. IEEE Access, 7, 141476-141486.



Data Science ; Social Science ; Smart City ; Data Analysis ; Urban Analytics ; Artificial Intelligence ; Crowd Sensing

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