Data-driven profiling of inland areas: studying changes in mobile users presence after COVID-19

Cellular networks worldwide are currently experiencing a significant surge in service demand, forcing operators to focus on the accurate modeling of network dynamics as a key task to enhance efficiency. Besides being useful for optimizing network functioning, mobile data analytics have unleashed unforeseen opportunities to address several social and urban issues on a large scale. In this work, we seize such opportunities and propose a framework capable of profiling urban settlements based on the interplay between their attractiveness and the characteristics of the built environment. Focusing on the impact of the COVID-19 pandemic on mobile users’ behavior, we conduct a comprehensive case study in Italy. Leveraging real-world mobile radio access data, we investigate the spatial variations in people’s visiting patterns, providing insights into how these changes correlate with the social and urban context characterizing the reference area.