High-resolution human mobility: from face-to-face interactions to the urban scale

Digital technologies and advanced analytics provide the opportunity to quantify specific human behaviors with unprecedented levels of detail and scale. Personal electronic devices and wearable sensors, in particular, can be used to map the network structure of human mobility and proximity in environments relevant for research in computational social science, urban mobility and public health. In the first part of this talk I will review the experience of the SocioPatterns collaboration, a decade-long international effort, led by my team, aimed at studying high-resolution human social networks using wearable sensors in important settings such as schools, hospitals, and low-resource rural environments.

I will illustrate the network structures observed in empirical data, reflect on generalization and data incompleteness, and review modeling approaches based on ideas from network science and machine learning. In the second part of the talk I will shift to the scale of an entire city, discussing the opportunities – and limitations – of using call Detail Record (CDR) data from telephone operators to map urban mobility and socio-economic conditions. I will compare traditional data sources, such as census data and origin-destination surveys, with digital proxies for urban mobility, and I will specifically focus on novel perspectives enabled by gender-disaggregated CDR data. I will close with a reflection on the challenges and promises of using privately-held data for the public interest.