Data-driven public health in times of pandemic: towards deep spatialisation
Dušan Ristić, Dušan Marinković · Territory Politics Governance · 2023
In the COVID-19 pandemic, data-driven healthcare practices are subject to rapid growth. These practices challenge the aspects of social and spatial orders encapsulated in the term deep spatialisation. Our assumption is that this term indicates the emergence of new possibilities of relations among open data, social actors and localities. We explore this using established concepts in social theory of space and media theory. Namely, the concept of deep mediatisation – to point to the background processes of digitalisation and datafication; and the concept of translocalisation – to explain the spatial dynamics of the process. We analyse examples of data-driven public health practices developed during the COVID-19 pandemic. We define deep spatialisation as a modulation of deep mediatisation. It emerges at the global scale with effects in spatial dynamics we recognise as translocalisation. It is still a ‘sensitising concept’ and it needs further empirical verification. However, it does seem plausible in aiming to fully grasp the refigurations that occur simultaneously in social and spatial structures as a result of open data-sharing and use in data-driven practices, particularly in the domain of public health.