Wednesday, 2 September 2020

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

by Age Poom, Olle Järv, Matthew Zook and Tuuli Toivonen

Big Data & Society 7(2), First published: August 28, 2020

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. Hot spots, quarantine, closed borders, video-conferencing, social distancing and temporary closure of workplaces, schools, restaurants and recreational facilities are all profoundly about distance, separation, and space. Examining the geographical aspect of the pandemic is important in understanding its broad implications, including the broader societal impacts of containment policies. 

The avalanche of mobile Big Data – location and time-stamp data from mobile phone call records, operating system, social media or apps – makes it possible to study the spatial effects of the crisis with spatiotemporal detail even at national and global scales. Beyond health care objectives such as understanding how virus transmission is mediated by human mobility or evaluating adherence to restrictions, mobile Big Data also allows us to understand the changes in people’s daily interactions, mobilities and socio-spatial responses across population groups.

Our advocacy for the use of these data, however, is tempered both by our experiences in recent months with the serious limitations of using mobile Big Data and our unease with the power of these same data to track, surveil and discipline social behaviour at the scale of entire populations. 

Thus, we pose the question: How can we use mobile Big Data for social good, while also protecting society from social harm? Drawing on the Estonian and Finnish experiences during the early phases of COVID-19 pandemic, we highlight issues with quickly developed ad hoc data products as well as the “black box” solutions (Pasquale, 2015) offered by large platform companies that created “new digital divides” among researchers (boyd and Crawford, 2012).

We argue that these examples demonstrate a clear need to re-evaluate the public-private relationships with mobile Big Data and propose two strategic pathways forward.

First, we call for transparent and sound mobile Big Data products that provide relevant up-to-date longitudinal data on the mobility patterns of dynamic populations. To help increase their usefulness, data products should be transparent about their production methodology, and ensure easy access and stability. 

Second, there is also a need to develop trustworthy platforms for collaborative use of raw individual level data. Secured and privacy-respectful access to near real-time raw data is needed for developing and testing sound methodologies for the above-mentioned data products. This would help bridge the Big Data digital divide, enable scientific innovation, and offering needed flexibility in responding to unanticipated questions on changing locations and mobilities in case of crises. To be clear, we do not view this as simple to achieve, particularly as we weigh what kind of institution might best fill this role, or how is “social good” defined and operationalized in practice. But addressing these issues via public debates and academic discourses will leave us better prepared for the next crisis.

Summing up,
  • We need harmonized and representative data about human mobility for better crisis preparedness and social good in general;
  • Methodological transparency about mobile Big Data products are vital for open societies and capacity building;
  • Access to mobile Big Data to develop feasible methodologies and baseline knowledge for public decision-making is needed before the next crisis occurs;
  • Recognizing the fundamental spatiality of the current COVID-19 crisis and crises more generally is the most relevant of all.

Mobile Big Data can help us to better understand and address the important spatial dimensions of COVID-19 pandemic and every other social phenomenon. The challenge is doing so responsibly (Zook et al., 2017) and not normalizing a lack of spatial privacy.


boyd, d, Crawford, K (2012) Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society 15(5): 662–679.

Pasquale, F (2015) The Black Box Society. Cambridge: Harvard University Press.

Zook, M, Barocas, S, boyd, d, et al. (2017) Ten simple rules for responsible big data research. PLOS Computational Biology 13(3): e1005399.

Keywords: mobile Big Data, mobility, COVID-19, spatial data infrastructure, social good, mobile phone data, social media data, privacy