We are a team of economists, geographers, engineers and mathematicians from the University if Oxford, united by our view that mobile phone data may help to understand, predict and control the course of Covid-19.
Epidemic spreading is mediated by human contacts and it is thus critical to understand, in real time, how governmental measures translate into change of behaviour in the UK. For this purpose, we have implemented a set of metrics in order to capture mobility patterns from mobile data, that can be used by policy makers to inform their decisions and by modellers to calibrate the epidemic models.
The population movement, POI and flow data that we use is provided by Cuebiq, which is a location intelligence and measurement platform. Through its Data for Good programme, Cuebiq provides access to aggregated and privacy-enhanced mobility data for academic research and humanitarian initiatives. This first-party data is collected via anonymised users who have opted-in to provide access to their location data anonymously, through a GDPR-compliant framework. The underlying anonymised data is collected via smartphone applications from users who have opted-in with complete anonymity regarding their personal identity and personal details. At the device level, iOS and Android operating systems combine various location data sources, including GPS, wifi, beacons and network. These data sources provide geographical coordinates across a range of accuracy. Location accuracy is determined on a device-by-device basis and is therefore variable in nature. In terms of sampling frequency, data is aggregated over five minute windows.
The original data is confidential but all the statistical indicators used to prepare figures on this site can be downloaded. In addition, we are open to recommendations, so do not hesitate to send us suggestions of alternative metrics, which may be more appropriate from your modelling perspective, and we will be happy to look into it if relevant.
Yes. Beyond simple anonymisation, in order to preserve privacy the data provider aggregates sensitive locations such as home and work areas to the Geohash 6 level.
Department of Economics, University of Oxford
SKOPE, Department of Education, University of Oxford
Saïd Business School, University of Oxford
Department of Engineering Science, University of Oxford
Tony Blair Institute for Global Change
Transport Studies Unit, School of Geography and the Environment, University of Oxford
Department of Engineering Science, University of Oxford
Mathematical Institute, University of Oxford
School of Geography and the Environment, University of Oxford
Please send enquires to contact@ox-ai.com
