Exploring urban mobility using new forms of spatial big data from mobile phones
- Friday 8 April 2022
- 7:00 - 8:00 (BST)
- Online (via Microsoft Teams) GET DIRECTIONS
UBDC's Dr Michael Sinclair will present our work on the representation and use of new forms of mobile phone data for urban research at this City Futures Research Centre seminar.
New forms of spatial big data generated by the use of mobile phones offer enormous potential to explore human activity by virtue of the data volume available, and the spatial and temporal detail provided. However, uncertainty in the geographic and socio-demographic representativeness of the data means there is an ethical risk that underlying biases in the datasets could impact results. Leading to biased evidence then be used to support policy. In particular, underlying social inequalities in access to and use of mobile phones may be reproduced through the analysis, skewing resources towards already-advantaged social groups. Researchers also need to be mindful of legal and ethical issues around privacy.
This research begins by establishing a sound basis for the use of these new forms of data by addressing the issues of bias and representativeness in mobile phone data directly. We advance methods in home location detection, using high resolution land use data, for new forms of mobile phone application data and compare resulting estimates on the mobile phone population to public and private socio-demographic data. The results allow us to identify and potentially reduce biases in mobile phone data which arise through uneven population coverage as well as the variations which arise between different commercial providers of the data. Using these results as a foundation, we utilise two large mobile phone application datasets to explore human mobility across a wide range of contexts in the urban setting of Glasgow and its surrounding regions. We present examples of how mobile phone data can be used in collaboration with government to aid policy related to greenspace, 20 minute neighborhoods, and for transport modelling as we recover from the pandemic.