Methodological challenges of digital footprint data for mobility analysis

Digital footprint data offers a variety of new and exciting opportunities for transport planners. However, with these new opportunities come new challenges. This work packages uses a variety of different forms of digital footprint data and works to understand where the data can make a valuable contribution as well as where and how it should not be used.

The main aims of this project are:

  • To develop new analytical approaches to better utilise mobile phone app data to understand mobility flows. Key questions include: How do we minimise bias in mobile phone app data collected using different apps? How do we mitigate privacy risks? How do we examine mobility patterns (e.g., trip purposes, travel mode choice, origin, and destination matrices, etc.) with mobile phone app data?
  • To learn what and who is represented by digital footprint data
  • To enhance existing data sources
  • To develop guidance on the use of different kinds of digital footprint data and associated tools

Lead: Dr. Qunshan Zhao

Latest outputs

Paper: Raturi, V., Hong, J., McArthur, D. and Livingston, M. (2021) The impact of privacy protection measures on the utility of crowdsourced cycling data. Journal of Transport Geography, 92, 103020. (doi:10.1016/j.jtrangeo.2021.103020)

Paper: Livingston, M., McArthur, D., Hong, J. and English, K. (2020) Predicting cycling volumes using crowdsourced activity data. Environment and Planning B: Urban Analytics and City Science, (doi:10.1177/2399808320925822)