  From the perspective of human mobility, the COVID-19 pandemic constituted a
natural experiment of enormous reach in space and time. Here, we analyse the
inherent multiple scales of human mobility using Facebook Movement Maps
collected before and during the first UK lockdown. First, we obtain the
pre-lockdown UK mobility graph, and employ multiscale community detection to
extract, in an unsupervised manner, a set of robust partitions into flow
communities at different levels of coarseness. The partitions so obtained
capture intrinsic mobility scales with better coverage than NUTS regions, which
suffer from mismatches between human mobility and administrative divisions.
Furthermore, the flow communities in the fine scale partition match well the UK
Travel to Work Areas (TTWAs) but also capture mobility patterns beyond
commuting to work. We also examine the evolution of mobility under lockdown,
and show that mobility first reverted towards fine scale flow communities
already found in the pre-lockdown data, and then expanded back towards coarser
flow communities as restrictions were lifted. The improved coverage induced by
lockdown is well captured by a linear decay shock model, which allows us to
quantify regional differences both in the strength of the effect and the
recovery time from the lockdown shock.
