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

Multiscale mobility patterns and the restriction of human movement