Multiscale mobility patterns and the restriction of human movement.

Area of Science:

• Computational social science
• Network science
• Epidemiology

Background:

• The COVID-19 pandemic significantly impacted global human mobility patterns.
• Understanding human mobility at multiple scales is crucial for public health and policy.
• Existing administrative divisions (e.g., NUTS regions) may not accurately reflect actual human movement.

Purpose of the Study:

• To analyze the multi-scale nature of human mobility using Facebook Movement data before and during the UK's first COVID-19 lockdown.
• To identify intrinsic mobility scales that better represent human movement than administrative regions.
• To model the impact of lockdown restrictions on mobility patterns and recovery.

Main Methods:

• Utilized Facebook Movement maps data from before and during the first UK lockdown.
• Employed unsupervised multiscale community detection on the UK mobility graph to extract flow communities at various coarseness levels.
• Applied a linear decay shock model to quantify lockdown effects and recovery times.

Main Results:

• Identified robust partitions of human mobility into flow communities at multiple scales, outperforming traditional administrative regions.
• Fine-scale partitions closely matched UK travel-to-work areas and captured broader mobility patterns.
• Observed mobility reverting to fine scales during lockdown and expanding to coarser scales as restrictions lifted, with quantifiable regional variations in shock strength and recovery.

Conclusions:

• Human mobility exhibits intrinsic multi-scale structures that are better captured by data-driven community detection than administrative boundaries.
• The COVID-19 lockdown temporarily altered mobility patterns, causing a reversion to finer scales before a gradual expansion.
• The study provides a quantitative framework for understanding and modeling mobility dynamics during large-scale disruptions.