  With the logistic challenges faced by most countries for the production,
distribution, and application of vaccines for the novel coronavirus
disease~(COVID-19), social distancing~(SD) remains the most tangible approach
to mitigate the spread of the virus. To assist SD monitoring, several tech
companies have made publicly available anonymized mobility data. In this work,
we conduct a multi-objective mobility reduction rate comparison between the
first and second COVID-19 waves in several localities from America and Europe
using Google community mobility reports~(CMR) data. Through multi-dimensional
visualization, we are able to compare in a Pareto-compliant way the reduction
in mobility from the different lockdown periods for each locality selected,
simultaneously considering all place categories provided in CMR. In addition,
our analysis comprises a 56-day lockdown period for each locality and COVID-19
wave, which we analyze both as 56-day periods and as 14-day consecutive
windows. Results vary considerably as a function of the locality considered,
particularly when the temporal evolution of the mobility reduction is
considered. We thus discuss each locality individually, relating social
distancing measures and the reduction observed.
