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.
A multi-objective time series analysis of community mobility reduction comparing first and second COVID-19 waves