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With the logistic challenges faced by most countries for the production,distribution, and application of vaccines for the novel coronavirusdisease~(COVID-19), social distancing~(SD) remains the most tangible approachto mitigate the spread of the virus. To assist SD monitoring, several techcompanies have made publicly available anonymized mobility data. In this work,we conduct a multi-objective mobility reduction rate comparison between thefirst and second COVID-19 waves in several localities from America and Europeusing Google community mobility reports~(CMR) data. Through multi-dimensionalvisualization, we are able to compare in a Pareto-compliant way the reductionin 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-19wave, which we analyze both as 56-day periods and as 14-day consecutivewindows. Results vary considerably as a function of the locality considered,particularly when the temporal evolution of the mobility reduction isconsidered. We thus discuss each locality individually, relating socialdistancing measures and the reduction observed.

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