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Social distancing (SD) has been critical in the fight against the novelcoronavirus disease (COVID-19). To aid SD monitoring, many technology companieshave made available mobility data, the most prominent example being thecommunity mobility reports (CMR) provided by Google. Given the wide range ofresearch fields that have been drawing insights from CMR data, there has been arising concern for methodological discussion on how to use them. Indeed, Googlerecently released their own guidelines, concerning the nature of the placecategories and the need for calibrating regional values. In this work, wediscuss how measures developed in the field of multi-criteria decision making(MCDM) might benefit researchers analyzing this data. Concretely, we discusshow Pareto dominance and performance measures adopted in MCDM enable themobility evaluation for (i) multiple categories for a given time period and(ii) multiple categories over multiple time periods. We empirically demonstratethese approaches conducting both a region- and country-level analysis,comparing some of the most relevant outbreak examples from differentcontinents.

Google COVID-19 community mobility reports: insights from multi-criteria decision making