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