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

TL;DR

This paper explores how multi-criteria decision making techniques can enhance the analysis of Google COVID-19 community mobility reports, providing a structured approach to evaluate mobility changes across regions and time periods.

Contribution

It introduces MCDM methods like Pareto dominance to improve the interpretation of mobility data in COVID-19 social distancing studies.

Findings

MCDM methods facilitate multi-category mobility analysis.

Pareto dominance helps identify significant mobility changes.

Empirical case studies demonstrate the approach's effectiveness.

Abstract

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.