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Varieties of mobility measures: Comparing survey and mobile phone data during the COVID-19 pandemic

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Human mobility has become a major variable of interest during the COVID-19 pandemic and central to policy decisions all around the world. To measure individual mobility, research relies on a variety of indicators that commonly stem from two main data sources: survey-self-reports and behavioral mobility data from mobile phones. However, little is known about how mobility from survey self-reports relates to popular mobility estimates using GSM and GPS data. Spanning March 2020 until April 2021, this study compares self-reported mobility from a panel survey in Austria to aggregated mobility estimates utilizing (i) GSM data and (ii) Google's GPS-based Community Mobility Reports. Our analyses show that correlations in mobility changes over time are high, both in general and when comparing subgroups by age, gender, and mobility category. However, while these trends are similar, the size of relative mobility changes over time differs substantially between different mobility estimates. Overall, while our findings suggest that these mobility estimates manage to capture similar latent variables, especially when focusing on changes in mobility over time, researchers should be aware of the specific form of mobility different data sources capture.
Title: Varieties of mobility measures: Comparing survey and mobile phone data during the COVID-19 pandemic
Description:
Human mobility has become a major variable of interest during the COVID-19 pandemic and central to policy decisions all around the world.
To measure individual mobility, research relies on a variety of indicators that commonly stem from two main data sources: survey-self-reports and behavioral mobility data from mobile phones.
However, little is known about how mobility from survey self-reports relates to popular mobility estimates using GSM and GPS data.
Spanning March 2020 until April 2021, this study compares self-reported mobility from a panel survey in Austria to aggregated mobility estimates utilizing (i) GSM data and (ii) Google's GPS-based Community Mobility Reports.
Our analyses show that correlations in mobility changes over time are high, both in general and when comparing subgroups by age, gender, and mobility category.
However, while these trends are similar, the size of relative mobility changes over time differs substantially between different mobility estimates.
Overall, while our findings suggest that these mobility estimates manage to capture similar latent variables, especially when focusing on changes in mobility over time, researchers should be aware of the specific form of mobility different data sources capture.

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