Convergence in Mobility Data Sets From Apple, Google, and Meta
BackgroundThe higher movement of people was one of the variables that contributed to the spread of the infectious agent SARS-CoV-2 during the COVID-19 pandemic. Governments worldwide responded to the virus by implementing measures that would restrict people’s movements, and c...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-06-01
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| Series: | JMIR Public Health and Surveillance |
| Online Access: | https://publichealth.jmir.org/2023/1/e44286 |
| _version_ | 1827857251803594752 |
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| author | Gustavo Sganzerla Martinez David J Kelvin |
| author_facet | Gustavo Sganzerla Martinez David J Kelvin |
| author_sort | Gustavo Sganzerla Martinez |
| collection | DOAJ |
| description |
BackgroundThe higher movement of people was one of the variables that contributed to the spread of the infectious agent SARS-CoV-2 during the COVID-19 pandemic. Governments worldwide responded to the virus by implementing measures that would restrict people’s movements, and consequently, the spread of the disease. During the onset of the pandemic, the technology companies Apple, Google, and Meta used their infrastructure to anonymously gather mobility reports from their users.
ObjectiveThis study aims to compare mobility data reports collected by Apple, Google, and Meta (formerly Facebook) during the COVID-19 pandemic and a major winter storm in Texas in 2021. We aim to explore the hypothesis that different people exhibit similar mobility trends during dramatic events and to emphasize the importance of this type of data for public health measures. The study also aims to promote evidence for companies to continue releasing mobility trends data, given that all 3 companies have discontinued these services.
MethodsIn this study, we collected mobility data spanning from 2020 to 2022 from 3 major tech companies: Apple, Google, and Meta. Our analysis focused on 58 countries that are common to all 3 databases, enabling us to conduct a comprehensive global-scale analysis. By using the winter storm that occurred in Texas in 20201 as a benchmark, we were able to assess the robustness of the mobility data obtained from the 3 companies and ensure the integrity of our findings.
ResultsOur study revealed convergence in the mobility trends observed across different companies during the onset of significant disasters, such as the first year of the COVID-19 pandemic and the winter storm that impacted Texas in 2021. Specifically, we observed strong positive correlations (r=0.96) in the mobility data collected from different tech companies during the first year of the pandemic. Furthermore, our analysis of mobility data during the 2021 winter storm in Texas showed a similar convergence of trends. Additionally, we found that periods of stay-at-home orders were reflected in the data, with record-low mobility and record-high stay-at-home figures.
ConclusionsOur findings provide valuable insights into the ways in which major disruptive events can impact patterns of human mobility; moreover, the convergence of data across distinct methodologies highlights the potential value of leveraging mobility data from multiple sources for informing public health decision-making. Therefore, we conclude that the use of mobility data is an asset for health authorities to consider during natural disasters, as we determined that the data sets from 3 companies yielded convergent mobility patterns. Comparatively, data obtained from a single source would be limited, and therefore, more difficult to interpret, requiring careful analysis. |
| first_indexed | 2024-03-12T12:37:45Z |
| format | Article |
| id | doaj.art-d32f1ab39f4b42838aeec59491e33c38 |
| institution | Directory Open Access Journal |
| issn | 2369-2960 |
| language | English |
| last_indexed | 2024-03-12T12:37:45Z |
| publishDate | 2023-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Public Health and Surveillance |
| spelling | doaj.art-d32f1ab39f4b42838aeec59491e33c382023-08-29T00:05:47ZengJMIR PublicationsJMIR Public Health and Surveillance2369-29602023-06-019e4428610.2196/44286Convergence in Mobility Data Sets From Apple, Google, and MetaGustavo Sganzerla Martinezhttps://orcid.org/0000-0002-7656-0579David J Kelvinhttps://orcid.org/0000-0001-7757-9493 BackgroundThe higher movement of people was one of the variables that contributed to the spread of the infectious agent SARS-CoV-2 during the COVID-19 pandemic. Governments worldwide responded to the virus by implementing measures that would restrict people’s movements, and consequently, the spread of the disease. During the onset of the pandemic, the technology companies Apple, Google, and Meta used their infrastructure to anonymously gather mobility reports from their users. ObjectiveThis study aims to compare mobility data reports collected by Apple, Google, and Meta (formerly Facebook) during the COVID-19 pandemic and a major winter storm in Texas in 2021. We aim to explore the hypothesis that different people exhibit similar mobility trends during dramatic events and to emphasize the importance of this type of data for public health measures. The study also aims to promote evidence for companies to continue releasing mobility trends data, given that all 3 companies have discontinued these services. MethodsIn this study, we collected mobility data spanning from 2020 to 2022 from 3 major tech companies: Apple, Google, and Meta. Our analysis focused on 58 countries that are common to all 3 databases, enabling us to conduct a comprehensive global-scale analysis. By using the winter storm that occurred in Texas in 20201 as a benchmark, we were able to assess the robustness of the mobility data obtained from the 3 companies and ensure the integrity of our findings. ResultsOur study revealed convergence in the mobility trends observed across different companies during the onset of significant disasters, such as the first year of the COVID-19 pandemic and the winter storm that impacted Texas in 2021. Specifically, we observed strong positive correlations (r=0.96) in the mobility data collected from different tech companies during the first year of the pandemic. Furthermore, our analysis of mobility data during the 2021 winter storm in Texas showed a similar convergence of trends. Additionally, we found that periods of stay-at-home orders were reflected in the data, with record-low mobility and record-high stay-at-home figures. ConclusionsOur findings provide valuable insights into the ways in which major disruptive events can impact patterns of human mobility; moreover, the convergence of data across distinct methodologies highlights the potential value of leveraging mobility data from multiple sources for informing public health decision-making. Therefore, we conclude that the use of mobility data is an asset for health authorities to consider during natural disasters, as we determined that the data sets from 3 companies yielded convergent mobility patterns. Comparatively, data obtained from a single source would be limited, and therefore, more difficult to interpret, requiring careful analysis.https://publichealth.jmir.org/2023/1/e44286 |
| spellingShingle | Gustavo Sganzerla Martinez David J Kelvin Convergence in Mobility Data Sets From Apple, Google, and Meta JMIR Public Health and Surveillance |
| title | Convergence in Mobility Data Sets From Apple, Google, and Meta |
| title_full | Convergence in Mobility Data Sets From Apple, Google, and Meta |
| title_fullStr | Convergence in Mobility Data Sets From Apple, Google, and Meta |
| title_full_unstemmed | Convergence in Mobility Data Sets From Apple, Google, and Meta |
| title_short | Convergence in Mobility Data Sets From Apple, Google, and Meta |
| title_sort | convergence in mobility data sets from apple google and meta |
| url | https://publichealth.jmir.org/2023/1/e44286 |
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