The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic
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SciScore for 10.1101/2020.07.31.20143016: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The COVID-19 pandemic, an event with dramatic mobility changes on a large scale, provides a great opportunity for us to learn the strengths and weaknesses of each data source, laying the foundation for the potential multi-source integration. Third, given that county is the smallest geographical unit from Apple, Descartes, and Google …
SciScore for 10.1101/2020.07.31.20143016: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The COVID-19 pandemic, an event with dramatic mobility changes on a large scale, provides a great opportunity for us to learn the strengths and weaknesses of each data source, laying the foundation for the potential multi-source integration. Third, given that county is the smallest geographical unit from Apple, Descartes, and Google mobility datasets, we explore the correlation between county-level income and county-level mobility-based responsiveness. However, changes in aggregated geographic units (e.g., from counties to Census tracts) might alter the conclusions due to the famous Modifiable Areal Unit Problem (MAUP) (Fotheringham and Wong, 1991). Future studies should investigate the disparity in mobility at various spatial units or scales. Although income is one of the fundamental factors in socioeconomic status, other factors may also contribute to the disparity in responsiveness revealed in this study, therefore deserve further investigation. Finally, the spatial incontinuity of the available counties in all four mobility datasets precludes a detailed spatial examination. However, we acknowledge that spatial non-stationarity (regional variation) may exist in the contribution of socioeconomic factors to the mobility dynamics. Future studies can consider spatial autocorrelation when using other spatially continuous mobility datasets.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 26 and 11. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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