Analyzing the 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

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google community mobility reports, 3) mobility data from Descartes Labs, and 4) Twitter mobility calculated via weighted distance. We find statistically significant positive correlations in the  between either two data sources, revealing their general similarity, albeit with varying Pearson’s  coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The positive correlation between RI and income at the county level is significant in all mobility datasets, suggesting that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. To our best knowledge, this is the first study that cross-compares multi-source mobility datasets. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.

Human Mobility, Policy, and COVID-19: A Preliminary Analysis of South Carolina

Using geotagged Twitter data as the mobility data source and South Carolina as the case study, we present some preliminary findings and visualizations on population flows and human mobility changes during the pandemic at state level and county level. The potential associations between human mobility, state policies, and COVID-19 cases are also examined.

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How our collective efforts of fighting COVID-19 are reflected on maps?

The whole world is now fighting the coronavirus (COVID-19). Social/physical distancing and limiting travel are effective approaches to contain the virus. Everyone’s effort counts. By analyzing world population flows through the lens of geotagged Twitter data during the COVID-19 Pandemic, this article (story map) showcases how our collective efforts of fighting the virus are reflected on maps and how big social media data can be used for such analyses.

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Another interactive web app for aggregated population flows and statistics is being developed and tested.
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