The rapid spread of the virus at a global scale highlights the need for a more harmonized approach to monitoring the human mobility that has proven to be associated with viral transmission. We analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.
The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing under COVID-19
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. mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The results suggest that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. 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.
Track the Effects of the COVID-19 Pandemic on Spatiotemporal Patterns of National Park Visitation
Effective quantification of visitation is important for understanding the many impacts of the COVID-19 pandemic on national parks and other protected areas. We mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively capture park visitation patterns but with much finer spatiotemporal granularity. our study highlights the capability of mobility data for providing spatiotemporally explicit knowledge of place visitation.
Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective
We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. We collected over 300,000 geotagged tweets in the United States and examined the spatiotemporal patterns of public sentiment and emotion overtime and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes.