Novel mobility index tracks COVID-19 transmission following stay-at-home orders

Change of urban park usage as a response to the COVID-19 global pandemic

Urban parks became critical for maintaining the well-being of urban residents during the COVID-19 global pandemic. To examine the impact of COVID-19 on urban park usage, we selected New York City (NYC) and used SafeGraph mobility data, which was collected from a large sample of mobile phone users, to assess the change in park visits and travel distance to a park based on 1) park type, 2) the income level of the visitor census block group (visitor CBG) and 3) that of the park census block group (park CBG). All analyses were adjusted for the impact of temperature on park visitation, and we focused primarily on visits made by NYC residents. Overall, for the eight most popular park types in NYC, visits dropped by 49.2% from 2019 to 2020. The peak reduction in visits occurred in April 2020. Visits to all park types, excluding Nature Areas, decreased from March to December 2020 as compared to 2019. Parks located in higher-income CBGs tended to have lower reductions in visits, with this pattern being primarily driven by large parks, including Flagship Parks, Community Parks and Nature Areas. All types of parks saw significant decreases in distance traveled to visit them, with the exception of the Jointly Operated Playground, Playground, and Nature Area park types. Visitors originating from lower-income CBGs traveled shorter distances to parks and had less reduction in travel distances compared to those from higher-income CBGs. Furthermore, both before and during the pandemic, people tended to travel a greater distance to parks located in high-income CBGs compared to those in low-income CBGs. Finally, multiple types of parks proved crucial destinations for NYC residents during the pandemic. This included Nature Areas to which the visits remained stable, along with Recreation Field/Courts which had relatively small decreases in visits, especially for lower-income communities. Results from this study can support future park planning by shedding light on the different uses of certain park types before and during a global crisis, when access to these facilities can help alleviate the human well-being consequences of "lockdown" policies.

Convergence in Mobility Data Sets From Apple, Google, and Meta

BACKGROUND

The 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.

OBJECTIVE

This 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.

METHODS

In 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.

RESULTS

Our 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.

CONCLUSIONS

Our 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.

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

Our exploratory research focuses on the possible relations between tourism and the mobility of people, using short longitudinal data for mobility dimensions during the COVID-19 pandemic. One of these is real-time, exhaustive type data, published by Google, about the mobility of people in six different dimensions, (retail, parks, residential, workplace, grocery, and transit). The aim is to analyze the directional, intensity, causal, and complex interplay between the statistical data of tourism and mobility data for Romanian counties. The main objective is to determine if real-world big data can be linked with tourism arrivals in the first 14 months of the pandemic. We have found, using correlations, factorial analysis (PCA), regression models, and SEM, that there are strong and/or medium relationships between retail and parks and overnights, and weak or no relations between other mobility dimensions (workplace, transit). By applying factorial analysis (PCA), we have regrouped the six Google Mobility dimensions into two new factors that are good predictors for Romanian tourism at the county location. These findings can help provide a better understanding of the relationship between the real movement of people in different urban areas and the tourism phenomenon: the GM parks dimension best predicts tourism indicators (overnights), the GM residential dimension correlates inversely with the tourism indicator, and the rest of the GM indices are generally weak predictors for tourism. A more complex analysis could signal the potential and the character of tourism in different destinations, by territorially and chronologically determining the GM indices that are better linked with the tourism statistical indicators. Further research is required to establish forecasting models using Google Mobility data.