How our collective efforts of fighting the virus 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.


Figure 1 illustrates the contiguous US population flows from March 12, 2020 to March 24, 2020. It’s clear that the intensity of population movement declines dramatically from March 12 to March 24. Note that only tweets geotagged at the place level (e.g., Columbia, SC) are used in the maps for privacy concerns.

Figure 1. Population flows within the contiguous US from March 12, 2020 to March 24, 2020. Each line indicates a user movement from day one to day two (the next day). The yellow end of the line denotes origin and the purple end denotes destination. Brightness indicates movement intensity (more people flow in and out). The origin (destination) location is derived as the mean center of a user’s all tweets posted on day one (two). Data are collected with Twitter API.
Check the interactive maps at
http://gis.cas.sc.edu/GeoAnalytics/COVID19.html .

Figure 2 shows the world population flows from March 12, 2020 to March 24, 2020. Similar to the US, the intensity of population movement declines over the past days, though the decline is not as clear as in the U.S. due to the small scale maps.

Figure 2. World population flows from March 12, 2020 to March 24, 2020.

Note that the users on each map only include those who posted tweets on the two consecutive days of that map. We observed a clear increase of the twitter users for both US and world, which is likely due to the fact that more people tweet during the crisis.

Let’s look at the movement from another perspective by calculating the average travel distance (in mile) of all user samples for each day. Figure 3 reveals that average travel distance for both US (left ) and the world (right ) dropped dramatically from March 8 to March 22 and started to see slight increase from April 8, though with fluctuations.

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Figure 3. Average daily travel distance (mile) in the US (left) and the world (right) since March 1, 2020

By comparing the population flows of a region during the pandemic to a baseline flow (e.g., the same week of the year before the pandemic), the impact can be revealed. Figure 4 and Figure 5 show the comparison of the population flows from 03/23 to 03/24 between 2019 and 2020 for Europe, Japan, and South Korea.

Figure 4. Comparing the Europe population flows from 03/23 to 03/24 between 2019 and 2020.
Figure 5. Comparing the Japan and South Korea population flows from 03/23 to 03/24 between 2019 and 2020.

An interactive spatial web portal is being developed to allow users to generate dynamic mobility maps at varying spatial and temporal resolutions. Figure 6, screenshots of the portal, shows the county level average travel distance on March 7, 2020 (left map) and April 11, 2020 (right map). Both days are Saturdays and the color ramps on each map have the same value groups so they are comparable. This again shows a significant decline in travel distance in a majority of counties.

Figure 6. County level average travel distance on Saturday March 7, 2020 (left map) and Saturday April 11, 2020 (right map)

Also derived from the web portal, Figure 9, 10, and 11 show the temporal trend of average travel distance for selected geographic areas at different geographic levels including county (Figure 7), state (Figure 8), and country (Figure 9) from January 1, 2020 to April 29, 2020. Travel distance drops are observed for all selected counties and states in the U.S. starting from around March 10.

Figure 7. Temporal trend of average travel distance for six counties in the U.S. from January 1, 2020 to April 29, 2020
Figure 8. Average travel distance (mile) for ten states in the U.S. from January 1, 2020 to April 29, 2020
Figure 9. Average domestic travel distance (mile) for eight countries from January 1, 2020 to April 29, 2020

To analyze the decline in a more quantitative way, Figure 10 shows the daily mobility change at the state level by comparing to the baseline mobility in January and February of 2020. The y-axis is the ratio (R) between each day’s average movement distance and the baseline distance. ( R > 1.0 indicates increase in average movement distance; R < 1.0 indicates decrease in average movement distance; R =1.0 indicates no change;).

Figure 10. State level normalized mobility index (NMI)

This study is significant not only because it shows the efforts of limiting our travels to contain the virus, but also because monitoring population flows within a region or between different places could help us gain better insights into the current and future infectious risk, as human movement is an important driver of geographic spread of COVID-19. Figure 11 shows the outgoing population flows from Italy to other parts of the world between 03/01/2020 and 03/11/2020.

Figure 11. Outgoing population flows from Italy to other parts of the world between March 1, 2020 and March 11, 2020

Our next step is to conduct further quantitative analysis and modeling to examine, for example, the aggregated county or community level population movement analysis, the compliance of the social distancing measures, and the effectiveness of the control measures in containing the spread of the virus. The analysis will be conducted at different geographic scales including country, state, and county.


All the population flow maps are captured from a web portal providing near real-time movement using twitter data (Figure 13). Try the interactive app here.

Figure 12. Interactive spatial web portal for population flow visualizations
http://gis.cas.sc.edu/GeoAnalytics/COVID19.html

The aggregated human mobility maps and time series are generated from another interactive spatial web portal (Figure 15). This web portal is currently being developed and tested.


Figure 13. Interactive spatial web portal for generating aggregated human mobility maps and time series

Updated on June 10 2020

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