How our collective efforts of fighting the virus are reflected on maps?

This page is updated periodically as new data comes in.


The whole world is now fighting the coronavirus (COVID-19). Social distancing and limiting travel are effective approaches to contain the virus. Everyone’s effort counts. Let’s see how our collective efforts are reflected on the maps and numbers.


The maps below illustrate the contiguous US population flows through the lens of twitter data from March 12, 2020 to March 24, 2020. It’s clear that the intensity of population movement declines gradually over the past days.



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. Only tweets geotagged at the place level (e.g., Columbia, SC) are used in the maps for privacy concerns.

To view the the interactive and more updated maps, go to
http://gis.cas.sc.edu/GeoAnalytics/COVID19.html .

The maps below show 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.


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

To view the the interactive and more updated maps, go to
http://gis.cas.sc.edu/GeoAnalytics/COVID19.html .

Note that the users on each map only include those who posted tweets on the two consecutive days of that map. The average daily twitter user samples within the contiguous US is around 152,000 and for the whole world is around 484,000. So the declining population movement intensity is not due to the declining of the twitter user samples. Instead, we observe 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.

The following maps show the comparison of the population flows from 03/23 to 03/24 between 2019 and 2020 for Europe, Japan, and South Korea.

Comparing the Europe population flows from 03/23 to 03/24 between 2019 and 2020.

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

The map below shows the short distance travels (less than 450 miles) from 03/24 to 03/25 in 2019 (left) and 2020 (right).

Short distance travels (less than 450 miles)
from 03/24 to 03/25 in 2019 (left) and 2020 (right)

To view the the interactive and more updated maps, go to
http://gis.cas.sc.edu/GeoAnalytics/COVID19.html .

The map below shows the population outflows from New York City to other states between March 20 and March 30, 2020

Outflows from New York City to other states between March 20 and March 30, 2020

Let’s look at the movement from another perspective by calculating the average travel distance (in mile) of all user samples for each day. The figure below clearly indicates that the average travel distance for both US (left ) and the world (right ) declines since March 8, though with fluctuations.

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

To explore the patterns in a finer spatial scale, the average travel distance for each state of the contiguous U.S. is calculated from Tuesday to Wednesday of the four weeks of March 2020. The four maps below show the declines of the travel distance for a majority of the states.

Average travel distance for each state of the contiguous U.S. of the four weeks of March, 2020

To check the decline in a more quantitative way, the map below shows the percentage of change when comparing average travel distance of Week 4 (03/24 → 03/25) with Week 1 (03/03 → 03/04). As can be seen, except Idaho all states shows drops of average travel distance. 35 states (including DC) have over 50 percent of distance drops. 24 states have over 60 percent of drops.

The percentage change of the state-level average travel distance
from Week 4 to Week 1, March 2020.

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. The figure below shows the association between the number of twitter users traveled from Italy between 03/01 and 03/11, and the number of confirmed cases for each state as of 03/12/2020. Washington state is not included as it is considered as an outlier (has a different situation) among other states at the early stage of the crisis. It’s clear that there is a strong positive correlation between the number of incoming users and number of confirmed cases (R-square 0.828).

Association between the number of twitter users from Italy between 03/01 and 03/11, and the number of confirmed cases for each state as of 03/12/2020.

We plan to conduct further analysis 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 app providing near real-time movement using twitter data. New maps will be updated on a daily basis during the crisis as new data comes in. Try the interactive app here.


http://gis.cas.sc.edu/GeoAnalytics/COVID19.html


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