Visualizing pandemics at different spatial scales


In early 2020, the world as we know it seemingly came to a stop as the coronavirus disease 2019 (COVID-19) created a worldwide pandemic. Consequent social restrictions, quarantines, and other governmental policies caused an unprecedented alteration in the typical mobility patterns of humans. Inversely, human mobility patterns can also influence patterns of the virus.

But how exactly are COVID-19 and human mobility related? More specifically, what relationship existed between human mobility and COVID-19 in the months following initial quarantine? How has that relationship changed over time? Does the relationship vary across different places? Is the relationship different at the country, state, or county levels? How might governmental policies affect the relationship? These are all questions we pondered as we began exploration of the topic.

We started by looking at an interesting data set released by Apple Maps in light of the pandemic. The data set displays daily requests for driving, walking, and transit directions in Apple Maps for a given location, shown as a percent change since January 13, 2020, and available at the country level worldwide or the state or county levels for the United States. For each geography, we then sought to compare these data to daily COVID-19 case data provided by the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University, as well as to COVID-19-related policy data from the Oxford COVID-19 Government Response Tracker.

But we soon realized that the relationship between these data sets varied vastly across different geographies and spatio-temporal scales, making it extremely difficult to appropriately summarize the relationship between human mobility and COVID-19 at a macro level. One solution we considered was to simply focus our study on just one or a few specific locations. But with no strong research preference toward any particular location and a deliberate desire to keep our research broad considering the worldwide impacts of the pandemic, we declined such an option.

At this point, we became inspired by the Johns Hopkins COVID-19 Dashboard, which efficiently displays vast amounts of useful COVID-19 data while allowing users to query for the information they seek. We realized that creating a similar tool to represent our data could be the most effective way to show the relationship between COVID-19 and human mobility.

After several weeks of coding, we launched our own dashboard as a web application. The application allows users to select a country at the global scale or a state or county for the United States and then displays a corresponding plot that compares human mobility to COVID-19 cases across time for the location, as well as to policy data. Since initial launch, we have continued to update the application with appropriate data at regular intervals.

The web application produces interesting visualizations that can reveal interesting trends specific to a given area that might otherwise not be recognized. For example, a look at the application’s plot for New Orleans clearly shows a spike in mobility at the end of February, 2020 as a result of the city’s celebration of Mardi Gras followed by a corresponding spike in COVID-19 cases around a month later. Further analysis would be needed before any sort of causation can be confirmed, but our tool is nonetheless a great first step in uncovering relationships.

Similarly, it is also interesting to observe the patterns of COVID-19 cases in India since it started last year. It is particularly interesting to note the relatively low numbers during the summer of 2020, when the rest of the world was experiencing record surges. Lately the patterns have reversed with record numbers in India and neighboring countries, while many countries in the western hemisphere are starting to open back. 

Thus, we hope that our application helps highlight the usefulness of data visualization as a tool for discovering patterns that might otherwise go unnoticed, thereby helping to inform decision making or raise curiosity for further investigation. If nothing else, it can serve as an exciting example of what high-level conveyance of information can be achieved through interactive data visualization. More detailed information about our web application is available in this recently published paper.

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Go to the profile of Saumitra Mukherjee
over 2 years ago

The Pandemics spread due to a lack of understanding by mass and implementation of different social groups in different times. There are examples of past pandemics like Spanish flu and the migration of people was very low in comparison to the present day. However, the reason for the spread of pandemics in India is not clear to date otherwise it would have been controlled. 

Go to the profile of Shouraseni Roy
over 2 years ago

Thank you for your comment. The purpose of the spatial web application is to explore the connection between human mobility and government policies on COVID 19 incidence. As evident from the analysis these relationships change over space and time, which is constantly evolving with time.