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How to explore the globe

Data sources

Travel restriction data is sourced from the Humanitarian Data Exchange's COVID-19 Global Travel Restrictions and Airline Information. This data set is an effort by the World Food Program to gather information from various sources: IATA, media, national sources, WFP internal or any other.

Covid-19 cases data is sourced from COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.

Both data sources are used under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Data is updated daily. Due to the complexity and rapidly changing nature of travel regulations, we cannot guarantee the accuracy or completeness of the data. This visualization should not be used to make any travel decisions, and it is up to you to verify restrictions with official, authorized sources before you travel.

Methodology and assumptions

We built Python scripts and used Natural Language Processing in order to adapt to the daily changing data structure.

To determine which places and nationalities were banned from entering a specific country, we used Natural Language Processing on the info column to look for country names, country abbreviations, regions, and persons associated with the terms ban, suspend, stop, halt, block, and bar, and also made sure to exclude words like "lift" that are associated with the term ban. This means if a country or place is not directly named as being banned in the info column, it is not represented in our dataset. Then, we used the resulting countries to determine outbound travel bans. For example, if the United States data said "Banned all travelers from the United Kingdom," then the United States would have an inbound ban list including the United Kingdom. Inversely, the United Kingdom would have an outbound ban list including the United States. If the data for the United States said "Banned all travelers from Europe," then the United States would show an inbound ban on Europe. However, all countries in Europe would not show an outbound ban to the United States. We made the decision to not code for outbound bans on regions/areas due to the complexity of the restrictions and the limited occurrence in the data.

For the latest news and resource links of each country, we took the text directly from the respective columns.

To determine if a negative test was required for entry, we used the first sentence from the optional3 column. Then, we standardized the sentence structure to produce three categories (negative certification required, negative certification not required, negative certification subject to special conditions).

To determine if quarantine or self-isolation was required upon entry, we used the first sentence from the optional2 column. Then, we standardized the sentence structure to determine what was required (quarantine, self-isolation, self-monitoring, nothing) and where (government site or own accommodation).

To determine which countries are currently in lockdown, we used Natural Language Processing on the info column to look for the words lockdown in association with the word extend, put, impose, national, or nationwide. Lockdowns can occur on an entire country or specific parts. If any type of lockdown was in place, we marked the country as in lockdown.

To determine which countries have closed borders, we used Natural Language Processing on the info column to look for the word borders associated with close, closed, block, or closure. Lockdowns can occur on land, sea, and air borders. If any type of border closure was in place, we marked the country as having border closures.

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