A world drawn with TripHappy user travel itineraries. Beautiful data visualization.

The World, Drawn with Travel Itineraries Analytics from 17,000+ Itineraries

6 minute read

Over the last two years, travelers have created over 17,000 itineraries in 184 countries using our interactive trip planner. The TripHappy team is a big fan of maps, so we thought it would be a fun exercise to try and create our own using only the itineraries submitted to our site.

This map was created without land, sea, or geographic borders. It’s what the world looks like when created by travelers. Each circle is a stop on an itinerary, and each stop in a trip is connected by a line. The countries are clustered together based on how connected they are between itineraries. This means that similarly colored countries are visited during the same trip more often than countries of different colors. Pretty cool, huh?

The results tell an interesting story, but if you’d rather skip to the details on how the graphics are made click here.

Overall Stats

The average number of stops a per trip

Average # of Stops Per Trip: 10 Cities

The average length of a user trip

Average Length of Trip: 12 days

The average distance traveled per trip

Average Distance Traveled: 2,223 km / 1,381 miles

* The average above is calculated as the median. Only 8% of users added dates to their trip.

Most Frequently Visited Countries

  1. United States
  2. China
  3. Japan
  4. India
  5. Thailand
  6. Italy
  7. Turkey
  8. Spain
  9. Indonesia
  10. Vietnam

We counted the number of times a country appeared at least once in a trip. The United States took the top spot here, potentially because most of our users are from the United States and because there are proportionally more domestic trips within our itinerary dataset.

Most Frequently Visited Cities

  1. Tokyo
  2. Kyoto
  3. Bangkok
  4. Delhi
  5. Osaka
  6. Cusco
  7. Beijing
  8. Istanbul
  9. Lima
  10. Rome

Japanese cities earn 3 of the top 5 slots. Interestingly, no cities in the United States are present in the top 10, despite being the most frequently traveled country. Another surprise is seeing 2 cities in Peru on the list, even though Peru isn’t in the top 10 most popular countries.

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Most Connected Countries

  1. Hong Kong to China
  2. Brazil to Argentina
  3. Tanzania to Kenya
  4. Macau to Hong Kong
  5. Macau to China
  6. Netherlands to Germany
  7. United Kingdom to France
  8. United States to Canada
  9. Uganda to Congo
  10. Germany to Czech Republic
  11. Thailand to Cambodia
  12. France to Spain
  13. Vatican City to Italy
  14. Turkey to Greece
  15. Germany to Austria

All of the itinerary connections were clustered together based on their similarity and assigned a color based on their cluster group. So any country with the same color has many itineraries connecting those countries together. Using this cluster information, we then produced a secondary visual showing the strength of the connection between each country. Here, the thicker the line between two countries, the more user itineraries went between them, in either direction.

For the most part, countries ended up clustered to its neighboring countries. For example, all of South America is grouped into one cluster (with Panama, too!) But there are some countries that are unexpectedly clustered. Cyprus and the UAE are grouped together with South East Asia and Australia. One explanation is that Dubai is a big layover destination for travelers moving between Europe and Asia, so many travelers stop over there on the way. Similarly, Jamaica is grouped together with Western Europe and not North America nor the other Caribbean islands.

Making the Visualizations

The main visual was made using Gephi, a free and open-source program for data visualizations. Each city is plotted with its (lat, lng) coordinates and connected to any other city that came after it in someone’s itinerary. For example, London is connected to Paris because there’s at least one itinerary going from the former to the latter. The cities were colored based on the cluster grouping from the second visualization.

The interactive visuals were created using Tableau Public. The first sheet, Trips by City, is a symbol map with lat/lng as geographic dimensions and review count as the size and color measures. The second sheet, Trips by Country, is a filled map and the same dimensions and measures.

Datasets

Tools Used in Analysis

Special Thanks

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