4 min read
‧ 4 min read

We’ve been dreaming of one last trip before the end of 2022. There’s a million places to explore, each with their own set of must-see attractions. So we decided to dive into the data to see if we could find the perfect itinerary.
Check out our data guide to travel dashboard, or read on to learn what we’ve found.
For this exploration, we worked with data scraped from Google Maps to find the top-rated attractions in the world’s most visited destinations.
We took the Top 100 City Destinations in 2019 from Euromonitor International’s city arrivals research. To account for the pandemic completely skewing travel data, we opted to analyze travel stats for 2019.
We then took the top ten destinations and used phantombuster to scrape attractions data from Google Maps for those destinations.
As usual, the raw data needed some cleaning before we could analyze it. We had to transform the data and convert it into models in Metabase. And of course this travel data is affected by where people are traveling from: the global distribution of people, as well as economic factors - not everyone has the means to travel - will affect where people travel to. For example, given that about 40% of us live in Asia, we’d expect to find travel destinations in Asia seeing a lot of traffic.
The most visited destinations in 2019 were:
Bangkok, Macau, and Singapore were each seeing over 5% year-over-year growth in number of visitors. Hong Kong saw the largest drop in visitors in 2019 at -8.7%. Visitors to London remained pretty stable (cheers).
We then grouped the top 100 destinations into regions:
Based on the top ten destinations, we went on to scrape attractions data from Google Maps to find interesting places we could visit. We wanted to see if there were any significant differences in the number of contributors and ratings in these destinations.
Then we wanted to see which types of attractions we can visit in each of these places. For example:
We also look at the type of attraction that received the highest rating in each destinations.
✈️ We hope this data guide to travel will help inspire your next trip! Feel free to download the cleaned-up data as CSV, JSON, or XLSX to explore the data on your own (click on the links below and look for the download button in the bottom right).