Analyst, Developer, Hacker, Parser, Innovator
Hello everyone! Recently I analyzed all locations from Instagram and Facebook to find out the most photographed places in Paris. I also managed to get the data about the expensiveness and popularity during the day. Below you can find my report with a bunch of sexy maps and insightful tables. Enjoy 😉.
Image: Most photographed places in Paris and regions.
N.B. "This is my first article for Hackernoon and I just found out that I can't attach the descriptions to Images. Sorry for that."
Dear publishers: If you want to share images from the article, please provide a source link - my Twitter: https://twitter.com/danokhlopkov.
Image: The most popular & photographed places in Paris. Red color - a lot of photos, green - a little. You can find more details on the legend on the map.
The table below shows the most photographed places in Paris based on the number of uploaded Instagram posts with that Geotag (attached location).
The data was so dirty so I had to merge and remove some locations: couple of years ago anyone could create a new geotag. This resulted in having more than 15 different "Tour Eiffel" locations on Instagram.
More details about some columns:
I grouped all Instagram geotags by category to find the most photographed categories in Paris. The table below shows the total number of photos that were uploaded to Instagram since its creation.
Damn, Hackernoon! Please create a better way to embed pics!
For restaurants, cafes, parks and shopping centers I searched for Instagram posts from these locations. Then I collected the data about the publication time to visualize the activity during the day. In the video different categories are presented with different colors.
For some locations Facebook can provide the "expensiveness" value: $ (cheap), $$, $$$, and $$$$ (most expensive). I averaged this value for all locations to find the most expensive places in Paris.
Image: Average "expensiveness" for locations in Paris city center. The redder, the more expensive. Locations without that data were ignored.
If you look at the locations of expensive places (red dots), you will notice that there is always another red one nearby. Expensive/elite places tend to pile up!
I've calculated the popularity of each Category and drill it down by the Expensiveness of the location. Results:
Image: Places with Instagram stories (January 2021).
Image: Most likes places. I averaged like count on top media that were published on Instagram, purple - a few likes (less than 200), yellow - a lot (more than 1500). So now we know where to find some popular bloggers!
Image: Clusters of Paris restaurants & cafes. If you want something to eat - go to large red circles. The redder & larger - the more places can offer you food there.
I really like to collect data, sometimes even without a specific purpose. But this time I was sure that I would be able to pull out interesting insights.
Instagram provides the following data for each geo point:
At the same time, Instagram geotags that you can attach to posts are taken from Facebook's database of world locations. So if a location relates to some local business, such as a cafe, restaurant, shopping mall, etc. then Facebook can show more data about this place:
For simplicity, I assumed that Paris is located between latitudes 48.61 and 49.08 and longitudes 1.98 and 2.70 (the boundaries were chosen very roughly and almost randomly). Instagram Facebook geotagged 49,000 Instagram geotags within these boundaries, of which 32,000 were with data from Facebook.
That's it! Send the photos to your colleagues. I posted more beautiful visualizations on my Twitter https://twitter.com/danokhlopkov.
And what would you think if you had such data, for example, for the whole world? What kind of data-driven business would you build? Share your ideas— we will discuss them. 😉
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