Hackernoon logoMost Photographed Places in Paris [Facebook Data Analysis] by@okhlopkov

Most Photographed Places in Paris [Facebook Data Analysis]

Dan Okhlopkov Hacker Noon profile picture

@okhlopkovDan Okhlopkov

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 πŸ˜‰.

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."

So, the insights 🍿

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:

  1. Media Count β€” Total number of posts uploaded to Instagram with that Location
  2. Median Likes β€” the median number of likes of the "top photos" of this location (at the moment of parsing).
  3. Price Range - the number that shows how expensive this location is (values 1,2,3,4). Other values (-2,-1,0) are invalid.

Most Popular categories

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!

Paris check-ins during the day

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.

More about expensiveness

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!

The popularity of the categories vs. price

I've calculated the popularity of each Category and drill it down by the Expensiveness of the location. Results:

  • Empty cells mean that there are less than 5 locations in that category and with that price range
  • Total_geos - total number of locations that were analyzed in each row
  • Cell values - median of total number of Instagram posts with locations in current category and expensiveness level
  • Yellow just indicates the maximum value in a row - the most popular price in category.

Even more maps!

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.

About the data

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:

  1. The popularity of the place (how many photos were posted by tag)
  2. Popularity among bloggers (number of likes for top posts by geotag)
  3. How much is the place discussed (the number of comments under top posts)
  4. The popularity of the place today (if there is a Story / Reel)

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:

  1. A category (restaurant, gym, business center, city landmark)
  2. Expensiveness (values: $, $$, $ $ $ or $$$$, which are correlated to the average bill)
  3. Attached Instagram business account (number of followers, the average number of likes and comments under posts)
  4. Business contacts (phone, website, address, opening hours)

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 ideasβ€” we will discuss them. πŸ˜‰

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