Analyzing Google Maps' Algorithmic Impact on London Restaurant Visibility and Survival
By
justincormack
The kind of bagel that ruins lesser bagels for you.
Summary
A data scientist scraped over 13,000 restaurants in Greater London and built a machine learning model to analyze Google Maps' algorithmic recommendations. What began as a quest for better dinner recommendations evolved into a study of how digital platforms like Google Maps quietly redistribute economic survival across cities by controlling restaurant visibility. The project reveals how algorithmic curation affects which restaurants succeed or fail, examining the power dynamics behind digital platform recommendations.
Key quotes
· 5 pulledI was tired of doom-scrolling Google Maps, trying to disentangle genuinely good food from whatever the algorithm had decided to push at me that day.
Somewhere along the way, the project stopped being about dinner and became about something slightly more unhinged: how digital platforms quietly redistribute economic survival across cities.
Using 13000+ restaurants, I rebuild its ratings with machine learning and map how algorithmic visibility actually distributes power.
I wanted a dinner recommendation and got a research agenda instead.
Because once you start looking at London's restaurants through data, you see how algorithmic curation affects which businesses survive.
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