City Stats

I've always wondered about the true shape of cities – and one of the best ways to reveal it is by mapping where people eat, drink, and gather. To me these clusters light up like galaxies, showing where real urban life orbits. Some Latin American and Asian cities couldn’t be included due to limited OpenStreetMap coverage, though the OSM community still did an incredible job overall. After analyzing 80 global cities and mapping millions of restaurants, cafés, bars, and clubs, some clear patterns emerged: 🗺️ PARIS leads density with 183 venues per km² 🍺 MADRID has the most nightlife-focused scene ☕ Former Soviet cities dominate café culture (St. Petersburg 47%, Kyiv 44%, Moscow 40%) 🍽️ SEOUL perfects fine dining with 19 restaurants per fast food place. I first built this tool for my own travels. When visiting a new city, I map the dense activity zones to find vibrant neighborhoods for meeting locals - then pick quieter café clusters to work remotely. To detect nightlife and café hotspots, I used DBSCAN clustering, which automatically identifies where venues naturally group together. Some cities (like Berlin) were surprisingly tricky - the algorithm struggled to find clusters, which matches how scattered Berlin feels in real life. The cultural insights: - Post-Soviet cities (Petersburg, Kyiv, Tashkent, Tbilisi) are café-dominated landscapes - Muslim-majority cities prioritize food over alcohol (Dubai, Istanbul top the food-focused rankings) - Asian cities excel at restaurant-to-fast-food balance - European capitals lead in nightlife density Each city has its own "food fingerprint" that reflects its culture and urban development patterns. 📊 Full analysis covers 80+ cities with: Density rankings and cultural ratios Machine learning cluster detection Neighborhood hotspot mapping Where do you go to feel the pulse of your city? (I can share the GitHub repo with code and data sources in the comments if anyone’s curious.) Some ranks below: 🏙️ HIGHEST FOOD DENSITY (venues per km²)  1. Paris         182.9  2. Tokyo         117.1  3. New York        82.3  4. Barcelona       78.0  5. Madrid         75.6  6. London         69.9  7. Milan         59.1  8. Berlin         58.6  9. Moscow         56.1 10. Seoul         55.0 🍺 MOST NIGHTLIFE-FOCUSED (nightlife venues per dining venue)  1. Madrid         0.34  2. Tokyo         0.30  3. Glasgow        0.30  4. Manchester       0.28  5. Edinburgh       0.26  6. Valencia        0.25  7. San Diego       0.25  8. Riga          0.24  9. Stuttgart       0.24 10. Barcelona       0.23 🍕 MOST FOOD-FOCUSED (total dining venues per drinking venue)  1. Dubai         15.22  2. Hong Kong       13.80  3. Seoul         10.96  4. Kuala Lumpur      9.56  5. Los Angeles      9.25  6. Santiago        8.65  7. Istanbul        8.64  8. Vancouver       8.44  9. Tashkent        7.90 10. Boston         7.21 #UrbanPlanning #DataVisualization #OpenStreetMap #MachineLearning #Travel