Socioeconomic data is as valuable as it is difficult to come by. What if we can use existing data about restaurants to predict socioeconomic factors? We built a model that takes in restaurant data for 9 Chinese cities from Dianping, the Chinese equivalent to Yelp. This data ranges from cuisine type to restaurant ratings.

Using this data, we can predict factors like daytime population, nighttime population, company presence, and spending amount. These predictions come at more granular scale, both in time and space, than traditional census data. Below, we show predictions for 3km2 grid cells and compare the results and accuracy across the studied cities.

Beijing Predictions

# restaurants

restaurant diversity


Daytime Population

Nighttime Population

Number of Companies


Comparing Factors Across Cities

Select the cities in the map to filter the paths in the chart.

# restaurants

predicted totals

prediction accuracy


Siqi Zheng Director, MIT China Future City Lab
Carlo Ratti Director, MIT Senseable City Lab
Lei Dong Lead Researcher
Fábio Duarte Design Manager
Sarah Campbell Web, Visualization


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For further


Predicting neighborhoods’ socioeconomic attributes using restaurant data

Lei Dong, Carlo Ratti, and Siqi Zheng, 2019
Proceedings of the National Academy of Sciences of the United States of America

Support: National Science Foundation of China and MITOR