Predicting Neighborhoods' Socioeconomic Attributes Using Restaurant Data

Posted: 11 Nov 2019

See all articles by Lei Dong

Lei Dong

Massachusetts Institute of Technology (MIT)

Carlo Ratti

affiliation not provided to SSRN

Siqi Zheng

Massachusetts Institute of Technology (MIT) - Center for Real Estate; Massachusetts Institute of Technology (MIT) - Department of Urban Studies & Planning; Hang Lung Center for Real Estate, Tsinghua University

Date Written: October 29, 2019

Abstract

Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute — restaurant — can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends.

Suggested Citation

Dong, Lei and Ratti, Carlo and Zheng, Siqi, Predicting Neighborhoods' Socioeconomic Attributes Using Restaurant Data (October 29, 2019). Available at SSRN: https://ssrn.com/abstract=3477355

Lei Dong

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Carlo Ratti

affiliation not provided to SSRN

Siqi Zheng (Contact Author)

Massachusetts Institute of Technology (MIT) - Center for Real Estate ( email )

Building 9-323
Cambridge, MA 02139
United States

HOME PAGE: http://https://siqizheng.mit.edu/

Massachusetts Institute of Technology (MIT) - Department of Urban Studies & Planning ( email )

77 Massachusetts Avenue
Cambridge, MA 02139
United States

Hang Lung Center for Real Estate, Tsinghua University ( email )

HeShanHeng Building
Beijing, 100084
China

HOME PAGE: http://https://siqizheng.mit.edu/

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