Predicting neighborhoods’ socioeconomic attributes using restaurant data
Name
15447.full.pdf
Description
Published version
Size
953.61 KB
Format
Adobe PDF
Checksum (MD5)
8b4cb192a14c9f46b77fd593d2e0f0d0
Author(s)
Dong, Lei
Ratti, Carlo
Zheng, Siqi
Date Issued
July 15, 2019
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Dong, Lei, Carlo Ratti and Siqi Zheng. "Predicting neighborhoods’ socioeconomic attributes using restaurant data." Proceedings of the National Academy of Sciences of the United States of America 116 (2019): 15447-15425 © 2019 The Author(s)
Version
Final published version
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.
Subjects
Multidisciplinary
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/pnas.1903064116
