Predicting neighborhoods' socioeconomic attributes using restaurant data.

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 re

SUBMITTER: Dong L 

PROVIDER: S-EPMC6681720 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature