Work

Predicting socio-economic levels of urban regions via offline and online indicators

Year: 2019

Type: article

Abstract: Predicting the socio-economic level of an urban region is of great significance for governments and city managers when allocating resources and making decisions. However, the current approaches for estimating regional socio-economic levels heavily rely on census data, which demands significant effort in terms of time and money. With the ubiquitous usage of smart phones and the prevalence of mobile applications, massive amounts of data are generated by mobile networks that record people's behaviors. In this paper, we propose a low-cost approach of using humans' online and offline indicators to predict the socio-economic levels of urban regions. The results show that the socio-economic prediction model that is trained using online and offline features extracted from these data achieves a high accuracy over 85%. Notably, online features are showed to be tightly linked with socio-economic development. In environments where censuses are rarely held, our method provides an option for timely and accurate prediction of the economic status of urban regions.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0219058

Authors: Yi Ren, Tong Xia, Yong Li, Xiang Chen

Institutions: Sun Yat-sen University, Research Institute of Tsinghua University in Shenzhen, Tsinghua University

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,595