School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China.
Key Lab of EDA, Research Institute of Tsinghua University in Shenzhen (RITS), Shenzhen, China.
PLoS One. 2019 Jul 10;14(7):e0219058. doi: 10.1371/journal.pone.0219058. eCollection 2019.
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.
预测城市区域的社会经济水平对于政府和城市管理者在分配资源和做出决策时具有重要意义。然而，目前估算区域社会经济水平的方法主要依赖于人口普查数据，这在时间和金钱方面都需要大量的投入。随着智能手机的普及和移动应用的流行，移动网络生成了大量记录人们行为的数据。在本文中，我们提出了一种使用人类在线和离线指标来预测城市区域社会经济水平的低成本方法。结果表明，使用从这些数据中提取的在线和离线特征训练的社会经济预测模型的准确率超过 85%。值得注意的是，在线特征与社会经济发展紧密相关。在很少进行人口普查的环境中，我们的方法为及时、准确地预测城市区域的经济状况提供了一种选择。
