文献类型：期刊文献

英文题名：Inferring demographics from human trajectories and geographical context

作者：Wu, Lun[1,2];Yang, Liu[1,2];Huang, Zhou[1,2];Wang, Yaoli[1,2];Chai, Yanwei[3];Peng, Xia[4];Liu, Yu[1,2]

第一作者：Wu, Lun

通讯作者：Huang, Z[1]

机构：[1]Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China;[2]Peking Univ, Beijing Key Lab Spatial Informat Integrat & Its A, Beijing 100871, Peoples R China;[3]Peking Univ, Coll Urban & Environm Sci, Dept Urban & Econ Geog, Beijing 100871, Peoples R China;[4]Beijing Union Univ, Tourism Coll, Collaborat Innovat Ctr eTourism, Beijing 100101, Peoples R China

第一机构：Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China

通讯机构：[1]corresponding author), Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China.

年份：2019

卷号：77

外文期刊名：COMPUTERS ENVIRONMENT AND URBAN SYSTEMS

收录：;EI(收录号：20193107253028);Scopus(收录号：2-s2.0-85069838975);WOS:【SSCI(收录号:WOS:000488657500023)】；

基金：The authors would like to thank the Editor and three anonymous reviewers for their insightful comments. We would also like to thank colleges from the Spatio-Temporal Social Sensing Lab for their supports. This work was supported by grants from the National Key Research and Development Program of China (2017YFE9126100), the National Natural Science Foundation of China (41771425, 41830645, 41625003), and the Beijing Philosophy and Social Science Foundation (17JDGLB002).

语种：英文

外文关键词：Demographics inferring; Trajectory mining; Human mobility; Big geo-data

摘要：The advances of positioning technologies and the widespread use of mobile devices bring us massive data with location information, or so-called big geo-data. One important part of big geo-data is massive digital human trajectories recorded by location-enabled mobile terminals and social apps. Digital human trajectories have been studied to learn more about human mobility and human activity. Existing research has shown that there exist strong associations between trajectory patterns and demographics. Given that demographics are essential information to various domains but not easy to acquire timely and on a large scale, inferring demographics from human trajectories has attracted attention from academia. In this paper, we proposed a demographics inferring framework suitable for big geo-data processing. Trajectory patterns were quantified from both spatiotemporal and semantic perspectives. Spatiotemporal features extracted from trajectories directly were used for capturing how people traveled in space and time. Semantic features obtained by attaching geographical context to trajectories were to reflect activities people conducted. Spatiotemporal and semantic features were organized into feature vectors and then input to supervised classification models to infer demographics. GPS trajectories and land use data in Beijing were used to validate the framework. Results show that the inference accuracies of marital status and residency status achieve 80% and thus prove the feasibility of our framework. This study can facilitate decision making in both business and social studies, such as personalized recommendation, commercial site selection and urban planning.