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Coupling human mobility and social relationships to predict individual socioeconomic status: A graph neural network approach

文献类型：期刊论文

作者Chen, Xiao2,3; Pei, Tao1,2,3; Song, Ci2,3; Shu, Hua2,3; Guo, Sihui2,3; Wang, Xi2,3; Liu, Yaxi2,3; Chen, Jie3
刊名TRANSACTIONS IN GIS
出版日期2024-06-07
卷号N/A
DOI10.1111/tgis.13189
产权排序1
文献子类Article ; Early Access
英文摘要Understanding individual's socioeconomic status (SES) can provide supporting information for designing political and economic policies. Acquiring large-scale economic survey data is time-consuming and laborious. The widespread mobile phone data, which can reflect human mobility and social network characteristics, has become a low-cost data source for researchers to infer SES. However, previous studies often oversimplify human mobility features and social network features extracted from mobile phone data into general statistical features, resulting in discounting some important temporal and relational information. Therefore, we propose a comprehensive framework for individual SES prediction that effectively utilizes a combination of human mobility and social relationships. In this framework, Word2Vec module extracts human mobility features from mobile phone positioning data, and graph neural network (GNN) module GraphSAGE captures social network characteristics constructed from call detail records. We evaluated the effectiveness of our proposed approach by training the model with real-world data in Beijing. According to the experimental results, our proposed hybrid approach outperformed the other methods evidently, demonstrating that human mobility and social links are complementary in the characterization of SES. Coupling human mobility and social links can further deepen our understanding of cities' economic geography.
WOS关键词URBAN LAND-USE ; USERS ; MODEL
WOS研究方向Geography
WOS记录号WOS:001239930900001
出版者WILEY
源URL[http://ir.igsnrr.ac.cn/handle/311030/205316]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Pei, Tao
作者单位1.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Xiao,Pei, Tao,Song, Ci,et al. Coupling human mobility and social relationships to predict individual socioeconomic status: A graph neural network approach[J]. TRANSACTIONS IN GIS,2024,N/A.
APA Chen, Xiao.,Pei, Tao.,Song, Ci.,Shu, Hua.,Guo, Sihui.,...&Chen, Jie.(2024).Coupling human mobility and social relationships to predict individual socioeconomic status: A graph neural network approach.TRANSACTIONS IN GIS,N/A.
MLA Chen, Xiao,et al."Coupling human mobility and social relationships to predict individual socioeconomic status: A graph neural network approach".TRANSACTIONS IN GIS N/A(2024).

入库方式： OAI收割

来源：地理科学与资源研究所

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