Coupling human mobility and social relationships to predict individual socioeconomic status: A graph neural network approach
Xiao Chen
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorCorresponding Author
Tao Pei
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
Correspondence
Tao Pei, State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China.
Email: [email protected]
Search for more papers by this authorCi Song
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorHua Shu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorSihui Guo
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorXi Wang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorYaxi Liu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorJie Chen
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorXiao Chen
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorCorresponding Author
Tao Pei
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
Correspondence
Tao Pei, State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China.
Email: [email protected]
Search for more papers by this authorCi Song
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorHua Shu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorSihui Guo
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorXi Wang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorYaxi Liu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorJie Chen
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorAbstract
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.
CONFLICT OF INTEREST STATEMENT
The authors declare no competing interests.
Open Research
DATA AVAILABILITY STATEMENT
Research data are not shared.
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