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Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks
Li, Mingxiao1,2,3,4; Gao, Song4; Lu, Feng3,5,6; Liu, Kang3,7; Zhang, Hengcai3; Tu, Wei1,2
2021-04-21
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Pages28
Corresponding AuthorGao, Song(song.gao@wisc.edu) ; Tu, Wei(tuwei@szu.edu.cn)
AbstractDynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity intensity variation but is rarely considered in the existing literature. To mitigate these limitations, we proposed a novel dynamic activity intensity prediction method with deep learning on graphs using the interactions in both physical and social spaces. In this method, the physical interactions and social interactions between spatial units were integrated into a fused graph convolutional network to model multi-type spatial interaction patterns. The future activity intensity variation was predicted by combining the spatial interaction pattern and the temporal pattern of activity intensity series. The method was verified with a country-scale anonymized mobile phone dataset. The results demonstrated that our proposed deep learning method with combining graph convolutional networks and recurrent neural networks outperformed other baseline approaches. This method enables dynamic human activity intensity prediction from a more spatially and socially integrated perspective, which helps improve the performance of modeling human dynamics.
KeywordHuman activity intensity prediction graph convolutional networks social interaction mobile phone data human mobility
DOI10.1080/13658816.2021.1912347
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program[2016YFB0502104] ; Guangdong Province Basic and Applied Basic Research Fund Project[2020A1515111166] ; State Key Laboratory of Resources and Environmental Information System ; National Natural Science Foundation of China[41771436] ; National Natural Science Foundation of China[41901391] ; National Natural Science Foundation of China[42071360] ; Shenzhen Basic Research Program[JCYJ20190807163001783] ; National Science Foundation of United States[1940091] ; Wisconsin Alumni Research Foundation
Funding OrganizationNational Key Research and Development Program ; Guangdong Province Basic and Applied Basic Research Fund Project ; State Key Laboratory of Resources and Environmental Information System ; National Natural Science Foundation of China ; Shenzhen Basic Research Program ; National Science Foundation of United States ; Wisconsin Alumni Research Foundation
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000641781900001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/161709
Collection中国科学院地理科学与资源研究所
Corresponding AuthorGao, Song; Tu, Wei
Affiliation1.Shenzhen Univ, Shenzhen Key Lab Spatial Informat Smart Sensing &, Guangdong Lab Artificial Intelligence & Digital E, Guangdong Key Lab Urban Informat, Shenzhen, Peoples R China
2.Shenzhen Univ, Res Inst Smart Cities, Shenzhen, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
4.Univ Wisconsin, Dept Geog, Geospatial Data Sci Lab, Madison, WI 53706 USA
5.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China
6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
7.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
Recommended Citation
GB/T 7714
Li, Mingxiao,Gao, Song,Lu, Feng,et al. Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2021:28.
APA Li, Mingxiao,Gao, Song,Lu, Feng,Liu, Kang,Zhang, Hengcai,&Tu, Wei.(2021).Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,28.
MLA Li, Mingxiao,et al."Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2021):28.
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