Please wait a minute...
浙江大学学报（理学版）
Journal of ZheJIang University(Science Edition)  2018, Vol. 45 Issue (1): 37-43    DOI: 10.3785/j.issn.1008-9497.2018.01.007
    
Mobile data visual analysis for human activity understanding
JIANG Hongyu1, WU Yadong1,2, ZHAO Weixin1, TANG Kai1
1. Southwest University of Science and Technology, Mianyang 621010, Sichuan Province, China;
2. Sichuan Civil-Military Integration Institute, Southwest University of Science and Technology, Mianyang 621010, Sichuan Province, China
Download: HTML ( )    PDF(3141KB)
Export: BibTeX | EndNote (RIS)      

Abstract  Mobile data imply various information, including spatio-temporal characteristics and the social relationship of human activities, which have great value for human behavior exploration. In order to analyze and understand the activities of mobile users, a mobile data visual analytics framework is proposed focusing on users' activity understanding based on the spatio-temporal and social features of mobile data. And, a visual analytic system for mobile data is also built, which aims to explore mobile users' behavior patterns in different period, detect their social roles and discover their real social relationship. It has been examined with mobile data in a city, and the results prove the effectiveness of the proposed method.

Key words： activities of users      spatio-temporal data      sparse data     
Received: 01 July 2017      Published: 15 December 2017
CLC:  TP391  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
JIANG Hongyu
WU Yadong
ZHAO Weixin
TANG Kai
Cite this article:

JIANG Hongyu, WU Yadong, ZHAO Weixin, TANG Kai. Mobile data visual analysis for human activity understanding. Journal of ZheJIang University(Science Edition), 2018, 45(1): 37-43.

URL:

https://www.zjujournals.com/sci/10.3785/j.issn.1008-9497.2018.01.007     OR     https://www.zjujournals.com/sci/Y2018/V45/I1/37


面向用户行为理解的移动通讯数据可视分析

通信数据包含人类活动的时空以及社会关系等信息，对人类行为分析有重要的价值.为了帮助分析者对用户的行为进行分析和理解，构建了从通信数据中探索用户的时空、社交等信息以分析用户行为的可视化流程，旨在理解用户的行为模式并通过行为的对比发现用户的社会角色以及用户之间的真实社交关系，通过迭代式交互过程，对用户不同时段的行为进行有效的理解和分析.在此基础上，构建了用户行为可视分析系统，采用半年的通信数据对该方法以及系统进行评估，结果显示，本方法能够有效理解个人行为、识别用户之间的关系.

关键词： 用户行为,  时空数据,  稀疏轨迹 
[1] CHEN S, YUAN X, WANG Z, et al. Interactive visual discovering of movement patterns from sparsely sampled geo-tagged social media data[J]. IEEE Transactions on Visualization & Computer Graphics, 2016, 22(1):270.
[2] KRVGER R, LOHMANN S, THOM D, et al. Using social media content in the visual analysis of movement data[J]. Research Microsoft Com, 2012, 97(11):31-32.
[3] ANDRIENKO G, ANDRIENKO N, WROBEL S. Visual analytics tools for analysis of movement data[J].ACM SIGKDD Explorations Newsletter, 2007, 9(2):38-46.
[4] CHU D, SHEETS D A, ZHAO Y, et al. Visualizing hidden themes of Taxi movement with semantic transformation[C]//Visualization Symposium (PacificVis). Piscataway:IEEE Press, 2014:137-144.
[5] GONZÁLEZ M C, HIDALGO C A, BARABÁSI A L. Understanding individual human mobility patterns[J]. Nature, 2008, 453(7196):779.
[6] ZHU Y, ZHANG Y, SHANG W, et al. Trajectory enabled service support platform for mobile users' behavior pattern mining[C]//Mobile and Ubiquitous Systems:NETWORKING & Services. Piscataway:IEEE Press, 2009:1-10.
[7] 谭钧元, 宋国杰, 谢昆青, 等. 一种有效的基于生活熵的移动用户分类算法[J]. 计算机研究与发展2009, 46:433-438. TAN J Y, SONG G J, XIE K Q, et al. An effective mining method for mobile subscribers based on life entropy[J]. Journal of Computer Research and Development, 2009, 46:433-438.
[8] SHAD S A.移动用户轨迹与行为模式挖掘方法研究[D]. 合肥:中国科学技术大学, 2013. SHAD S A. Mobile User Trajectory and Profile Mining[D]. Hefei:University of Science and Technology of China, 2013.
[9] CALABRESE F, PEREIRA F C, LORENZO G D, et al. The geography of taste:Analyzing cell-phone mobility and social events[J]. Lecture Notes in Computer Science, 2010, 6030:22-37.
[10] PULSELLI R M, ROMANO P, RATTI C, et al. Computing urban mobile landscapes through monitoring population density based on cell-phone chatting[J]. International Journal of Design & Nature & Ecodynamics, 2008, 3(2):121-134.
[11] READES J, CALABRESE F, SEVTSUK A, et al. Cellularcensus:Explorations in urban data collection[J]. IEEE Pervasive Computing, 2007, 6(3):30-38.
[12] DE MONTJOYE Y A, HIDALGO C A, VERLEYSEN M, et al. Unique in the crowd:The privacy bounds of human mobility[J]. Scientific Reports, 2013, 3(6):1376.
[13] WU W, XU J, ZENG H, et al. TelCoVis:Visual exploration of co-occurrence in urban human mobility based on telco data[J].IEEE Transactions on Visualization & Computer Graphics, 2016, 22(1):935.
[14] GONZÁLEZ M C, HIDALGO C A, BARABÁSI A L. Understanding individual human mobility patterns[J].Nature, 2008, 453(7196):779.
[15] ANDRIENKO G, ANDRIENKO N, FUCHS G.Business Intelligence[M]. Berlin:Springer International Publishing, 2015:39-59.
[16] ARIETTA S M, EFROS A A, RAMAMOORTHI R, et al. City forensics:Using visual elements to predict non-visual city attributes[J].IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):2624-2633.
No related articles found!