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Discovering locations and habits from human mobility data

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Abstract

Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals’ visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual’s habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.

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Notes

  1. https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html

  2. https://bit.ly/31XrMkM

  3. https://www.google.com/maps/timeline

  4. https://takeout.google.com/

  5. https://bit.ly/31XrMkM

References

  1. Toch E, Lerner B, Ben-Zion E, Ben-Gal I (2019) Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowl Inf Syst 58(3):501–523

    Article  Google Scholar 

  2. Berry DM (2011) The computational turn: thinking about the digital humanities. Culture Machine, vol 12

  3. Lazer D, Pentland A, Adamic L, Aral S, Barabási A-L, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M et al (2009) Computational social science. Science 323(5915):721–723

    Article  Google Scholar 

  4. Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 37 (6):1067–1080

    Article  Google Scholar 

  5. Poushter J, et al. (2016) Smartphone ownership and internet usage continues to climb in emerging economies. Pew Res Center 22:1–44

    Google Scholar 

  6. Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W-Y (2008) Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, ACM, pp 34

  7. Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on World wide Web, ACM, pp 791–800

  8. Zheng Y, Xie X, Ma W-Y (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39

    Google Scholar 

  9. Cao X, Cong G, Jensen CS (2010) Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment 3(1-2):1009–1020

    Article  Google Scholar 

  10. Lee I, Cai G, Lee K (2013) Mining points-of-interest association rules from geo-tagged photos. In: 2013 46th Hawaii international conference on system sciences, IEEE, pp 1580–1588

  11. Calabrese F, Ferrari L, Blondel VD (2015) Urban sensing using mobile phone network data: a survey of research. Acm Computing Surveys (csur) 47(2):25

    Article  Google Scholar 

  12. Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779

    Article  Google Scholar 

  13. Song C, Qu Z, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021

    Article  MathSciNet  Google Scholar 

  14. Calabrese F, Colonna M, Lovisolo P, Parata D, Ratti C (2011) Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans Intell Transp Syst 12(1):141–151

    Article  Google Scholar 

  15. Alhasoun F, Almaatouq A, Greco K, Campari R, Alfaris A, Ratti C (2014) The city browser: utilizing massive call data to infer city mobility dynamics. In: 3rd international workshop on urban computing (UrbComp 2014). Urbcomp: New York

  16. Herder E, Siehndel P (2012) Daily and weekly patterns in human mobility. In: UMAP Workshops Citeseer

  17. Talbot D (2013) Big data from cheap phones. Technol Rev 116(3):50–54

    Google Scholar 

  18. Andrade T, Cancela B, Gama J (2020) Mining human mobility data to discover locations and habits. In: Cellier P, Driessens K (eds) Machine learning and knowledge discovery in databases. Springer International Publishing, Cham , pp 390–401

  19. Suzuki J, Suhara Y, Toda H, Nishida K (2019) Personalized visited-poi assignment to individual raw GPS trajectories. arXiv:1901.06257

  20. Andrade T, Gama J (2020) Identifying points of interest and similar individuals from raw GPS data. In: Cagáñová D, Horñáková N (eds) Mobility Internet of Things 2018. Springer International Publishing, Cham, pp 293–305

  21. Yang M, Cheng C, Chen B (2018) Mining individual similarity by assessing interactions with personally significant places from GPS trajectories. ISPRS International Journal of Geo-Information 7(3):126

    Article  Google Scholar 

  22. Chen X, Shi D, Zhao B, Liu F (2016) Periodic pattern mining based on GPS trajectories. In: International symposium on advances in electrical, electronics and computer engineering, Atlantis Press, 2016

  23. Thuillier E, Moalic L, Lamrous S, Caminada A (2018) Clustering weekly patterns of human mobility through mobile phone data. IEEE Trans Mob Comput 17(4):817–830

    Article  Google Scholar 

  24. Ester M, Kriegel H-P, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34):226–231

    Google Scholar 

  25. Andrews BR (1903) Habit. The American Journal of Psychology 14(2):121–149. [Online]. Available: http://www.jstor.org/stable/1412711

    Article  Google Scholar 

  26. Ye Y, Zheng Y, Chen Y, Feng J, Xie X (2009) Mining individual life pattern based on location history. In: Tenth international conference on Mobile Data management: Systems, Services and Middleware, 2009. MDM’09, IEEE, pp 1–10

  27. Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous computing 7(5):275–286

    Article  Google Scholar 

  28. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  29. Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD international conference on Management of data, 47–57

  30. Andrade T, Cancela B, Gama J (2019) Discovering common pathways across users’ habits in mobility data. In: EPIA conference on artificial intelligence, Springer, pp 410–421

  31. Bishop CM (2006) Pattern recognition and machine learning. Springer

  32. Zheng Y, Li Q, Chen Y, Xie X, Ma W-Y (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on Ubiquitous computing, ACM, pp 312–321

  33. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  34. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intel 2:224–227

    Article  Google Scholar 

  35. Gama J, Carvalho ACPdL, Faceli K, Lorena AC, Oliveira M et al (2015) Extração de conhecimento de dados: data mining

  36. Bianchi FM, Rizzi A, Sadeghian A, Moiso C (2016) Identifying user habits through data mining on call data records. Eng Appl Artif Intell 54:49–61

    Article  Google Scholar 

  37. Sardianos C, Varlamis I, Bouras G (2018) Extracting user habits from google maps history logs. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, pp 690–697

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Funding

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundaç ão para a Ciência e a Tecnologia within project : UID/EEA/50014/2019

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Authors and Affiliations

  1. INESC TEC, Porto, Portugal

    Thiago Andrade, Brais Cancela & João Gama

  2. Universidade da Coruña, Coruña, Spain

    Thiago Andrade & Brais Cancela

  3. University of Porto, Porto, Portugal

    João Gama

Authors
  1. Thiago Andrade
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  2. Brais Cancela
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  3. João Gama
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Correspondence to Thiago Andrade.

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Andrade, T., Cancela, B. & Gama, J. Discovering locations and habits from human mobility data. Ann. Telecommun. 75, 505–521 (2020). https://doi.org/10.1007/s12243-020-00807-x

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  • DOI: https://doi.org/10.1007/s12243-020-00807-x

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