Multimedia Tools and Applications

, Volume 62, Issue 1, pp 179–207 | Cite as

Discovering places of interest in everyday life from smartphone data

  • Raul MontoliuEmail author
  • Jan Blom
  • Daniel Gatica-Perez


In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users’ real lives. A place-of-interest is defined as a location where the user usually goes and stays for a while. Two levels of clustering are used to obtain places of interest. First, user location points are grouped using a time-based clustering technique which discovers stay points while dealing with missing location data. The second level performs clustering on the stay points to obtain stay regions. A grid-based clustering algorithm has been used for this purpose. To obtain more user location points, a client-server system has been installed on the mobile phones, which is able to obtain location information by integrating GPS, Wifi, GSM and accelerometer sensors, among others. An extensive set of experiments has been performed to show the benefits of using the proposed framework, using data from the real life of a significant number of users over almost a year of natural phone usage.


Mobile Phones Multimodal data Place discovering Real-life long-term experiments 



This work was supported by Nokia Research Center Lausanne (NRC) through the LS-CONTEXT project. R. Montoliu was also supported by the Spanish Ministerio de Ciencia e Innovación under project Consolider Ingenio 2010 CSD2007-00018. Part of this work was done while R. Montoliu visited Idiap. We thank Niko Kiukkonen (NRC) and Olivier Bornet (Idiap) for their contribution to data collection, Trinh-Minh-Tri Do (Idiap) for help with data processing, and all the volunteers in the experiments for their participation.


  1. 1.
    Apple-Inc (2011) Reminders. A better way to do to-dos. Accessed 24 October 2011
  2. 2.
    Ashbrook D, Starner T (2003) Using gps to learn significant locations and predict movement across multiple users. Pers Ubiquit Comp 7:275–286CrossRefGoogle Scholar
  3. 3.
    Bamis A, Savvides A (2011) Exploiting human state information to improve gps sampling. In: Proceedings of the IEEE international conference on pervasive computing and communications workshops (PerCom ’11), pp 32–37Google Scholar
  4. 4.
    Brasche S, Bischof W (2005) Daily time spent indoors in german homes - baseline data for the assessment of indoor exposure of german occupants. Int J Hyg Environ Health 208(4):247–253CrossRefGoogle Scholar
  5. 5.
    Choujaa D, Dulay, N (2010) Predicting human behaviour from selected mobile phone data points. In: Proceedings of the 12th ACM international conference on ubiquitous computing (UbiComp ’10), pp 105–108Google Scholar
  6. 6.
    Eagle N, Pentland AS (2009) Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 63(7):1057–1066CrossRefGoogle Scholar
  7. 7.
    Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, pp 226–231Google Scholar
  8. 8.
    Farrahi K, Gatica-Perez D (2008) Discovering human routines from cell phone data with topic models. In: Proceedings of the IEEE international symposium on wearable computers (iswc ’08)Google Scholar
  9. 9.
    Farrahi K, Gatica-Perez D (2008) What did you do today?: discovering daily routines from large-scale mobile data. In: Proceeding of the 16th ACM international conference on multimedia (MM ’08), pp 849–852Google Scholar
  10. 10.
    Gatica-Perez D (2010) Modeling human behavior with mobile phones. In: Proceedings of the ACM multimedia 2010 international conference (MM’10), pp 1783–1784Google Scholar
  11. 11.
    Gonzalez M, Hidalgo C, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782CrossRefGoogle Scholar
  12. 12.
    Hightower J, Consolvo S, LaMarca A, Smith I, Hughes J (2005) Learning and recognizing the places we go. In: Proceedings of the seventh international conference on ubiquitous computing (Ubicomp ’05), pp 159–176Google Scholar
  13. 13.
    Jensen B, Larsen J, Jensen K, Larsen J, Hansen L (2010) Estimating human predictability from mobile sensor data. In: Proceedings of the 2010 IEEE international workshop on machine learning for signal processing (MLSP ’10), pp 196–201Google Scholar
  14. 14.
    Kang JH, Welbourne W, Stewart B, Borriello G (2005) Extracting places from traces of locations. Mobile Comput Commun Rev 9(3):58–68CrossRefGoogle Scholar
  15. 15.
    Kim DH, Hightower J, Govindan R, Estrin D (2009) Discovering semantically meaningful places from pervasive rf-beacons. In: Proceedings of the 11th international conference on ubiquitous computing (Ubicomp ’09), pp 21–30Google Scholar
  16. 16.
    Kiukkonen N, Blom J, Dousse O, Gatica-Perez D, Laurila J (2010) Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proceedings of ACM international conference on pervasive services (ICPS ’10)Google Scholar
  17. 17.
    LaMarca A, Chawathe Y, Consolvo S, Hightower J, Smith IE, Scott J, Sohn T, Howard J, Hughes J, Potter F, Tabert J, Powledge P, Borriello, G, Schilit BN (2005) Place lab: device positioning using radio beacons in the wild. In: Gellersen H-W, Want R, Schmidt A (eds) Proceedings of the third international conference on pervasive computing, pp 116–133Google Scholar
  18. 18.
    Marmasse N, Schmandt C (2000) Location-aware information delivery with commotion. InL Proceedings of the 2nd international symposium on handheld and ubiquitous computing (HUC ’00), pp 157–171Google Scholar
  19. 19.
    Montoliu R, Gatica-Perez D (2010) Discovering human places of interest from multimodal mobile phone data. In: Proceedings of the 9th international conference on mobile and ubiquitous multimedia (MUM2010)Google Scholar
  20. 20.
    Raento M, Oulasvirta A, Eagle N (2009) Smartphones: an emerging tool for social scientists. Sociol Methods Res 37(3):426–454MathSciNetCrossRefGoogle Scholar
  21. 21.
    Reddy S, Burke J, Estrin D, Hansen M, Srivastava M (2008) Determining transportation mode on mobile phones. In: Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers (ISWC ’08), pp 25–28Google Scholar
  22. 22.
    Schapsis C (2011) Location based social networks, location based social apps and games - links. Accessed 10 November 2011
  23. 23.
    Wang Y, Lin J, Annavaram M, Jacobson QA, Hong J, Krishnamachari B, Sadeh N (2009) A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of the 7th international conference on mobile systems, applications, and services (MobiSys ’09), pp 179–192Google Scholar
  24. 24.
    Wikipedia (2011) List of countries by number of mobile phones in use. Accessed 13 October 2011
  25. 25.
    Ye Y, Zheng Y, Chen Y, Feng J, Xie X (2009) Mining individual life pattern based on location history. In: MDM 2009, tenth international conference on mobile data management, pp 1–10Google Scholar
  26. 26.
    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 (UbiComp ’08), pp 312–321Google Scholar
  27. 27.
    Zheng Y, Liu L, Wang L, Xie X (2008) Learning transportation mode from raw gps data for geographic applications on the web. In: Proceeding of the 17th international conference on world wide web (WWW ’08), pp 247–256Google Scholar
  28. 28.
    Zheng VW, Zheng Y , Xie X, Yang Q (2010) Collaborative location and activity recommendations with gps history data. In: Proceedings of the 19th international world wide web conference (WWW ’10)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Institute of New Imaging Technologies (iNIT)Jaume I UniversityCastellónSpain
  2. 2.Nokia Research CenterLausanneSwitzerland
  3. 3.Idiap Research InstituteMartignySwitzerland
  4. 4.Ècole Polytechnique Fédérale de LausanneLausanneLausanneSwitzerland

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