Location-based big data analytics for guessing the next Foursquare check-ins


Location-based services on GPS-enabled smartphones are undergoing strong growth. Capitalizing on the popularity of this geo-location social media, a mobile app called Foursquare is developed to recommend its users places where they may be interested in, to travel from their current proximities. Such location data, in the form of check-ins by Foursquare, have huge business potentials including marketing, advertising and consumers’ behaviors analysis. Many researchers from both academia and industries are seriously looking into this location-based big data which comes in high velocity (with millions of users and frequent geo-tagging), and wide variety (with potentially many meta-data and associations), accumulating into a huge volume. One of the fundamental analytics in such big data is to guess which check-in locations a user would move to, as a prerequisite for sequential mining and other lifestyle pattern analysis. This paper reports a novel, but simple big data analytic by sampling a portion of location data for predicting the next check-in locations. This proposed analytic does not need every individual user’s history path and ID to match the history path of the current user in the database in order to infer a prediction. We show by a simulation experiment based on a Foursquare dataset that a minimum of two pairs of coordinates are required to provide a prediction. Several variables such as segment lengths, number of check-ins, and time factors are investigated in the experiment in relation to the prediction accuracy.

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  1. 1.

    Ho CW, Wang YB (2015) Re-purchase intentions and virtual customer relationships on social media brand community. In: Human-centric computing and information sciences, 15 July 2015, 5:18

  2. 2.

    Chong WH, Dai BT, Lim EP (2013) Prediction of venues in foursquare using flipped topic models. In: Advances in information retrieval. The series lecture notes in computer science, vol 9022, pp 623–634

  3. 3.

    He W, Liu X, Ren M (2011) Location cheating: a security challenge to location-based social network services. In: ICDCS ‘11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems, Washington, DC, pp 740–749

  4. 4.

    Li W, Li X, Yao M, Jiang J, Jin Q (2015) Personalized fitting recommendation based on support vector regression. In: Human-centric computing and information sciences, 22 July 2015, 5:21

  5. 5.

    Cramer H, Rost M, Holmquist LE (2011) Performing a check-in: emerging practices, norms and ‘conflicts’ in location-sharing using Foursquare. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, New York, pp 57–66

  6. 6.

    Belyi E, Giabbanelli PJ, Patel I, Balabhadrapathruni NH, Abdallah AB, Hameed W, Mago Vijay K (2016) Combining association rule mining and network analysis for pharmacosurveillance. J Supercomput 72(5):2014–2034

  7. 7.

    Sung Young, Chul Song (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FRAT analysis. J Converg 6(1):1–17

    Google Scholar 

  8. 8.

    Lee Eung-Jong, Kim Cheol-Hong, Im Y (2014) An intelligent green service in internet of things. J Converg 5(3):4–8

    Google Scholar 

  9. 9.

    Anagnostopoulos ZS, Exposito E (2016) Handling big data: research challenges and future directions. J Supercomput 72(4):1494–1516

    Article  Google Scholar 

  10. 10.

    Li H, Tang C, Qiao S, Wang Y, Yang N, Li C (1973) Hotspot district trajectory prediction, web-age information management. In: The series lecture notes in computer science, vol 6185, pp 74–84

  11. 11.

    Tomar RS, Verma S (2016) Neural network based lane change trajectory prediction in autonomous vehicles. In: Transactions on computational science XIII. The series lecture notes in computer science, vol 6750, pp 125–146

  12. 12.

    Herder E, Siehndel P, Kawase R (2010) Predicting user locations and trajectories, user modeling, adaptation, and personalization. The series lecture notes in computer science, vol 8538, pp 86–97

  13. 13.

    Calliess JP, Osborne M, Roberts SJ (2014) Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp 1109–1116

  14. 14.

    Piórkowski M, Grossglauser M (2015) Constrained tracking on a road network, wireless sensor networks. The series lecture notes in computer science, vol 3868, pp 148–163

  15. 15.

    Bianco C, Guarino L, Wahl FM (2011) A novel second order filter for the real-time trajectory scaling. In: 2011 IEEE International Conference on Robotics and automation (ICRA), 9–13 May 2011, pp 5813–5818

  16. 16.

    Ninawe SS, Venkataram P (2015) A method of designing a generic actor model for a professional social network. In: Human-centric computing and information sciences, 12 August 2015, 5:25

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The authors of this paper are thankful to the financial supports of the Grant offered with code: MYRG2015-00024, called “Building Sustainable Knowledge Networks through Online Communities” by RDAO, University of Macau.

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Correspondence to Simon Fong.

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Zhuang, Y., Fong, S., Yuan, M. et al. Location-based big data analytics for guessing the next Foursquare check-ins. J Supercomput 73, 3112–3127 (2017). https://doi.org/10.1007/s11227-016-1925-2

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  • Sequence mining
  • Next location prediction
  • Foursquare