Assessing spatiotemporal predictability of LBSN: a case study of three Foursquare datasets

  • Ming Li
  • Rene Westerholt
  • Hongchao Fan
  • Alexander Zipf
Article

Abstract

Location-based social networks (LBSN) have provided new possibilities for researchers to gain knowledge about human spatiotemporal behavior, and to make predictions about how people might behave through space and time in the future. An important requirement of successfully utilizing LBSN in these regards is a thorough understanding of the respective datasets, including their inherent potential as well as their limitations. Specifically, when it comes to predictions, we must know what we can actually expect from the data, and how we could maximize their usefulness. Yet, this knowledge is still largely lacking from the literature. Hence, this work explores one particular aspect which is the theoretical predictability of LBSN datasets. The uncovered predictability is represented with an interval. The lower bound of the interval corresponds to the amount of regular behaviors that can easily be anticipated, and represents the correct predication rate that any algorithm should be able to achieve. The upper bound corresponds to the amount of information that is contained in the dataset, and represents the maximum correct prediction rate that cannot be exceeded by any algorithms. Three Foursquare datasets from three American cities are studied as an example. It is found that, within our investigated datasets, the lower bound of predictability of the human spatiotemporal behavior is 27%, and the upper bound is 92%. Hence, the inherent potentials of the dataset for predicting human spatiotemporal behavior are clarified, and the revealed interval allows a realistic assessment of the quality of predictions and thus of associated algorithms. Additionally, in order to provide further insight into the practical use of the dataset, the relationship between the predictability and the check-in frequencies are investigated from three different perspectives. It was found that the individual perspective provides no significant correlations between the predictability and the check-in frequency. In contrast, the same two quantities are found to be negatively correlated from temporal and spatial perspectives. Our study further indicates that the heavily frequented contexts and some extraordinary geographic features such as airports could be good starting points for effective improvements of prediction algorithms. In general, this research provides novel knowledge regarding the nature of the LBSN dataset and practical insights for a more reasonable utilization of the dataset.

Keywords

Predictability Spatiotemporal behavior Context Location-based social networks Foursquare Citizen sensing 

References

  1. 1.
    Barabási A-L (2011) Human Dynamics: From Human Mobility to Predictability. In: Machine Learning and Knowledge Discovery in Databases. Springxer Berlin Heidelberg, 3–3Google Scholar
  2. 2.
    Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439(7075):462–465Google Scholar
  3. 3.
    Calabrese F, Smoreda Z, Blondel VD, Ratti C (2011) Interplay between telecommunications and face-to-face interactions: a study using mobile phone data. PLoS ONE 6(7):e20814CrossRefGoogle Scholar
  4. 4.
    Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next : successive point-of-interest recommendation. In: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence. AAAI Press, 2605–2611Google Scholar
  5. 5.
    Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD’11. New York, New York, USA: ACM Press, 1082Google Scholar
  6. 6.
    Cramer H, Rost M, Holmquist LE (2011) Performing a check-in. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services - MobileHCI’11. ACM, 57Google Scholar
  7. 7.
    de Albuquerque J-P, Herfort B, Brenning A, Zipf A (2015) A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. Int J Geogr Inf Sci 29(4):667–689Google Scholar
  8. 8.
    Do TMT, Dousse O, Miettinen M, Gatica-Perez D (2015) A probabilistic kernel method for human mobility prediction with smartphones. Pervasive Mob Comput 20:13–28CrossRefGoogle Scholar
  9. 9.
    Fano RM (1961) Transmission of information: a statistical theory of communication. M.I.T. Press, CambridgeGoogle Scholar
  10. 10.
    Gavalas D, Kenteris M (2011) A web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquit Comput 15(7):759–770Google Scholar
  11. 11.
    Giannotti F, Nanni M, Pedreschi D, Pinelli F, Renso C, Rinzivillo S, Trasarti R (2011) Unveiling the complexity of human mobility by querying and mining massive trajectory data. Int J Very Large Data Bases 20(5):695–719CrossRefGoogle Scholar
  12. 12.
    González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–82CrossRefGoogle Scholar
  13. 13.
    Goodchild MF (2007) Citizens as sensors: web 2.0 and the volunteering of geographic information. GeoFocus 7:8–10Google Scholar
  14. 14.
    Gu Y, Liu W, Yao Y, Song J (2014) Fast routing in location-based social networks leveraging check-in data. In: 2014 I.E. International Conference on Internet of Things(iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom). IEEE, pp 428–435Google Scholar
  15. 15.
    Krueger R, Thom D, Ertl T (2014) Visual analysis of movement behavior using web data for context enrichment. In: Pacific Visualization Symposium (PacificVis), 2014 IEEE. IEEE, 193–200Google Scholar
  16. 16.
    Kurashima T, Iwata T, Irie G, Fujimura K (2013) Travel route recommendation using geotagged photos. Knowl Inf Syst 37(1):37–60CrossRefGoogle Scholar
  17. 17.
    Lee R, Sumiya K (2010) Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks. ACM, 1–10Google Scholar
  18. 18.
    Li M, Sun Y, Fan H (2015) Contextualized relevance evaluation of geographic information for mobile users in location-based social networks. ISPRS Int J Geo-Inf 4(2):799–814CrossRefGoogle Scholar
  19. 19.
    Liu X, Troncy R, Huet B (2011) Using social media to identify events. In: Proceedings of the 3rd ACM SIGMM international workshop on Social media. 3–8Google Scholar
  20. 20.
    Liu X, Liu Y, Aberer K, Miao C (2013) Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. San Francisco, CA, USA, 733–738Google Scholar
  21. 21.
    Liu Y, Sui Z, Kang C, Gao Y (2014) Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE 9(1):e86026CrossRefGoogle Scholar
  22. 22.
    Majid A, Chen L, Chen G, Mirzaa H-T, Hussain I, Woodward J (2013) A context-aware personalized travel recommendation system based on geotagged social media data mining. Int J Geogr Inf Sci 27(4):662–684Google Scholar
  23. 23.
    McKenzie G, Adams B, Janowicz K (2013) A thematic approach to user similarity built on geosocial check-ins. In: Vandenbroucke D, Bucher B, Crompvoets J (eds) Geographic information science at the heart of Europe. Springer International Publishing, Cham, pp 39–53CrossRefGoogle Scholar
  24. 24.
    McKenzie G, Janowicz K, Gao S, Gong L (2015) How where is when? On the regional variability and resolution of geosocial temporal signatures for points of interest. Comput Environ Urban Syst 54:336–346CrossRefGoogle Scholar
  25. 25.
    Miller GA (1955) Note on the bias of information estimates. In: Information Theory in Psychology: Problems and Methods. Free Press, 95–100Google Scholar
  26. 26.
    Munar AM (2010) Digital exhibitionism the Age of exposure. Cult Unbound: J Curr Cult Res 2(3):401–422CrossRefGoogle Scholar
  27. 27.
    Noulas A, Scellato S, Lathia N, Mascolo C (2012) A random walk around the City: New Venue Recommendation in Location-Based Social Networks. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing. Ieee, 144–153Google Scholar
  28. 28.
    Parent C, Spaccapietra S, Renso C, Andrienko G, Andrienko N, Bogorny V, Damiani ML, Gkoulalas-Divanis A, Macedo J, Pelekis N, Theodoridis Y, Yan Z (2013) Semantic trajectories modeling and analysis. ACM Comput Surv 45(4), 42:1–42:32Google Scholar
  29. 29.
    Pham MC, Cao Y, Klamma R, Jarke M (2011) A clustering approach for collaborative filtering recommendation using social network analysis. J Univ Comput Sci 17(4):583–604Google Scholar
  30. 30.
    Preoţiuc-Pietro D, Cohn T (2013) Mining user behaviours: a study of check-in patterns in Location Based Social Netoworks. In: Proceedings of the 5th Annual ACM Web Science Conference on - WebSci’13. New York, New York, USA: ACM Press, 306–315Google Scholar
  31. 31.
    Quercia D, Lathia N (2010) Recommending social events from mobile phone location data. In: Data Mining (ICDM), 2010 I.E. 10th International Conference on. Sydney, Australia, 971–976Google Scholar
  32. 32.
    Rattenbury T, Good N, Naaman M (2007) Towards automatic extraction of event and place semantics from flickr tags. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 103–110Google Scholar
  33. 33.
    Ruths D, Pfeffer J (2014) Social media for large studies of behavior. Science 346(6213):1063–1064CrossRefGoogle Scholar
  34. 34.
    Salah A, Gevers T, Sebe N, Vinciarelli A (2010) Challenges of human behavior understanding. In: Human behavior understanding. Springer Berlin/Heidelberg, pp 1–12 Google Scholar
  35. 35.
    Sengstock C, Gertz M, Flatow F, Abdelhaq H (2013) A probablistic model for spatio-temporal signal extraction from social media. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL’13. New York, New York, USA: ACM Press, 264–273Google Scholar
  36. 36.
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423CrossRefGoogle Scholar
  37. 37.
    Sheth A (2009) Citizen sensing, social signals, and enriching human experience. IEEE Internet Comput 13(4):87–92CrossRefGoogle Scholar
  38. 38.
    Song C, Qu Z, Blumm N, Barabasi A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021CrossRefGoogle Scholar
  39. 39.
    Spiegler ED, Hildebrand C, Michahelles F (2011) Social networks in pervasive advertising and shopping. In: Pervasive advertising. Springer, London, pp 207–225Google Scholar
  40. 40.
    Steiger E, Ellersiek T, Zipf A (2014) Explorative public transport flow analysis from uncertain social media data. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information - GeoCrowd ’14. New York, New York, USA: ACM Press, pp 1–7Google Scholar
  41. 41.
    Steiger E, Westerholt R, Resch B, Zipf A (2015) Twitter as an indicator for whereabouts of people? Correlating twitter with UK census data. Comput Environ Urban Syst 54:255–265CrossRefGoogle Scholar
  42. 42.
    Sun Y, Fan H, Li M, Zipf A (2016) Identifying the city center using human travel flows generated from location-based social networking data. Environ Plann B Plann Des 43(3):480–498CrossRefGoogle Scholar
  43. 43.
    Wilson E (1927) Probable inference, the law of succession, and statistical inference. J Am Stat Assoc 22(158):209–212CrossRefGoogle Scholar
  44. 44.
    Woerndl W, Brocco M, Eigner R (2009) Context-aware recommender systems in mobile scenarios. Int J Inf Technol Web Eng 4(1):67–85CrossRefGoogle Scholar
  45. 45.
    Ye M, Yin P, Lee W-C, Lee D-L (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR’11. New York, New York, USA: ACM Press, 325.Google Scholar
  46. 46.
    Zhu L-C, Li Z-J, Jiang S-X (2014) LBSN-Based Personalized Routes Recommendation. Appl Mech Mater 644-650:3230–3234Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.GIScience Research Group, Institute of GeographyHeidelberg UniversityHeidelbergGermany

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