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

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.

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References

  1. Barabási A-L (2011) Human Dynamics: From Human Mobility to Predictability. In: Machine Learning and Knowledge Discovery in Databases. Springxer Berlin Heidelberg, 3–3

  2. Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439(7075):462–465

  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):e20814

    Article  Google Scholar 

  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, 2605–2611

  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, 1082

  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, 57

  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):667–689

  8. Do TMT, Dousse O, Miettinen M, Gatica-Perez D (2015) A probabilistic kernel method for human mobility prediction with smartphones. Pervasive Mob Comput 20:13–28

    Article  Google Scholar 

  9. Fano RM (1961) Transmission of information: a statistical theory of communication. M.I.T. Press, Cambridge

    Google Scholar 

  10. Gavalas D, Kenteris M (2011) A web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquit Comput 15(7):759–770

  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):695–719

    Article  Google Scholar 

  12. González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–82

    Article  Google Scholar 

  13. Goodchild MF (2007) Citizens as sensors: web 2.0 and the volunteering of geographic information. GeoFocus 7:8–10

    Google Scholar 

  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 428–435

  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, 193–200

  16. Kurashima T, Iwata T, Irie G, Fujimura K (2013) Travel route recommendation using geotagged photos. Knowl Inf Syst 37(1):37–60

    Article  Google Scholar 

  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, 1–10

  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):799–814

    Article  Google Scholar 

  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. 3–8

  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, 733–738

  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):e86026

    Article  Google Scholar 

  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):662–684

  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 39–53

    Chapter  Google Scholar 

  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 54:336–346

    Article  Google Scholar 

  25. Miller GA (1955) Note on the bias of information estimates. In: Information Theory in Psychology: Problems and Methods. Free Press, 95–100

  26. Munar AM (2010) Digital exhibitionism the Age of exposure. Cult Unbound: J Curr Cult Res 2(3):401–422

    Article  Google Scholar 

  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, 144–153

  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), 42:1–42:32

  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):583–604

    Google Scholar 

  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, 306–315

  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, 971–976

  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, 103–110

  33. Ruths D, Pfeffer J (2014) Social media for large studies of behavior. Science 346(6213):1063–1064

    Article  Google Scholar 

  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

  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, 264–273

  36. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Article  Google Scholar 

  37. Sheth A (2009) Citizen sensing, social signals, and enriching human experience. IEEE Internet Comput 13(4):87–92

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Spiegler ED, Hildebrand C, Michahelles F (2011) Social networks in pervasive advertising and shopping. In: Pervasive advertising. Springer, London, pp 207–225

  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 1–7

  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 54:255–265

    Article  Google Scholar 

  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):480–498

    Article  Google Scholar 

  43. Wilson E (1927) Probable inference, the law of succession, and statistical inference. J Am Stat Assoc 22(158):209–212

    Article  Google Scholar 

  44. Woerndl W, Brocco M, Eigner R (2009) Context-aware recommender systems in mobile scenarios. Int J Inf Technol Web Eng 4(1):67–85

    Article  Google Scholar 

  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.

  46. Zhu L-C, Li Z-J, Jiang S-X (2014) LBSN-Based Personalized Routes Recommendation. Appl Mech Mater 644-650:3230–3234

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Li, M., Westerholt, R., Fan, H. et al. Assessing spatiotemporal predictability of LBSN: a case study of three Foursquare datasets. Geoinformatica 22, 541–561 (2018). https://doi.org/10.1007/s10707-016-0279-5

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  • DOI: https://doi.org/10.1007/s10707-016-0279-5

Keywords

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