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Photowalking the City: Comparing Hypotheses About Urban Photo Trails on Flickr

  • Martin Becker
  • Philipp Singer
  • Florian Lemmerich
  • Andreas Hotho
  • Denis Helic
  • Markus Strohmaier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9471)

Abstract

Understanding human movement trajectories represents an important problem that has implications for a range of societal challenges such as city planning and evolution, public transport or crime. In this paper, we focus on geo-temporal photo trails from four different cities (Berlin, London, Los Angeles, New York) derived from Flickr that are produced by humans when taking sequences of photos in urban areas. We apply a Bayesian approach called HypTrails to assess different explanations of how the trails are produced. Our results suggest that there are common processes underlying the photo trails observed across the studied cities. Furthermore, information extracted from social media, in the form of concepts and usage statistics from Wikipedia, allows for constructing explanations for human movement trajectories.

Keywords

Markov Chain Model Human Mobility Center Hypothesis Mobile Phone Data Human Mobility Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    An, J., Quercia, D., Crowcroft, J.: Partisan sharing: facebook evidence and societal consequences. In: Conference on Online Social Networks, pp. 13–24. ACM (2014)Google Scholar
  2. 2.
    Borges, J., Levene, M.: Evaluating variable-length markov chain models for analysis of user web navigation sessions. IEEE Transactions on Knowledge and Data Engineering 19(4), 441–452 (2007). http://dx.doi.org/10.1109/TKDE.2007.1012 CrossRefGoogle Scholar
  3. 3.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: International Conference on World Wide Web, pp. 107–117. Elsevier Science Publishers B. V. (1998)Google Scholar
  4. 4.
    Catledge, L.D., Pitkow, J.E.: Characterizing browsing strategies in the world-wide web. Computer Networks and ISDN Systems 27(6), 1065–1073 (1995)CrossRefGoogle Scholar
  5. 5.
    Cha, M., Mislove, A., Gummadi, K.P.: A measurement-driven analysis of information propagation in the flickr social network. In: International Conference on World Wide Web, pp. 721–730. ACM (2009)Google Scholar
  6. 6.
    Chi, E.H., Pirolli, P., Pitkow, J.: The scent of a site: a system for analyzing and predicting information scent, usage, and usability of a web site. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 161–168. ACM (2000)Google Scholar
  7. 7.
    Chi, E.H., Pirolli, P.L.T., Chen, K., Pitkow, J.: Using information scent to model user information needs and actions and the web. In: Conference on Human Factors in Computing Systems, pp. 490–497. ACM (2001). http://doi.acm.org/10.1145/365024.365325
  8. 8.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090. ACM (2011)Google Scholar
  9. 9.
    Cramer, H., Rost, M., Holmquist, L.E.: 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, pp. 57–66. ACM (2011)Google Scholar
  10. 10.
    Crandall, D.J., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: International Conference on World Wide Web, pp. 761–770. ACM (2009)Google Scholar
  11. 11.
    De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., Yu, C.: Automatic construction of travel itineraries using social breadcrumbs. In: Conference on Hypertext and Hypermedia, pp. 35–44. ACM (2010)Google Scholar
  12. 12.
    Gallegos, L., Lerman, K., Huang, A., Garcia, D.: Geography of emotion: Where in a city are people happier? arXiv preprint arXiv:1507.07632 (2015)
  13. 13.
    Girardin, F., Calabrese, F., Fiore, F.D., Ratti, C., Blat, J.: Digital footprinting: Uncovering tourists with user-generated content. Pervasive Computing 7(4), 36–43 (2008)CrossRefGoogle Scholar
  14. 14.
    Girardin, F., Fiore, F.D., Ratti, C., Blat, J.: Leveraging explicitly disclosed location information to understand tourist dynamics: a case study. Journal of Location Based Services 2(1), 41–56 (2008)CrossRefGoogle Scholar
  15. 15.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  16. 16.
    Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., Ratti, C.: Geo-located twitter as proxy for global mobility patterns. Cartography and Geographic Information Science 41(3), 260–271 (2014)CrossRefGoogle Scholar
  17. 17.
    Huberman, B.A., Pirolli, P.L.T., Pitkow, J.E., Lukose, R.M.: Strong regularities in world wide web surfing. Science 280(5360), 95–97 (1998). http://www.sciencemag.org/content/280/5360/95.abstract CrossRefGoogle Scholar
  18. 18.
    Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., Banchs, R.: Bicycle cycles and mobility patterns-exploring and characterizing data from a community bicycle program. arXiv preprint arXiv:0810.4187 (2008)
  19. 19.
    Kass, R.E., Raftery, A.E.: Bayes factors. Journal of the American Statistical Association 90(430), 773–795 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Kruschke, J.: Doing Bayesian data analysis: A tutorial introduction with R. Academic Press (2010)Google Scholar
  21. 21.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., et al.: Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web (2014)Google Scholar
  22. 22.
    Mahdisoltani, F., Biega, J., Suchanek, F.: Yago3: a knowledge base from multilingual wikipedias. In: 7th Biennial Conference on Innovative Data Systems Research, CIDR 2015 (2014)Google Scholar
  23. 23.
    Marlow, C., Naaman, M., Boyd, D., Davis, M.: Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: Conference on Hypertext and Hypermedia, pp. 31–40. ACM (2006)Google Scholar
  24. 24.
    Mislove, A., Koppula, H.S., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Growth of the flickr social network. In: Workshop on Online Social Networks, pp. 25–30. ACM (2008)Google Scholar
  25. 25.
    Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. ICwSM 11, 70–573 (2011)Google Scholar
  26. 26.
    Noulas, A., Shaw, B., Lambiotte, R., Mascolo, C.: Topological properties and temporal dynamics of place networks in urban environments. arXiv:1502.07979 [cs.SI] (2015)
  27. 27.
    Peng, C., Jin, X., Wong, K.C., Shi, M., Liò, P.: Collective human mobility pattern from taxi trips in urban area. PloS one 7(4), e34487 (2012)CrossRefGoogle Scholar
  28. 28.
    Pirolli, P.L.T., Pitkow, J.E.: Distributions of surfers’ paths through the world wide web: Empirical characterizations. World Wide Web 2(1–2), 29–45 (1999)CrossRefGoogle Scholar
  29. 29.
    Rekimoto, J., Miyaki, T., Ishizawa, T.: LifeTag: WiFi-based continuous location logging for life pattern analysis. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 35–49. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  30. 30.
    Sen, R., Hansen, M.: Predicting a web user’s next access based on log data. Journal of Computational Graphics and Statistics 12(1), 143–155 (2003). http://citeseer.ist.psu.edu/sen03predicting.html MathSciNetCrossRefGoogle Scholar
  31. 31.
    Sigurbjörnsson, B., Van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: International Conference on World Wide Web, pp. 327–336. ACM (2008)Google Scholar
  32. 32.
    Singer, P., Helic, D., Hotho, A., Strohmaier, M.: Hyptrails: a bayesian approach for comparing hypotheses about human trails on the web. In: International Conference on World Wide Web (2015)Google Scholar
  33. 33.
    Singer, P., Helic, D., Taraghi, B., Strohmaier, M.: Detecting memory and structure in human navigation patterns using markov chain models of varying order. PloS one 9(7), e102070 (2014)CrossRefGoogle Scholar
  34. 34.
    Sinnott, R.W.: Virtues of the haversine. Sky and Telescope 68(2), 158 (1984)MathSciNetGoogle Scholar
  35. 35.
    Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010). http://www.sciencemag.org/cgi/content/abstract/327/5968/1018 MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Tai, C.H., Yang, D.N., Lin, L.T., Chen, M.S.: Recommending personalized scenic itinerarywith geo-tagged photos. In: International Conference on Multimedia and Expo, pp. 1209–1212. IEEE (2008)Google Scholar
  37. 37.
    Walk, S., Singer, P., Noboa, L.E., Tudorache, T., Musen, M.A., Strohmaier, M.: Understanding how users edit ontologies: comparing hypotheses about four real-world projects. In: International Semantic Web Conference (2015)Google Scholar
  38. 38.
    Walk, S., Singer, P., Strohmaier, M.: Sequential action patterns in collaborative ontology-engineering projects: a case-study in the biomedical domain. In: International Conference on Conference on Information & Knowledge Management. ACM (2014)Google Scholar
  39. 39.
    West, R., Leskovec, J.: Human wayfinding in information networks. In: International Conference on World Wide Web, pp. 619–628. ACM (2012). http://doi.acm.org/10.1145/2187836.2187920
  40. 40.
    White, R.W., Huang, J.: Assessing the scenic route: measuring the value of search trails in web logs. In: Conference on Research and Development in Information Retrieval, pp. 587–594. ACM (2010)Google Scholar
  41. 41.
    Wiehe, S.E., Carroll, A.E., Liu, G.C., Haberkorn, K.L., Hoch, S.C., Wilson, J.S., Fortenberry, J.D.: Using gps-enabled cell phones to track the travel patterns of adolescents. International Journal of Health Geographics 7(1), 22 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Martin Becker
    • 1
  • Philipp Singer
    • 2
  • Florian Lemmerich
    • 2
  • Andreas Hotho
    • 1
  • Denis Helic
    • 3
  • Markus Strohmaier
    • 2
    • 4
  1. 1.University of WuerzburgWuerzburgGermany
  2. 2.GESIS - Leibniz Institute for the Social SciencesMannheimGermany
  3. 3.Graz University of TechnologyGrazAustria
  4. 4.University of Koblenz-LandauMainzGermany

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