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Following the Voice of the Crowd: Exploring Opportunities for Using Global Voting Data to Enrich Local Urban Context

  • Martin Traunmueller
  • Ava Fatah gen. Schieck
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 369)

Abstract

With the introduction of the internet to the public and the rise of digital technologies we experience a shift in our understanding of space. Mobile devices and ubiquitous computing in urban landscape make the physicality of distance disappear – the modern citizen is digitally connected to everybody at anytime and anywhere. The result of this network is a highly globalized world which effects economy same as personal interests and decisions of its inhabitants. The introduction of web 3.0 with its methods of comment, recommender and voting systems offers a broad platform for people all over the world to share experiences and exchange opinions about an unlimited variety of topics. Global opinions meet local interests.

In this paper we explore the possibilities of using global voting data to enrich locally the modern citizen’s urban walking experience. We describe a new approach to wayfinding by implementing methods of digital recommender systems into the physical world. We investigate Facebook voting data to generate an alternative to the shortest route, as suggested by common route finder systems, in order to maximize the pleasure of an urban stroll. The testing of the system in a real world context together with collected feedback stimulate the discussions.

Keywords

Wayfinding Urban Pedestrian Navigation Social Networks Voting data Mobile Devices Recommendation Systems 

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References

  1. 1.
    Coyne, R.: The Tuning of Place: Sociable Spaces and Pervasive Digital Media. MIT Press, Cambridge (2010)Google Scholar
  2. 2.
    Debord, G.: Internationale Situationniste #1, Translated by Ken Knabb. Paris (1958)Google Scholar
  3. 3.
    Dörk, M., Carpendale, S., Williamson, C.: The Information Flaneur: A Fresh Look at Information Seeking. In: Proc. CHI 2011, pp. 1215–1224. ACM (2011)Google Scholar
  4. 4.
    Gen, F., Schieck, A., Penn, A., O’Neill, E.: Mapping, sensing and visualizing the digital co-presence in the public arena. In: DDSS, pp. 38–58, NLpages (2008)Google Scholar
  5. 5.
    Gordon, E., de Souza e Silva, A.: Net Locality – Why Location matters in a net-worked World. John Wiley & Sons Ltd. (2011)Google Scholar
  6. 6.
    Hiller, B., Penn, A., Hanson, J., Grajewski, T., Xu, J.: Natural movement: or, con-figuration and attraction in urban pedestrian movement. Environment and Planning B: Planning and Design 20, 29–66 (1993)Google Scholar
  7. 7.
    Hillier, B., Hanson, J.: The Social Logic of Space. CUP, Cambridge (1984)CrossRefGoogle Scholar
  8. 8.
    Kirman, B.: “Get Lost, GetLostBot!” Annoying people by offering recommendations when they are not wanted. In: Proc. LocalPeMA 2012, pp. 19–20. ACM Press (2012)Google Scholar
  9. 9.
    Kirman, B., Linehan, C., Lawson, S.: Get lost: facilitating serendipitous exploration in location-sharing services. In: Proc. CHI 2012, pp. 2303–2308. ACM Press (2012)Google Scholar
  10. 10.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer (2001)Google Scholar
  11. 11.
    Krüger, A., Aslan, I., Zimmer, H.: The effects of mobile pedestrian navigation systems on the concurrent acquisition of route and survey knowledge, pp. 39–60. Mobile Human-Computer Interaction–MobileHCI (2004)Google Scholar
  12. 12.
    Lindqvist, J., Cranshaw, J., Wiese, J., Hong, H., Zimmerman, J.: I’m the Mayor of My House: Examining Why People Use foursquare. In: Proc. CHI 2011, pp. 2409–2418. ACM Press (2011)Google Scholar
  13. 13.
    Lynch, K.: The Image of the City. MIT Press (1960)Google Scholar
  14. 14.
    Pariser, E.: The Filter Bubble: What the Internet is hiding from you. Penguin Press HC (2011)Google Scholar
  15. 15.
    Raubal, M., Winter, S.: Enriching Wayfinding Instructions with Local Landmarks. In: Egenhofer, M., Mark, D.M. (eds.) GIScience 2002. LNCS, vol. 2478, pp. 243–259. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Schoening, J., Hecht, B., Starosielski, N.: Evaluating automatically generated loca-tion-based stories for tourists. In: Proc. CHI 2008, pp. 2937–2943. ACM Press (2008)Google Scholar
  17. 17.
    Shang, S., Hui, P., Kukami, S.R., Cuff, P.W.: Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models. In: Proc. ICPADS 2011, pp. 835–840. IEEE (2011)Google Scholar
  18. 18.
    Shepard, M.: Sentient City: ubiquitous computing, architecture, and the future of urban space. The MIT Press (2011)Google Scholar
  19. 19.
    Traunmueller, M., Gkougkoustamos, S., Tang, Y.: Modelling mediated Urban Space through geo located social Microblogging. In: Proc. Mediacity 4: Mediacities 2013, Buffalo, NY (in press, 2013)Google Scholar
  20. 20.
    Turner, A., Penn, A.: Encoding natural movement as an agent-based system: an investigation into human pedestrian behavior in the built environment. Environment and Planning B: Planning and Design 29, 473–490 (2002)CrossRefGoogle Scholar
  21. 21.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Martin Traunmueller
    • 1
  • Ava Fatah gen. Schieck
    • 1
  1. 1.UCL The Bartlett, University College LondonLondonUK

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