Designing a Mobile Behavior Sampling Tool for Spatial Analytics

  • Shin’ichi KonomiEmail author
  • Tomoyo Sasao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10922)


In this paper, we build on our previous research [1, 4] to explore techniques and tools for collecting detailed behavioral data in large public spaces by deploying a small number of technology-armed researchers who act according to mobile notifications. To go beyond the limitations to conventional urban sensing, we first examine the challenges of human-in-the-loop sensing. We then propose a mobile behavior sampling tool based on smart notifications so as to address the challenge of in-situ sampling.


Mobile behavior sampling Spatial analytics 



This work was supported by JSPS KAKENHI Grant Numbers JP17909134 and JP17865988.


  1. 1.
    Hemminki, S., Kuribayashi, K., Konomi, S., Nurmi, P., Tarkoma, S.: Quantitative evaluation of public spaces using crowd replication. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2016), San Francisco, CA, 31 October–3 November 2016. ACM Press, New York (2016).
  2. 2.
    Konomi, S., Ohno, W., Sasao, T., Shoji, K.: A context-aware approach to microtasking in a public transport environment. In: Proceedings of the 5th IEEE International Conference on Communications and Electronics, Special Session on Crowdsourcing and Crowdsourcing Applications, Da Nang, 30 July–1 August 2014, pp. 498–503. IEEE, Piscataway (2014). ISBN: 978-1479950492
  3. 3.
    Konomi, S., Sasao, T.: Crowd geofencing. In: Proceedings of the 2nd EAI International Conference on IoT in Urban Space (Urb-IoT 2016), Tokyo, Japan, 24–25 May 2016, pp. 14–17. ACM Press, New York (2016). ISBN: 978-1-4503-4204-9
  4. 4.
    Sasao, T., Konomi, S.: The use of historical information to support civic crowdsourcing. In: Streitz, N., Markopoulos, P. (eds.) DAPI 2016. LNCS, vol. 9749, pp. 470–481. Springer, Cham (2016). Scholar
  5. 5.
    Sasao, T., Konomi, S., Arikawa, M., Fujita, H.: Context weaver: awareness and feedback in networked mobile crowdsourcing tools. Comput. Netw. Int. J. Comput. Telecommun. Netw. 90, 74–84 (2015). Special Issue on CrowdsourcingCrossRefGoogle Scholar
  6. 6.
    Sasao, T., Konomi, S., Kostakos, V., Kuribayashi, K., Goncalves, J.: Community reminder: participatory contextual reminder environments for local communities. Int. J. Hum. Comput. Stud. 102, 41–53 (2017). Scholar
  7. 7.
    Sasao, T., Konomi, S., Kuribayashi, K.: Activity recipe: spreading cooperative outdoor activities for local communities using contexual reminders. In: Streitz, N., Markopoulos, P. (eds.) DAPI 2015. LNCS, vol. 9189, pp. 590–601. Springer, Cham (2015). ISBN: 978-3319208039CrossRefGoogle Scholar
  8. 8.
    Benenson, R., Mathias, M., Timofte, R., Van Gool, L: Pedestrian detection at 100 frames per second. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 2903–2910 (2012)Google Scholar
  9. 9.
    Youssef, M., Mah, M., Agrawala, A.: Challenges: device-free passive localization for wireless environments. In: Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking (MobiCom 2007), pp. 222–229 (2007)Google Scholar
  10. 10.
    Raja, R., Exler, A., Hemminki, S., Konomi, S., Sigg, S., Inoue, S.: Towards geospatial emotional perception. Geoinform. J. (2017).
  11. 11.
    Hall, E.T.: The Hidden Dimension. Anchor Books, New York (1966)Google Scholar
  12. 12.
    Rittel, H.: Dilemmas in a general theory of planning. Policy Sci. 4(2), 155–169 (1973). Scholar
  13. 13.
    Spradley, J.P.: Participant Observation. Harcourt Brace Jovanovich College Publishers, New York (1980)Google Scholar
  14. 14.
    Altmann, J.: Observational study of behavior: sampling methods. Behavior 49(3), 227–266 (1974)CrossRefGoogle Scholar
  15. 15.
    Thompson, S.K.: Adaptive cluster sampling. J. Am. Stat. Assoc. 85(412), 1050–1059 (1990)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Smith, M.J., Goodchild, M.F., Longley, P.A.: Geospatial Analysis, 5th edn. Prentice Hall, Upper Saddle River (2015)Google Scholar
  17. 17.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995)CrossRefGoogle Scholar
  18. 18.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci. 39(1), 1–24 (2005)CrossRefGoogle Scholar
  19. 19.
    Thepvilojanapong, N., Konomi, S., Tobe, Y.: A study of cooperative human probes in urban sensing environments. IEICE Trans. Commun. E93-B(11), 2868–2878 (2010). Special Section on Fundamental Issues on Deployment of Ubiquitous Sensor NetworksCrossRefGoogle Scholar
  20. 20.
    Ferreira, D., Kostakos, V., Dey, A.K.: AWARE: mobile context instrumentation framework. Front. ICT 2 (2015). 6 pagesGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Kyushu UniversityFukuokaJapan
  2. 2.Tokushima UniversityTokushimaJapan

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