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The Mordor Shaper—The Warsaw Participatory Experiment Using Gamification

  • Robert OlszewskiEmail author
  • Agnieszka Turek
Conference paper
  • 36 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1077)

Abstract

The article promotes the idea of using the ICT and AR technologies, geoinformation as well as the gamification method to develop so-called a ‘serious game’ and support the process of creating (geo)information societies in the smart city. The aim of developing this game is to optimise the issue of spatial development and public transport through the gamification stimulated ‘carpooling’, on the one hand, and to conduct a participatory experiment and study the ethical issues of using the ICT in an urban society, on the other. The test field used to carry out such an experiment is the office district of Warsaw called Mordor, where over one hundred thousand people are employed. They use mobile devices and the ICT technology on a daily basis and are impressionable to social gamification.

Keywords

Geoparticipation ICT Smart city Serious game Gamification Geoinformation Mordor Spatial data mining 

References

  1. 1.
  2. 2.
    Manville, C.: Mapping Smart Cities in the EU (2014)Google Scholar
  3. 3.
    Pink, D.H.: A Whole New Mind: Why Right-brainers Will Rule the Future. Riverhead Books (2005)Google Scholar
  4. 4.
    Kapp, K.: The Gamification of Learning and Instruction. Pfeiffer/Wiley (2012)Google Scholar
  5. 5.
    Kim, T.W.: gamification ethics: exploitation and manipulation. In: Proceedings of the ACM SIGCHI Gamifying Research Workshop (2015)Google Scholar
  6. 6.
    Olszewski, R., Wieszaczewska, A.: The application of modern geoinformation technologies in social geoparticipation. In: Gotlib, D., Olszewski, R. (eds.) Smart City. Spatial Information in Smart Cities Management. PWN, Warszawa (2016)Google Scholar
  7. 7.
    Salen, K., Zimmermann, G.: Rules of Play. MIT Press (2006)Google Scholar
  8. 8.
    Werbach, K.: (Re)Defining Gamification: A Process Approach. Persuasive Technology. Lecture Notes in Computer Science, vol. 8462, pp. 266–272 (2014)Google Scholar
  9. 9.
    Lattemann S., Robra-Bissantz C., Zarnekow S., Brockmann R., Stieglitz T. (eds.): Gamification. Using Game Elements in Serious Contexts. Springer International Publishing (2017)Google Scholar
  10. 10.
    Abt, C.: Serious Games. University Press of America (1970)Google Scholar
  11. 11.
    Adams, E., Dormans J.: Game Mechanics. Advanced Game Design. New Readers/Pearson Education (2012)Google Scholar
  12. 12.
    Aldrich, C.: The Complete Guide to Simulations and Serious Games. Pfeiffer/Wiley (2009)Google Scholar
  13. 13.
    Bartle, R.: Designing Virtual Worlds. New Riders (2003)Google Scholar
  14. 14.
    Bogost, I.: Persuasive Games. MIT Press (2007)Google Scholar
  15. 15.
    Kapp, K.M: The Gamification of Learning and Instruction: Game-Based Methods and Strategies for Training and Education, 1st edn. Pfeiffer, San Francisco, CA, USA, p. 336. (2012)Google Scholar
  16. 16.
    Olszewski R., Turek A.: Application of the spatial data mining methodology and gamification for the optimisation of solving the transport issues of the “Varsovian Mordor”. In: Tan, Y., Shi, Y. (eds.) Proceedings of Data Mining and Big Data. First International Conference, DMBD 2016 Lecture Notes in Computer Science, vol. 9714, pp. 103–114. Springer International Publishing,  https://doi.org/10.1007/978-3-319-40973-3_10 (2016)
  17. 17.
    Olszewski, R., Pałka, P., Turek, A.: Solving smart city transport problems by designing carpooling gamification schemes with multi-agent systems: the case of the so-called “Mordor of Warsaw”. Sensors 18, 1–25 (2018).  https://doi.org/10.3390/s18010141CrossRefGoogle Scholar
  18. 18.
    Berry, M.J.A.; Linoff, G.S.: Mastering data mining. In: The Art and Science of Customer Relationship Management. Wiley, New York, NY, USA (2000)Google Scholar
  19. 19.
    Fayyad, U.M.; Piatetsky-Shapiro, G.; Smyth, P.: From data mining to knowledge discovery in databases. In: AI Magazine; American Association for Artificial Intelligence: Menlo Park, CA, USA, vol. 17, pp. 37–54 (1996)Google Scholar
  20. 20.
    Miller, H.J., Han, J.: Geographic Data Mining and Knowledge Discovery. Taylor & Francis, London, UK (2001)CrossRefGoogle Scholar
  21. 21.
    Lu, W.; Han, J.; Ooi, B.C.: Discovery of general knowledge in large spatial databases. In: Proceedings of the Far East Workshop on GIS, Singapore, pp. 275–289 (1993)Google Scholar
  22. 22.
    McGraw, K.L., Harbison-Briggs, K.: Knowledge Acquisition: Principles and Guidelines. Prentice Hall, Englewood Cliffs, NJ, USA (1989)Google Scholar
  23. 23.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier Science & Technology, Saint Louis (2011)zbMATHGoogle Scholar
  24. 24.
    Loh, C.S., Yanyan, S., Ifenthaler, D.: Serious Games Analytics. Methodologies for Performance Measurement, Assessment, and Improvement. Springer (2015)Google Scholar
  25. 25.
    Olszewski, R., Turek, A.: Using fuzzy geoparticipation methods to optimize the spatial development process in a smart city. In: Proceedings: 4th IEEE International Conference on Collaboration and Internet Computing. CIC 2018, pp. 430–437.  https://doi.org/10.1109/cic.2018.00065 (2018)
  26. 26.
    Zimbardo, P.G., Maslach, C., Haney, C.: Reflections on the stanford prison experiment: genesis, transformations, consequences. In: T. Blass (eds.) Obedience to authority: Current Perspectives on the Milgram Paradigm pp. 193–237. Lawrence Erlbaum Associates, Mahwah, N.J. (2000)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of Geodesy and CartographyWarsaw University of TechnologyWarsawPoland

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