Abstract
The worldwide Pokémon Go fever has brought location spoofing into the public’s spotlight. Location spoofing is an intentional act to masquerade locational information to somewhere other than the actual location where a network communication takes place. In the realm of Cartography and GIScience, compared with well-studied spatial quality issues, our knowledge of location spoofing is still quite limited. By reviewing five frequently-conducted location spoofing techniques for Pokémon Go, this paper critically examines location spoofing as an emerging spatial data quality issue. To unveil the hidden motivation for location spoofing, we discuss the uneven spatial distribution of game rewards through mapping a large volunteered data set of worldwide Pokémons, and gaze at the major actors in the location-based game, including location spoofers, game designers, hackers and bots. Though the research scope of this paper lies in a location-based game per se, location spoofing widely exists in Internet applications and is not fundamentally different from other deceptive phenomena in the real world. We encourage cartographers and GIScientists to face this spoofing phenomena head-on in order to promote more effective and trustworthy uses of geospatial big data.
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References
Callon, M. (1999). Actor-network theory—The market test. The Sociological Review, 47(S1), 181–195.
Cypher, M., & Richardson, I. (2006). An actor-network approach to games and virtual environments. In Proceedings of the 2006 International Conference on Game research and Development. ACM International Conference Proceeding Series, 223. Retrieved January, 30, 2017 from http://dl.acm.org/citation.cfm?id=1234382.
Gilbert, B. (2016). Pokémon Go has been downloaded over 500 million times. Retrieved Janurary, 30, 2017 from http://www.businessinsider.com/pokemon-go-500-million-downloads-2016-9.
Harvey, A. (2016). SKYLIFT-WiFi SSID geolocation emulator. Retrieved Janurary 30, 2017, from https://ahprojects.com/projects/skylift/.
Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., & Case, T. J. (2013). Spatial uncertainty in ecology: Implications for remote sensing and GIS applications. Springer Science & Business Media.
Jenner, M. (2014). Is this TVIV? On Netflix, TVIII and binge-watching. New Media & Society doi:10.1177/1461444814541523.
Kang, A. R., Woo, J., Park, J., & Kim, H. K. (2013). Online game bot detection based on party-play log analysis. Computers & Mathematics with Applications, 65(9), 1384–1395.
Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Harvard university press.
Latour, B. (1999). Pandora’s hope: Essays on the reality of science studies. Harvard University Press.
Law, J., & Hassard, J. (1999). Actor network theory and after.
Lovelace, R., Birkin, M., Cross, P., & Clarke, M. (2016). From big noise to big data: Toward the verification of large data sets for understanding regional retail flows. Geographical Analysis, 48(1), 59–81.
Muoio, D. (2016). One ‘Pokémon GO’ player had a genius idea to try and catch ‘em all. Retrieved Janurary, 30, 2017 from http://www.businessinsider.com/man-uses-drone-to-catch-pokemon-in-pokemon-go-2016-7.
O’Hara, K. (2008). Understanding geocaching practices and motivations. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
Przybylski, A. K., Rigby, C. S., & Ryan, R. M. (2010). A motivational model of video game engagement. Review of General Psychology, 14(2), 154.
Schurgot, M. R., Shinberg, D. A., & Greenwald, L. G. (2015). Experiments with security and privacy in IoT networks. Paper presented at the World of Wireless, Mobile and Multimedia Networks (WoWMoM) 2015 IEEE 16th International Symposium on a. 1–6.
Sofka, S. (2016). TRNDLabs Pokédrone will solve some of your Pokémon go problems. Retrieved Janurary, 30, 2017 from http://nerdist.com/trndlabs-pokedrone-will-solve-some-of-your-pokemon-go-problems/.
Sommers, J., & Barford, P. (2012). Cell vs. WiFi: on the performance of metro area mobile connections. Paper presented at the Proceedings of the 2012 ACM Conference on Internet Measurement Conference.
Wetzel, R., Blum, L., & Oppermann, L. (2012). Tidy city: A location-based game supported by in-situ and web-based authoring tools to enable user-created content. Paper presented at the Proceedings of the International Conference on the Foundations of Digital Games.
Zaheer, S., & Nachum, L. (2011). Sense of place: From location resources to MNE locational capital. Global Strategy Journal, 1(1–2), 96–108.
Zhao, B., & Sui, D. Z. (2017). True lies in geospatial big data: Detecting location spoofing in social media. Annals of GIS. 1–14 doi:10.1080/19475683.2017.1280536.
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Zhao, B., Chen, Q. (2017). Location Spoofing in a Location-Based Game: A Case Study of Pokémon Go. In: Peterson, M. (eds) Advances in Cartography and GIScience. ICACI 2017. Lecture Notes in Geoinformation and Cartography(). Springer, Cham. https://doi.org/10.1007/978-3-319-57336-6_2
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DOI: https://doi.org/10.1007/978-3-319-57336-6_2
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