Rethinking Spatial Data Quality: Pokémon Go as a Case Study of Location Spoofing

Abstract

The worldwide surge of the location-based game Pokémon Go since mid-2016 has raised debates among online gaming communities and the general public on the unique phenomenon of location spoofing. Location spoofing enables gamers to engage in remote activities by using simulated locational information. This has been largely considered a threat to the underlying fairness of the game and thus to the social order of both online game communities and the real world. This article takes up location spoofing as a unique case to engage the spatial data quality issue, an ever more fundamentally important issue in geography in today’s era of big data. Unlike the well-examined systematic error, outliers, and uncertainty in spatial data, our understanding of user-generated spatial data from location spoofing is rather limited. To fill this gap and advance the debates on the spatial data quality issue, we collected a data set of Pokémon and applied actor–network theory to examine the various human and nonhuman agents involved, including gamers, spoofers, game developers, bots, and hackers. We argue that location spoofing in mobile games and other increasingly location-based services (e.g., Uber) should not be merely interpreted as fake data but should be taken seriously as real human geographic data in new spatial (re)assemblages proliferating nowadays. We encourage scholars in geography and neighboring fields to investigate this emerging issue in location-based services, with the goal of promoting a more critical and holistic understanding of geospatial big data.