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Where to catch ‘em all? – a geographic analysis of Pokémon Go locations

机译:在哪里捕捉他们? - 神奇宝贝的地理分析

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摘要

In 2016, Niantic Labs released Pokémon Go, an augmented reality smartphone game that attracted millions of users worldwide. This game allows users to “catch” Pokémons through their mobile cameras in different geographic locations that often correspond to prominent places. This paper analyzes the distribution of PokéStops, Pokémon gyms, and spawnpoints in selected urban areas of South Florida and Boston. It identifies which socioeconomic variables and land-use categories affect the density of PokéStops, and how PokéStops and gyms cluster relative to each other. Using nearest neighbor analysis, this paper assesses also how actual PokéStop locations are reflected in Yelp’s “PokéStop nearby” attribute. Results show that black and Hispanic neighborhoods are disadvantaged when it comes to crowd-sourced data coverage, that PokéStops occur more frequently in commercial, recreational and touristic sites and around universities, and that PokéStops tend to cluster around gyms. The latter suggests that these point sets were generated by a similar location selection process. To mitigate geographically linked biases, future versions of augmented reality and geo-games should aim to make them equally accessible in all areas, for example by placing extra resources, such as points of interest, in neighborhoods that are currently underrepresented in data coverage.
机译:2016年,NiantiC Labs发布了神奇宝贝,这是一个增强现实智能手机游戏,吸引了全球数百万用户。此游戏允许用户在不同地理位置中通过其移动摄像机“捕获”Pokémons,这些地点通常与突出位置相对应。本文分析了南佛罗里达州南部和波士顿的选定城区的Pokéstops,神奇宝贝健身房和产量的分布。它识别哪些社会经济变量和土地使用类别影响Pokéstops的密度,以及Pokéstops和健身房集群如何相互作用。使用最近的邻分析,本文还评估了实际的Pokéstop位置如何反映在Yelp的“附近”属性中的yelp的“Pokéstop”。结果表明,当涉及人群资源的数据覆盖范围时,黑人和西班牙裔社区是处于不利地位的,该娱乐会在商业,娱乐和旅游站点和大学周围发生频繁,倾向于聚集在健身房。后者表明这些点集由类似的位置选择过程生成。为了缓解地理上链接的偏见,未来的增强现实和地理游戏的旨在使他们在所有领域同样地访问它们,例如通过将额外的资源(例如兴趣点)置于目前在数据覆盖中所谓的邻居中。

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