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The Geography of Pokémon GO: Beneficial and Problematic Effects on Places and Movement
Ashley Colley, Jacob Thebault-Spieker, Allen Yilun Lin, Donald Degraen, Benjamin Fischman, Jonna Häkkilä, Kate Kuehl, Valentina Nisi, Nuno Jardim Nunes, Nina Wenig, Dirk Wenig, Brent Hecht, Johannes Schöning
TL;DR
The paper asks how Pokémon GO changed movement and which places it advantages, addressing geographic effects of location-based gaming at scale. It combines a five-country survey of 375 players with U.S. geostatistical analysis and finds large-scale new-location visits alongside substantial urban, racial, and ethnic inequalities. The findings motivate safer and less biased geographic design strategies, while remaining partly bounded by U.S.-focused analyses and nonrepresentative survey sampling.
Problem
The paper investigates how the widespread adoption of location-based gaming affects movement and places, questions previously hypothesized to have substantial geographic effects.
Method
The study combines field surveys of 375 players across five countries with U.S. geostatistical analysis of PokéStop distributions using spatial Durbin modeling.
Results
Pokémon GO causes people to visit new locations at a remarkable scale while heavily advantaging urban places with few minorities and their residents.
Takeaways & Limitations
The findings identify design strategies that could counter racial, ethnic, and urban bias and help players avoid distraction-related safety risks.
Takeaways & Limitations
The geostatistical analyses cover specific U.S. regions, and the field survey used nonrepresentative location selection rather than formal geographic sampling.
Abstract
from arXiv · showhide
The widespread popularity of Pokémon GO presents the first opportunity to observe the geographic effects of location-based gaming at scale. This paper reports the results of a mixed methods study of the geography of Pokémon GO that includes a five-country field survey of 375 Pokémon GO players and a large scale geostatistical analysis of game elements. Focusing on the key geographic themes of places and movement, we find that the design of Pokémon GO reinforces existing geographically-linked biases (e.g. the game advantages urban areas and neighborhoods with smaller minority populations), that Pokémon GO may have instigated a relatively rare large-scale shift in global human mobility patterns, and that Pokémon GO has geographically-linked safety risks, but not those typically emphasized by the media. Our results point to geographic design implications for future systems in this space such as a means through which the geographic biases present in Pokémon GO may be counteracted.
Author Keywords
The paper uses Pokémon GO’s widespread popularity to examine how location-based gaming affects movement and places. It finds large-scale new-location visits alongside distraction-related risks and strong urban, racial, and ethnic geographic advantages.
- The study asks how Pokémon GO changed human movement and which types of places it advantaged or disadvantaged.
- The mixed-methods design combined field surveys of 375 players across five countries with U.S. geostatistical analysis of PokéStop distributions.
- Pokémon GO causes people to visit new locations at a remarkable scale, although this movement is associated with distraction-related risk.
- Urban places and neighborhoods with few minorities receive substantial advantages from Pokémon GO’s design.
- Predominately white non-Hispanic urban neighborhoods have 20 more PokéStops per square kilometer than urban areas with very large minority populations.The 20 PokéStops per square kilometer is approximately 4 times the overall mean density.
- The findings suggest geographic design strategies can counter racial, ethnic, and urban bias and reduce distraction-related safety risks.
BACKGROUND AND RELATED WORK
Prior geography and HCI research suggests that digital and crowdsourced systems often reproduce existing spatial inequalities. Earlier location-based-game research also motivates examining movement, distraction, and visits to unfamiliar places.
- Geographers argue that augmented-reality data and code can remake places while reinforcing preexisting power structures.
- Research on Wikipedia, OpenStreetMap, and related geographic crowdsourcing finds better coverage in advantaged urban areas than in disadvantaged rural areas.
- Location-based gaming research has examined technical, mechanical, narrative, health, social, and geographic dimensions for more than a decade.
- Prior work found that distraction made movement more difficult while playing location-based games, motivating the paper’s inquiry into Pokémon GO safety.
- Ingress research reported increased local physical activity, global virtual participation, and previously unvisited-location visits among players.A survey cited in the paper found that 88% of Ingress players had visited previously unvisited locations while playing.
Pokémon GO Background
Pokémon GO is a mobile location-based game in which players find, capture, and battle virtual creatures appearing at real-world locations. Fixed PokéStops provide gameplay resources, making higher local density advantageous.
- Pokémon GO uses mobile positioning to place virtual creatures in the player’s real-world surroundings for locating, capturing, and battling.
- Pokémon temporarily spawn at locations, and catching them is a primary way players progress.
- PokéStops are fixed physical-world locations that provide Poké Balls, Potions, and experience points, with high spawning frequency under certain conditions.
- Players must wait at least five minutes before revisiting a PokéStop, while higher regional PokéStop density generally benefits players.
- PokéStop locations originated from Ingress portals seeded with crowdsourced historical markers and places such as churches, parks, monuments, and public art.
- The game provides limited information about its algorithms, and the map shows PokéStops and gyms within approximately a 3 km radius.
METHODS
The paper combines multinational field interviews with U.S. geostatistical analysis to study place and movement in Pokémon GO. It uses player reports and PokéStop density as complementary geographic evidence.
- The research uses a mixed-methods design combining a multinational field survey with U.S. geostatistical analysis of PokéStop distribution.
- The survey addressed place and movement through questions about boring or exciting game-related places and player experiences.
- The field study occurred from July 22 to August 5, 2016, across the USA, Germany, Portugal, Finland, and Belgium.
- Interviewers selected observed player locations using local knowledge, spaced subsequent sites at least 1km apart, and obtained 375 valid interviews.
- Free-text responses were open-coded by two evaluators, with disagreements arbitrated by a third researcher and repeated mentions within categories counted once.
- PokéStop density, measured as PokéStops per square kilometer, served as the core dependent variable and a proxy for in-game advantage.
- Spatial Durbin modeling was used to address spatial autocorrelation when analyzing geographic relationships in PokéStop density.
PokéStop Data
The study collected PokéStop locations while minimizing server impact, then analyzed geographic advantages through urban–rural and racial/ethnic lenses. Restricted access required regionally focused analyses, limiting conclusions largely to the United States and, for some analyses, Chicago and Detroit.
- Data collection: PokéStop locations were captured from Niantic using a customized program based on pgoapi and PokémonGo-Map.The program took a U.S. county’s minimum bounding rectangle as input and recorded all PokéStops within it.
- Data collection: The researchers collected only data essential to their questions and minimized requests because permission under Niantic’s terms was unclear.Requests were issued once every ten seconds, followed by a one-minute pause after every 15 requests.
- Scope: Restricted collector speed forced geographically focused analyses, so conclusions are limited to the United States and sometimes Chicago and Detroit.The paper calls for future investigation from a more international perspective.
- Analytic lenses: The analysis examined geographic advantage through urban–rural and racial/ethnic demographic differences identified in prior GeoHCI research.Race and ethnicity used the Census percentage of residents who were white and non-Hispanic; urban/rural analysis used government-defined classes.
- Analytic lenses: The researchers sampled 20 counties from each NCHS urban–rural class and focused racial and ethnic analyses on Chicago and Detroit.Detroit was added because it is a poorer metropolitan area with a large minority population, while Chicago had precedent in related work.
Geostatistical Modeling
The geostatistical analysis used descriptive statistics for urban–rural comparisons and spatial Durbin models for urban race and ethnicity analyses. Spatial autocorrelation required modeling relationships between tract demographics, nearby PokéStop density, and neighboring areas.
- Urban–rural analysis: PokéStop density across NCHS urban–rural classes was analyzed with descriptive statistics because the random county sets lacked spatial autocorrelation.The urban–rural analysis used 20 randomly selected counties from each NCHS class.
- Race and ethnicity analysis: Urban race and ethnicity analysis required different methods because spatial autocorrelation linked conditions in one area with PokéStop density in neighboring areas.Reported player travel over non-trivial distances made accounting for spatial dependence important.
- Race and ethnicity analysis: Spatial Durbin models were used because spatial error and spatial lag models did not capture demographic effects on nearby PokéStop density.The paper identifies Spatial Durbin models as an emerging geostatistical best practice for this dependence.
- Model specification: The Spatial Durbin models analyzed census tracts in Chicago and Detroit using non-Hispanic white population share as the primary independent variable.Log-scaled population density was included as a control, and PokéStop density was measured in PokéStops per square kilometer.
- Model interpretation: Model interpretation focused on direct and indirect effects rather than conventionally interpreted fitted coefficients.Direct effects describe relationships within a tract, while indirect effects describe relationships involving other areas.
RESULTS
Pokémon GO reinforces geographic inequalities in place access while redirecting players toward new destinations and shaping spending and social movement. Its risks arise primarily from distracted movement, especially near road traffic.
- Urban-Rural Differences: 2.9 PokéStops/km2 in core urban counties versus 0.03 in rural class 6 counties, a significant difference (t(19)=4.2, p < 0.001).The randomized county-level analysis found PokéStop density decreased dramatically as counties became more rural.
- Urban-Rural Differences: 15% of respondents described rural areas as boring for Pokémon GO, including locations with little or no game content.Survey responses suggest rural disadvantage can make the game somewhat unplayable in rural areas.
- Race and Ethnicity: A Detroit tract moving from 0% to 100% white non-Latino was associated with an increase of 26.8 PokéStops/km2, while Chicago neighbors changing similarly implied a 21.6 PokéStops/km2 indirect increase.Mean overall density was 5.7 in Detroit and 17.6 in Chicago, providing context for these modeled effects.
- Race and Ethnicity: PokéStop density was lower in minority neighborhoods but not necessarily in low-income neighborhoods, with no significant income effects detected in Chicago or Detroit.The income trends were much smaller, around 4 PokéStops per square kilometer, despite strong income-race associations.
- Movement and Spending: 17% of players first visited the survey location because of Pokémon GO, and almost 60% visited at least one new place while playing.New destinations included parks, stadiums or castles, and water features; 9% visited an entirely new town or city.
- Movement and Spending: 46% purchased something near a venue because of Pokémon GO-related movement, while 70% said they never played alone and only 12% always played alone.Purchases commonly involved drinks or food, and players typically moved in pairs or groups rather than individually.
DISCUSSION
The discussion introduces design implications from the paper’s five findings, then places those implications within a broader interpretation of the results.
- The discussion first presents design implications emerging from the paper’s five findings, followed by broader discussion of the results.
“Geotechnical Design” for Location-based Gaming
Pokémon GO’s geographic bias appears to arise from its geotechnical design, especially the distribution of PokéStops through organically crowdsourced data. Designers can counteract these biases through targeted redistribution, supplementation, and geographic auditing.
- Pokémon GO’s severe geographic bias likely emerges from the geographic distribution of PokéStops rather than location-based gaming generally.
- Organic geographic crowdsourcing tends to produce demographically linked coverage biases, and Ingress-derived PokéStop placement advantages urban, white, non-Hispanic populations.
- Designers could favor reuse of game elements in undercovered areas by reducing PokéStop cooldowns or increasing rare-Pokémon spawn rates.
- Designers could supplement crowdsourced locations in underrepresented areas with public-space data or non-geographic review of Street View imagery.
- Once identified, location-based game bias may be easier to correct and scale than coverage bias in datasets such as Wikipedia and OpenStreetMap.
- Designers should audit the geographic distributions of important game elements using geostatistical methods such as spatial Durbin modeling.
Reducing Movement-associated Risks
The discussion frames movement-related safety as a design problem and proposes interventions to reduce distraction risks while preserving location-based gameplay. The paper’s mixed-methods approach allowed the field and geostatistical studies to inform one another during rapid data collection.
- Reducing Movement-associated Risks: Smartphone use while walking creates general urban risks, motivating location-based game designs that reduce distraction-related hazards.
- Reducing Movement-associated Risks: Avoiding game content across a road could reduce players’ desire to rush across busy streets.
- Reducing Movement-associated Risks: The game’s passenger acknowledgment could be extended to prevent gameplay in moving vehicles that might encourage rapid route changes.
- DISCUSSION: The researchers combined rapid field surveys and geostatistical analysis because Pokémon GO’s popularity created a potentially short-lived research opportunity.
- DISCUSSION: The two methods reinforced one another: survey findings shaped spatial modeling choices, while geostatistical results contextualized survey findings.
Limitations and Future Work
The paper’s evidence is geographically and conceptually bounded: geostatistical analysis covered selected U.S. regions, survey sampling was nonrepresentative, and movement and place were examined through targeted questions. The authors therefore call for broader geographic coverage, representative sampling, and further study of the geography of location-based gaming.
- The geostatistical analyses covered only selected U.S. regions, so future work should expand to more U.S. areas and other countries.
- International generalization remains uncertain because countries differ in geographic structures, histories, race and ethnicity, and urban-rural relationships.
- Interview sites were selected using local knowledge of active players rather than formal geographic sampling, limiting representativeness despite little reported behavioral variation across five countries.
- The study addressed only how movement changed and which places were advantaged or disadvantaged, leaving other geographic questions for future research.
- Some findings may not generalize beyond Pokémon GO and very similar games, although they provide early warnings for safer and less biased technologies.