Impact of Location-based Games on Phone Usage and Movement: A Case ´ Study on Pokemon GO Ionut Andone Konrad Blaszkiewicz University of Bonn Bonn, Germany
[email protected] [email protected] shared co-first authorship
Alexander Markowetz markowetz.de Bonn, Germany
[email protected]
Matthias Böhmer Technische Hochschule Köln Köln, Germany
[email protected]
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Abstract Pokémon GO was a short lived mobile location-based gaming phenomenon. After its launch in July 2016, it quickly reached 500 million installs, but afterwards interest faded. As part of a large scale "in the wild" mobile phone study we have recorded phone usage and location measurements between June and September 2016. We investigate who were the people who installed and played Pokémon GO and what effects it had on their behaviour. We chose as a middle point the start of playing the game, and selected users that had activity for at least two weeks before and two weeks after it. In this work we present our findings on a sample of 2, 861 users. We compare demographic characteristics and Big Five personality traits of these users with 7, 904 non-playing users from the same time period. The general daily phone usage of players increased on average by 27 minutes, which represents 16% per day. In terms of large scale movement patterns, these did not change, with regard to diameter and total path length per day.
Author Keywords Phone usage; Pokémon GO; Location-based gaming; Movement.
ACM Classification Keywords H.5.m [Information interfaces and presentation (e.g., HCI)]: Miscellaneous
Introduction In our current times, smartphones have become ubiquitous. In many cases, these devices have replaced classical personal computers and they represent the only method of long distance communication between people. The smartphones are also filled with sensors that collect and provide information regarding the surroundings, such as GPS, and are also equipped with cameras. These capabilities allow developers to create applications that are not running just on a screen, but also use the sensors and cameras to provide an immersive experience.
Figure 1: Left: Pokémon GO map with points of interest overlayed on a real world map. Right: AR view of Pokémon capturing.
Figure 2: Different types of eggs with their respectiv distances for hatching.
Location-based games are one way of providing this type of experience [6]. In comparison to traditional computer games, where players sit in front of a computer, in this genre, players need to move around the real world. Instead of relying on keyboard and mouse as input devices, they use the real world movement patterns to reach certain places which are of interest to the game [16]. The interaction through a device becomes secondary in this case, even though it is still necessary. The location of the player allows him to play the game, thus becomes an affordance for the device. Players are also able to meet other players in the real world and interact with each other in the game context. Pokémon GO is a location-based mobile game. It incorporates features based on augmented reality (AR) and it is free-to-play. It was developed by Niantic, for iOS and Android, collaborating with Nintendo. It was released on the 6th of July 2016 in the US, Australia and New Zealand, quickly followed by Europe starting with the 13th of July with Germany. In the game, the players are Pokémon (pocket monsters) trainers who search, collect, train, evolve, and battle with these creatures. GPS is used to match the player’s real world location with the virtual world, see Figure1. When Pokémon appear in the virtual world, they are overlayed
on the real-world through AR by using the mobile camera, see Figure 1. Poké Balls, eggs, and other items can be collected from PokéStops, which coincide with points of interest in the real world. These locations represent re-purposed portals from Ingress, another highly popular location-based game developed by Niantic. New Pokémon can be found by moving in the real world, or they can be hatched from eggs. Based on the egg type, the player must move more than a specific number of kilometers to hatch it, see Figure 2. The players can choose to play against each other by fighting in Pokémon gyms, which can be conquered. Similar to PokéStops, Pokémon gyms are located on points of interest in the real world. In this paper we analyse a subset of data collected through the Menthal project, an ongoing long-term study that started in January 2014 [5]. It consists of a data collection Android app and an analysis framework [3]. Over its lifetime, the app has been installed on approximately half a million devices and has attracted more than 400, 000 registered participants. The main goal of the project is to investigate phone and app usage on Android devices. The Pokémon GO launch happened while Menthal was deployed, thus the means of data collection were already in place. This offered the unique possibility to collect "in the wild" phone usage before and after game installation. In the next sections, we review the related work. Next, we describe our methods and the dataset. We compare demographics and personality traits of users’ who played Pokémon GO with other study participants. We investigate which characteristics caused users to play more Pokémon GO. Further, we analyse phone usage and movement patterns before and after users install and start playing Pokémon GO. Finally, in the conclusion, we summarise our contributions and provide ideas for future work.
Related work
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Figure 3: Age and gender distribution of Pokémon GO players.
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Games are not the most used apps on a phone. People in general use their phone for around 160 minutes per day but the usage of game apps, compared to social or news apps, is significantly less [2]. This trend has changed since a few years ago, when people were playing games just before going to sleep [9] and for much longer than using other apps. Social apps, such as WhatsApp and Facebook, are today the most used apps on phones. Users are becoming accustomed to carrying extra batteries, but are still concerned with their battery levels when playing games or using power intensive apps [12, 13]. Location-based gaming has become an interesting area of research in the field of HCI [22]. Advancements have been made by implementing features such as augmented reality to expand the game immersion [8] and bringing elements from treasure hunting [15] and geocaching [17] to improve the movement and location requirements. All these features have led to the construction of ephemeral, experimental, or successful games such as: "BotFighters" (2001), "Songs of North" (2004) [14], "Feeding Yoshi" (2006) [7], "Insectopia" (2007) [20], "Ingress" (2012) [10]. These games have also been studied from the point of view of creating physical activity tasks for their players and how to use gamification for better adherence [21].
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Figure 4: Personality traits of Pokémon GO players and non-players (scale 2 - 10)
Other researchers have studied the Pokémon GO phenomenon. As with other location-based games, they studied them from the point of view of phyisical activity and not necessarily movement. Pokémon GO lead to a significant increase, more than 25%, in physical activity compared to previous levels [1]. The aspects of game enjoyability have also been researched [19]. Most players found it fun and considered it a good way to incorporate social aspects with outdoor group activities. Negative aspects were mostly re-
lated to technical issues. The placement of the points of interest found in the game was also studied. It was found that there is a bias towards urban areas and neighborhoods with smaller minority populations [11]. This should not be necessarily a surprise since these points of interest were derived from a previous popular game. Most of the current research conducted until now on Pokémon GO is based on questionnaires given to players, with the exception of [1].
Methods In the current work, we used a part of the dataset collected in the Menthal study[5]. The dataset consists of data of some 350, 000 android users who installed the Menthal app and agreed to take part in our study. The study was approved by the IRB at the University of Bonn. The Menthal app collects detailed information on phone and app usage, communication meta-data, location, and questionnaire based data on demographics and the Big Five personality traits. Users are incentivised to use the Menthal app by receiving feedback on their general phone usage [4]. Based on Google Play store data, the majority of the Menthal app users (79%) come from Germany, followed by Austria and Switzerland in the top three. To study behaviour and characteristics of Pokémon GO users we use data from the period between June and September 2016. As Pokémon GO launched in July 2016, focusing on this period allows us to look at the behaviour of people who just installed the game for the first time. We use the collected location information to be able to estimate how much players move during the day. This data is collected by default every 15, 30 minutes, or a user chosen interval. We use the best available source of localisation - GPS, mobile network, or the last cached measurement available. Hence, accuracy of the measurements varies, de-
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pending on the source. For the analysis of their movement patterns, we look at days when we have at least 10 location measurements with an average accuracy less than 2000 m. We measure two variables: daily diameter (distance between two farthest points captured during a day), and daily path length (sum of distances between consecutive location measurements). Even with all the filtering, we see outlier values, often with a diameter of daily movement larger than 1000 km. To use the data, we limit our investigation to users with a diameter of 100 km per day.
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Figure 8: Left: Distribution of average daily time spent playing Pokémon GO per user, Right: Distribution of daily average number of Pokémon GO sessions per user.
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Figure 6: Average phone usage before and after first Pokémon GO session. The phone usage increase by 16%.
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Figure 5: Average time spent playing Pokémon GO for our female and male players
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Figure 7: Left: Number of Pokémon GO players, with complete demographic profiles, recorded for each day. Right: Number of consecutive days that contain at least one Pokémon GO session after the starting to play until the end of the recording period. We observe that many users did not play the game for a long time.
During the collection period, 21, 112 users of the Menthal app sent data, out of which 4, 964 played Pokémon GO at least once. These players have sent data for at least 14 days before and 14 days after the first time they played Pokémon GO. They played the game for an average of 11.23 days with a median of 7 days, see Figure 7. From the users that have sent data, 10, 765 have filled our age, Big Five personality traits, and gender questionnaires. From these 10, 765 users, 2, 861 (1, 210 females, 1, 650 males) are Pokémon GO players and 7, 904 (4, 023 females, 3, 881 males) are non-players. The 2, 861 players represent our dataset that we have used in the analysis.
Results Figure 3 shows the age and gender distribution of our dataset. We see that younger male players are predominant in our dataset. Figure 4 shows the personality traits of players
compared to non-players in the same period of time. The personality traits of players differ significantly from the nonplayers, they are less extrovert(beta = −0.13, p = 0.00219) and less conscientious(beta = −0.57, p < 2e − 16).
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Figure 10: Daily diameter of Pokémon GO players. Comparison before playing the first time and after. There are no significant changes.
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Figure 12: Daily path length, before and after first Pokémon GO session. There are no significant changes.
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Figure 9: Daily Diameter - distance beetween the two farthest points, before and after first Pokémon GO session. There are no significant changes.
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Figure 11: Daily path length of Pokémon GO players. Comparison before playing the first time and after. There are no significant changes.
The daily average usage of Pokémon GO among players is 28.3 minutes and the median is 15.8 minutes. The average session length is 2.3 minutes and the median is 2.02 minutes. On average per day, the Pokémon GO app was started 11 times with a median of 7.1 times, see Figure 8. Anova analysis of demographic and personality traits on daily usage of Pokémon GO shows that only gender has a
statistically significant impact (p = 5.86e − 05, beta = 5.99). Males have a higher tendency to play Pokémon GO. We have found that phone usage increases after users start playing Pokémon GO for the first time. This effect is statistically significant(p =< 2e − 16) and on average phone usage increases by 27 min (16% increase). The effect still holds when we take into account differences due to weekdays and assume random effect for different users. The diameter in which users move does not change significantly after starting to play Pokémon GO. We control for the day of the week, since it has a big impact on the user’s
movement pattern. Figures 10 and 9 show the difference of diameter and path lengths between before and after starting to play Pokémon GO. The change is not statistically significant. This is not surprising, as for most of the users their movement diameter is connected with their daily routine (e.g. commute from home to work or school). The path length follows the same pattern as the diameter. Figures 11 and 12 show again that there is no significant change in path length after users start playing Pokémon GO. Our interpretation of these findings is that the Pokémon GO players do not travel much outside of the ordinary while playing, but rather stick to their normal routines. Our current focus was on large movement patterns instead of micro-movement patterns. Due to privacy concerns and current IRB regulations, we are not allowed to discuss micro-movement patterns and the general level of physical activity.
Conclusion Our main contribution is providing insights into who played Pokémon GO and how this affected their behaviour. We observe that, in the analysed sample, players were less extrovert, less conscientious, and younger than non-players. We have discovered that daily phone usage increased significantly after installing and subsequently starting playing Pokémon GO. Instead of previous usage being replaced by the game, new time was allocated for it. When taking into account all demographic and personality traits, the group that was the most susceptible to play Pokémon GO was represented by males. In terms of large scale movement patterns, the daily radius and the daily path length did not increase. Taking this at base value might be counterintuitive, but we assume that the majority of players do not devote extra travel time and effort to the game and instead play it while moving on with their normal daily routines. This
can be observed also when taking long distance commuting or travelling into account. The uniqueness of our contribution consists of data and the way that they were collected, and the quasi-experimental study design with the treatment of Pokémon GO appearing as a natural phenomenon [18]. A limitation of this work is represented by the fact that the dataset was sampled from the Menthal dataset. In this case, only users that had the Menthal app installed were considered. To the extent of our knowledge, our work represents the largest investigation of Pokémon GO based on data from actual phone usage. In terms of future development, we would like to analyze the period after the installation for a longer time, especially to observe the adherence rates. We would also like to look into how real social networks affect the game by looking into clusters of location data. We already know that players clump together with other players [19] but we would like to analyse cluster size. Besides clustering, we would also like to look into regions and how users travel in different contexts (e.g. urban vs. rural).
Acknowledgements IA was funded by the Federal Ministry of Education and Research (BMBF) (#00160280). IA and KB were funded by the University of Bonn and through numerous short term grants. We thank Andreas Weber for his support of the project. We thank Christian Montag, Boris Trendafilov, Mark Eibes, and Pascal Welke for their collaboration on various stages of the Menthal project.
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