Blended Realities, Dailylife and Pokemon GO Gameplay as Seen through Augmented Reality Snapshots

20th Dec. 2017 Paula Alavesa and Xu Yueqiang

Just participating in the hype of the global launch of Pokemon GO July 2016 was a shared cultural experience that created a sense of belonging in connection to both people and place (Vella et al. 2017). There has been a similar rush of interest in the phenomenon by researchers, over the past year several thousand research publications have been written from various perspectives (Google Scholar: search phrase “Pokemon GO”). For a mapping study (References in the end) on literature this is a fertile ground for transdisciplinary synthesis on the topic.

The context of game-play, the city streets, is an integral part of pervasive gameplay.  How the game-play is entwined with daily life is an equally important characteristic of these games (Montola et al. 2009; Paavilainen et al. 2017; Liu et al. 2017). Daily life cannot be observed in a controlled lab environment (unless one lives in a lab), which creates a challenge for observing the blending of daily life and game-space.


On our sample we extended the collection of photos to Nordic countries, to get some geographic distinction to the data set (now comprised of ). We conducted a mapping study on literature to Pokemon GO related papers and are confident that no one has yet published similar study on the imagery posted on social media and how it makes the blending of daily-life and the game perceivable as it happens. For this study we embrace hybrid reality logic (de Souza e Silva 2009), which states that pervasive games combine different realities such as social media and the city streets. We take for granted that “a hybrid information ground” contains both the physical and the online communities where the flow of information reaches. Following this logic, we focus our inspection where the blending can literally be seen, the (AR) snapshots the players publish in online communities.

Initial Analysis (Oslo-Oulu)


Based on initial observations from image material from Twitter, Facebook and Instagram from Oslo and Oulu the context of gameplay does not vary between the AR snapshots an regular photos, outdoor and indoor locations are equally presented. What is notable that many play the game at home, where the gameplay is not as intense perhaps but not outdoors, although the game is perceived to be an outdoors game. There are slightly less AR images in the gathered material. Most of the images (58%) are screenshots from the game user interface, either images of pokemon inventory or snapshots of rare pokemon. Other prominent types of photos depict crowds playing Pokemon GO or images from hatching eggs (pokemon eggs require a 2-10km walk to hatch). A typical comment for an image depicting outdoors scenery is: “On a Sunday walk through the rain and 100% raikoun.” or “Spotted this while pokehunting.” Urban nature is well presented in the outdoor locations for gameplay, but built city streets are equally important environment for gameplay.

Both AR snapshots and normal photos posted show equally the blending of dailylife (daily activities) and gameplay. In both photos and AR snapshots there are plenty of images in category “Arts and Crafts” depicting fan-art and cakes. There is however one form of novel art, AR fan-art, where the AR pokemon are situated in their physical environment with care, the images are edited for better composition and later submitted to the social media sites with a prominent watermark.


What is notable on the AR imagery specifically, is how the social media posts of photos depict the two-way influence between the physical world and the virtual. People pose alongside the pokemon and appear to hold them or take snapshots of the pokemon sitting on the edge of their wine glass… or a toilet seat. The distinction between private and public does not seem to exist when posting an image of a rare pokemon (Although, this the kind of initial impression that might change  with further analysis). The blending effect from physical to virtual can be observed in images where physical world objects are perceived as pokemon. One person from Pokemon GO Oslo Facebook group submitted a photo from an office-meeting commenting a projected graph: “Has anyone else seen Pokemon in real life objects. This is Jumpluff”. Some players craft intricate Pokemon sculptures from Hama-beads or playdough. Fanart of such popular game can be expected (Manifold 2009), but these sculptures are then placed on a street-walk and a photo is submitted to social media as if it was an AR view from the game.


Conclusions and Future Work

AR images and photographs from gameplay can function as probes to blending of magic circle (Huizinga 1955) of gameplay and daily life, these images can even give us a peak to the two way blending of digital and physical realities.

It will be interesting to compare the findings (once we conduct a more thorough qualitative analysis with the full dataset) to the frequencies in other/normal social media image posts (Hu et al. 2014). Pokemon GO players post scenic images and screenshots, but seldom there are selfies amongst the photos. We would also like to accompany this study with a survey where we probe what is the self-perceived significance of the AR images to the Pokemon GO players from social media groups, to see if the additional data will reveal hidden meanings that do not come across from the photos and the accompanying brief commentary. Perhaps the true value of the AR feature in Pokemon GO is in the formation of the social online communities.

Paper draft, under work!


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Overview on the Current Number of Images and Some Notes

We searched and retrieved photos posted in social media sites, Twitter, Facebook and Instagram over the period of 16-20. Nov. 2017

Search phrases: #pokemongooulu, #pokemongohelsinki, #pokemongotampere, #pokemongoturku for Twitter #pokemongo or #pokemon and either #oulu, #tampere, #helsinki or #turku. In Facebook we signed up for PokemonGo player groups for each city, the combined number of members in those groups was 16 947… at the time 17.-19. Nov. 2017.

We are considering adding Vantaa and Espoo to our selection of cities, to reach an adequate sample size.


What we can already tell is that there are way more screenshots of the game inventory or UI than the environment (Table beneath the text). On the images of the environments there are somewhat more pictures of the actual surroundings when playing Pokemon GO, although snapshots taken through the AR view are not far off. People do not post so many UI screenshots in Instagram or Twitter as they do in Facebook.

On images that are used to communicate with other players in SOME, AR images dominate… although there are lots of screenshots from the actual game too, either images of Pokemon inventory or snapshots of rare pokemon. Other prominent types of photos depict crowds playing Pokemon GO or images from hatching eggs (pokemon eggs require a 2-10km walk to hatch). A typical comment for an image is: “On a Sunday walk through the rain and 100% raikou.” or “Spotted this while pokehunting.”

Pervasive (location based) games entwine with daily life, with the underlying thought that people and space are intrinsically playful. One interesting additional aspect for the study is analyzing the playful nature of urban space; What kind of locations are in the selection of images, are there specific locations that cannot be found and why are they not there or are unplayful?

Source All available pictures Pokemon location pics Pics with AR Pics with the environment Tot. (AR+Env.)
Facebook Tampere 2 430 22 107 106 213
Helsinki 2 624 79 80 62 142
Turku 4 374 146 72 182 255
Oulu 69 59 61 120
Twitter Tampere 4 30 34
Helsinki 136 102 161 263
Turku 21 4 21 25
Oulu 7 4 11
Instagram Tampere 210 7 17 63 80
Helsinki 150 31 53 84
Turku 51 10 28 38
Oulu 60 11 26 37


Day 3.

Our research focus was refined by today’s discussions. We also plan to expand our selection material to analysis of the key sites in Pokemon GO game-play. Pokestops and gyms are close to well known sites in each town, but the Pokemon spawn spots are also somewhat well known locations, like small libraries, park info boards and graffiti. The most valuable information e.g. the spawn points of the Pokemon is out of our reach, but we can deduct something from the image material that shows where one can track the Pokemon, hence we also collect those images to our image data-set.

We established communication so that we can work together for the coming three weeks. We already had some discussion on how to refine our coding schema for image tagging. We plan to consult the course material especially on nature of locations and activities projected in social media imagery and do a further literature review on our topic to find something on augmented reality and pervasive location based games. There are plenty of Pokemon GO papers to inspect too; Google Scholar finds 5,880 papers with search phrase “Pokemon GO”. Doing a background study in now important, because we, diligent PhD students, always aim at publishing and we need to know how our research topic and findings can advance the current knowledge.

Day 2.

Today’s practical part was on learning to graph and filter big data using QGIS. We also refined our research question, since one team member withdrew his course participation. We are now looking into the context of augmented reality usage in Pokemon GO. Is it really game-play related and how the surrounding build environment is portrayed in the images. To gain good sample size for image analysis we expanded our scope to four cities Oulu, Tampere, Helsinki and Turku. We are also combining data from Instagram, Facebook, Twitter and Google image search, although we noticed that Google Image search does not provide us with results we can rely on being location spesific.

RQ: How the built environment and AR view and game-play in Pokemon GO entwine to form hybrid space?

Our plan is to conduct a qualitative analysis using the tools we have been learning to use over the past two days (Image Trackek, ImageJ, Grasshoppe etc). We are now discussing and constructing categorisation for coding (tagging) the images.


Day 1.

We had a crash course on meta-morphology and social media. We also explored image analysis, tagging and visualization tools on different kinds of data-sets. After the theory part we sat down and brainstormed on research topic that would yield new knowledge on the field, and also fascinate everyone in our interdisciplinary team (business, architecture and computer science). After initial discussion we searched the Web for interesting data-sets we could use in our study to answer our research question:

RQ: Is there a correlation between Pokemon GO playeractivity and changes in regional real-estate value (comparison 2015-2017)?

We have a exploratory approach and do not want to bias ourselves to a specific outcome, hence:

Expected outcome: We will observe changes in real-estate value and will describe the phenomenon in relation to Pokemon GO activity.

Pokemon GO was released globally at July 2016 and became a phenomenon to change the behavior and  attitudes towards (public) space for many. We will compare regional densities of Pokemon GO related hot-spots and real-estate prices from 2015 to current date to see if the real-estate values have changed in relation to player-activity.