DH500 Project

Rebecca Topping

[rebeccadh500]

Week 1

Welcome to my Digital Humanities 500 page! On this page, I’ll give some short reflections and examples of the work I’ve done and things I’ve learned during DH500 this term. Enjoy!

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Week 2

Below are the examples of my project road map. I wanted to get creative with it and make it look similar to a Pokedex entry in a Pokemon game, except instead of Pokemon, I used the weeks. It was fun, but took a lot longer than I expected it to. But it was more fun to make than a Gantt Chart so I figured I came out on top.

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Week 3

Data Sets

This week we discussed data sets. I found my data in 3 places:

1. Alberto Barradas’ Pokemon with Stats sheet

2. Bulbapedia

3. Serebii

The first data set was a listing of over 700 Pokemon that included their stats (HP, attack, defense, special attack, special defense, speed) and their types. This data set was built to help teach students about statistics in a way that would be relevant to them. Bulbapedia and Serebii are two major hubs of information for Pokemon players that include sprites, anime information, and even moves that Pokemon can learn. These sites are basically the catch all (see what I did there?) of information about Pokemon. If you need to know something about a Pokemon, these websites will have it.

There are some problems with the first dataset. It’s not complete since it doesn’t include the latest generation of Pokemon and it doesn’t include the regional variations of Pokemon. It doesn’t include images or note appearances of Pokemon either in games or in the anime, which is why I had to include Bulbapedia and Serebii as well.

I also checked in on my physical copies of my Pokemon games to make sure that the data I had was correct.

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Week 4

Research Questions

Research questions I considered:

- What colours are most often used when designing Pokemon?

- What Pokemon types have the highest number of Pokemon? How has that changed from generation to generation?

- What trends can be seen when analyzing the direction that Pokemon sprites face?

- Are Pokemon more beast, more object, or more humanoid in their inspiration? Are there more animal Pokemon than object Pokemon? Are humanoid Pokemon the rarest?

- What do the designs of Pokemon have to say about environmentalism?

And here are some scholarly articles that I found that are related to these questions:

- "How 'Japanese' is Pokemon?" by Koichi Iwabuchi

- ‘It’s a Pokemon World’: The Pokemon Franchise and the Environment by Jason Bainbridge

- “Sound Symbolic Patterns in Pokemon Names by Kawahara et al

- “What Pokemon can Teach us About Learning and Literacy” by Vivian Vasquez

- “Pokemon: Exploring the Role of Gender” by Ogletree et al

- “The Cultural Politics of Pokemon Capitalism” by Anne Allison

- “I Choose You! Diversity in the Design of Pokemon” by  Ochsner et al

One thing that I found when looking for previous research on Pokemon is that a lot of the research focused on Pokemon Go!, an augmented mobile game where Pokemon spawn at geographic points in the real world. Much of the research discussed whether or not the game’s health benefits (walking to find Pokemon and hatch eggs) were outweighed by the dangers of the game (people driving and playing, wandering into unsafe areas while playing, etc). There was little research on the design of Pokemon, though I could find some research on the naming of Pokemon. I wonder if this is because the design of Pokemon doesn’t really matter or if it’s just an understudied field.

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Week 5

Data Modeling

Help. I don't know about you guys, but the computer science part of this program short circuits my brain. And statistics? Yuck.

So for week 5, I'm taking a break. We will resume next week when I can function again.

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Week 6

Text Analysis

One thing I was immediately interested in when talking about text analysis was the Pokedex entries for Pokemon throughout the games.

For example, here are all the Pokedex entries for Pikachu from Gen 1 to Gen 8. What words would Voyant pick out? Electricity? Lightning? Charge? And does this mean anything? In terms of Pokemon, probably not. I mean, does it matter to the real world what word choices they chose? With Pikachu, not so much. But in other, real-world contexts? Perhaps.

Of course, text analysis always needs to be taken with a grain of salt since words in the English language have a multitude of meanings based on the context of where it is in the text, when the text was created, etc. So while a text analysis of Pikachu's Pokedex entries aren't all that important, they're also less problematic than more important texts could be.

Pokedex entries taken from https://bulbapedia.bulbagarden.net/wiki/Pikachu_(Pok%C3%A9mon)

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Week 7

Degrees of Pikachu

When coming to this week's project, I thought about our intensity challenge from earlier this semester when we were tasked to find out how our class was connected using the degrees of Bacon example. With Pokemon, there's been one Pokemon that has I figured would connect to every other Pokemon: Pikachu. Pikachu has been in every Pokemon game (except Pokemon Black/White) and is a main character in the Pokemon anime. Because of this, I figured that all Pokemon could be connected to each other via Pikachu.

Some example connections:

1. Every first generation Pokemon shares a generation with Pikachu

2. Pokemon that appear in the Pokemon anime may share a scene with Pikachu and perhaps even battle it

3. Pokemon (other than those in Black/White) share a regional Pokedex with Pikachu

4. Pokemon in games where Pikachu is also in the game may share spawn points

5. Pokemon in Black/White may share other games with Pikachu or may share games/anime scenes with Pokemon that DO have something in common with Pikachu

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Week 8

Image Processing

I had some ideas about how I could take the visual aspects of the dataset and compare/contrast them. I thought about analyzing the direction that Pokemon faced in their Pokedex entries and I wondered what kind of information that it could give us and what kind of trends are there.

For example, here are the Pikachu sprites from Gen 1 to Gen 8 (Gens 6 and 7 are the same). Pikachu started out by staring straight at the user before turning to the left in Pokemon Gold. Ever since, Pikachu always faces left in the sprites.

I wondered if there could be trends like prey animals face a specific direction or only ghost Pokemon stare at the user head on. However, with nearly 1000 Pokemon spanning 8 generations and counting different region forms, gender forms, and shinies, the number of sprites I had to analyze were a lot. But it could be a place for further research in the future.

Pokemon sprites were borrowed from https://www.serebii.net/pokedex/025.shtml

[maptitude1]

Regions of Pokémon games

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Week 9

Mapping

Mapping Pokemon data is somewhat easy because each game that comes out has its own map. The user above, maptitude1, has created a map of all of the different Pokemon games as a world rather than the different regions that we see in each game, which is super cool!

As discussed in my flipgrid, there are other, less obvious, ways that I could have mapped my data, such as mapping in my home where I catch these Pokemon when I play (like catching a Pikachu in the living room, a Butterfree in the bedroom, etc.) One map that Pokemon used to have in its Diamond/Pearl game was a map where Pokemon you traded came from. A globe sat in the trade centre and you could see where Pokemon came from. Though this was pretty general information (my location was set to Alberta, Canada). I think that would be really cool to have again, especially with Mystery Trading in the later games.

[rebeccadh500]

Week 10

Data Graph

I wondered what kind of visualization I should make this week and I realized that I hadn’t done examined how many Pokemon are in each type. I wasn’t surprised to see that water-type Pokemon are the most plentiful because there was a Pokemon game that you basically spent entirely in the water. However, I was surprised to see that ghost-type Pokemon were by far the rarest.

There were ideas for other graphs I could make that could further the data information a little bit. Like, how many of each type above are represented in legendary Pokemon. Or what’s the breakdown in type distribution in each game? Or the type distribution of gym challenges throughout the games. Lots of fun ways to show the data.

[retrogamingblog2]

Ancient Pokemon Artifacts made by OlfactoryProps

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Week 10

Affective Response

These above images remind me of my data set and how I used to view data. The idea of ancient Pokemon data is funny, but at the same time, these forms add a form of legitimacy to what I’m looking at. Studying Pokemon doesn’t feel that worthwhile because it’s just a child’s video game, but there can be interesting things found in the data.

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Week 11

This is what I looked like during Reading Week.

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Week 12

Dynamic Presentation

I was inspired by the Ferguson reading and by Compressed Movie Prints to make my own visualization of Pokemon. The above picture is the first 30 Pokemon if you reduced them to their two main colours each. Similarly to how the Compressed Movie Prints use a pixel to show a scene in a film and how Ferguson visualized the evil queen’s transformation as a 3D cube.

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Week 13

Scalar

The assignment this week was to outline a design of our own projects using Scalar. This was... a lot.

My data set feels too simplistic and nonacademic for a site like Scalar. However, if I were to make a Scalar book about my data, I’d organize it like so:

- First, I’d organize by generation. There are 8 generations as of 2020 and each have between 72-157 Pokemon in them each. Users would be able to see each generation’s data if they follow the linear path.

- Second, I’d organize by game appearance. While the first Pokemon games will have the same information as the first generation of Pokemon, each game includes previous generation Pokemon in them. You’d likely see different trends in this section, but still be related to the first section.

- Third, I’d organize by type. There are are 18 types (the most recent was the Fairy type, which was introduced in generation 6 and some Pokemon in previous generations were retyped as fairy). All Pokemon belong to at least one type and many have a second as well. There could be really cool visualizations about the percentage of Pokemon belonging to a certain type, etc. 

There are other ways I could organize a Scalar book as well, but this is where I would start.

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Week 13

Contrasting Project Curation

In this week's assignment, we were asked to find 3 DH project sites that curate their projects in contrasting ways. These are mine:

Stanford's Chinese Canadian Immigrant Flows, 1912-23

American Prison Writing Archive at Hamilton College

A People's Archive of Police Violence in Cleveland

These three projects show different ways of showing data. The Stanford one uses a program from the Spacial History Lab and Adobe Flash to let users see where Chinese immigrants lived in Canadian cities over the course of 11 years. The user can scroll left to right and watch how Chinese people from different districts settled in different areas over time. It's really interesting to look at, but is intimidating for those who aren't used to the system. As well, with Adobe basically dying at the end of 2020 (with whatever hope had left as humans), I wonder what will happen to this project and how it will (or won't) adapt.

The second project is a listing of 2776 essays written by American prison inmates about their lives since incarceration. The listing can be searched through a search function, as well as a browse function that can pull up essays from a specific state, prison, author, and even author attributes (such as ethnicity, gender identity, religion, etc.). There aren't any data visualizations like in the Stanford project, but I don't think they need it to be successful. The point of the essays is to remind people that incarcerated people are people, not numbers, not crime statistics, etc. If the researchers had created a data visualization about the number of black inmates or trans inmates or essays talking about police brutality, etc., those visualizations would further reduce inmates to a single part of themselves. It would take away from the voices of the inmates themselves. This taught me that there are places where the visual representation is not only unnecessary, but also inappropriate.

The third site posts images and stories of black people who have been abused or even killed by police. These stories are posted by people who have been affected by these events. Some of the stories are written essays, and others are videos, audio clips, and other forms of media. A striking aspect of this project is the faces that stare at the reader of people who have been killed by Cleveland police. In a similar vein as the second project, this project humanizes the victims through stories (though this time written by those who loved the victim), but also does so through images. Because the content is user generated, there is little data visualization here.

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Week 14

Why Tumblr?

Tumblr is the website for fandoms. When thinking about my audience for this work, the Pokemon fandom is who I came up with. Their devotion and commitment to Pokemon is how I got my data set to begin with and I have a responsibility to report my findings back to the community who provided me with the data. Using relevant hashtags can spread the work I’ve done to whomever has an interest in the topic.

As well, Tumblr provides a great way for audiences to respond to my findings: through reposts and replies. The repost method gives my audience a thread of responses that they can reply to and have a documented conversation in, which can help me revise and better understand my own work. As well, Tumblr has been a platform for thirteen years, which makes me confident in its stability. One tricky part of Tumblr was that I had to work in reverse. Because Tumblr shows newest posts on top, but I wanted to present my work in order from week 1 to 14, I had to start with 14 and work my way back.

This research doesn’t directly affect anyone’s lives so, while I enjoyed the project and what I learned, it’s not the grandest priority when thinking about preservation. As well, as new Pokemon games are released, the data within becomes less and less relevant.