It’s been a full year since I’ve become an independent data visualization designer. When I first started, projects that came to me didn’t relate to my interests or skills. Over the past eight months, it’s become very clear to me that when clients hire freelancers, they want either: dashboards, interactive visualizations, or reports. I didn’t care for any of these. It always surprises me when I get emails for this kind of work because my portfolio is far from what they look for. I specialize in static visualizations that aim to achieve two outcomes: data documentaries (like a film documentary, except it recounts events through data), and visualizing the world (integrating data into existing formats that don’t traditionally express data, like a food menu).

我 T的已整整一年，因为我已经成为一个独立的数据可视化设计。 我刚开始的时候，遇到的项目与我的兴趣或技能无关。 在过去的八个月中，对我来说很清楚，当客户雇用自由职业者时，他们要么想要：仪表板，交互式可视化或报告。 我不在乎这些。 当我收到此类工作的电子邮件时，总是感到惊讶，因为我的投资组合远非他们想要的那样。 我专门研究静态可视化，旨在实现两个结果：数据记录片(如电影记录片，只是通过数据重新记录事件)，以及可视化的世界(将数据集成到传统上不能表达数据的现有格式中，例如食物菜单) )。

I considered quitting data visualization altogether in June 2020 and moving onto something else. I originally entered the field because I was inspired by creative work done by Giorgia Lupi, Nadieh Bremer, Stefanie Posavec, Eleanor Lutz, Mona Chalabi, The Pudding, and so many more. I didn’t join this industry to spend the majority of my career creating dry client work. By August, however, I received two new opportunities which both aligned with my interests and skillset — a reminder that despite the outcome, I just need to remain patient during this journey.

我考虑过在2020年6月完全退出数据可视化，然后转向其他方式。 我最初进入该领域是因为我受到Giorgia Lupi，Nadieh Bremer，Stefanie Posavec，Eleanor Lutz，Mona Chalabi，The Pudding等人的创造性工作的启发。 我没有加入这个行业，而是将我的大部分职业生涯都花在了干客户工作上。 但是，到了八月，我获得了两个与我的兴趣和技能相匹配的新机会-提醒您，尽管取得了成果，但我在旅途中仍然需要保持耐心。

I learned that the independent market thrives in operations that can scale: courses, speaking, workshops, and books. Work usually comes from folks in business analytics, research, health, and non-profits. For work that doesn’t scale, like consulting or services, it’s tough to get a sustainable roster of clients who want creative work that goes beyond the standard bar chart.

我了解到，独立市场在可扩展的运营中蓬勃发展：课程，演讲，讲习班和书籍。 工作通常来自业务分析，研究，健康和非营利组织中的人们。 对于诸如咨询或服务之类的规模不大的工作，要获得想要超越标准条形图的创造性工作的可持续客户名单是很难的。

So, how do I move forward? I looked back to my previous article and thought about the people I talked to. I’ve tried freelancing, teaching, and speaking. The only option left is to develop products, specifically print work for sale. My skills thrive in static data visualization, so naturally, print made sense. I wondered about designing expected things like posters, reference guides, or books. However, I had no experience and was afraid to try these out.

那么，我该如何前进？ 我回头看了上一篇文章 然后想起我和之交谈的人。 我曾经尝试过自由职业，教学和演讲。 剩下的唯一选择是开发产品，特别是要出售的印刷作品。 我的技能在静态数据可视化中蓬勃发展，因此自然而然地打印就可以了。 我不知道要设计预期的东西，例如海报，参考指南或书籍。 但是，我没有经验，害怕尝试一下。

Miraculously, one project idea came to me in the middle of June, it was going to be based on Tekken, a 3D Japanese fighting game that’s been around since the late 90's. I’ve been around games for as long as I can remember. When I was a kid, my older brother would put me right beside him as he played Dreamcast or Playstation 2. Gaming is close to my own identity, so it’s fitting that it’s what would give me another chance in this industry.

奇迹般地，一个项目构想在6月中旬出现在我的脑海中，它将基于《铁拳》，这是一款90年代末开始出现的日本3D格斗游戏。 我记得游戏很久了。 当我还是个孩子的时候，哥哥在玩Dreamcast或Playstation 2时会把我放在他旁边。游戏与我的身份很接近，因此这给了我这个行业另一个机会。

See the full project I did on Tekken.

查看我在《铁拳》上所做的完整项目 。

如何思考 (How to Think About Something)

I often think that how we think matters more than what we know. I wanted to challenge data visualization and see if it can teach others how to think about something instead of focusing on the what.

我经常认为我们的想法比我们知道的重要 。 我想挑战数据可视化，看看它是否可以教别人如何思考某件事，而不是专注于事物 。

The chart from the Financial Times shows the explosive growth of online retail during the pandemic. The approach of this chart is to expose what happened to online retail during the pandemic. The reader can see there has been a sharp growth and the annotations provide context to the story. This approach of showing the what is an important facet of data visualization. But, I was more interested in showing the how.

《金融时报》的图表显示了大流行期间在线零售的爆炸性增长。 这个图表的方法是暴露在大流行期间所发生的网上零售。 读者可以看到增长Swift，注释为故事提供了背景。 显示了什么是数据可视化的一个重要方面的这种做法。 但是，我对展示操作方法更感兴趣。

I arrived at this idea of showing the how after watching a YouTube video describing the combat systems of video games; it opened my eyes to how combat works and the deeper mechanics involved with it. I learned about start-up animation, which is extremely important during combat. This is an animation that occurs as you attack, immediately before your move impacts the enemy. Light attacks are faster than heavy attacks because they have fewer frames in the start-up animation, whereas you risk being more vulnerable and subject to attack from enemies with heavy attacks.

我想到了这样的想法：在观看描述视频游戏战斗系统的 YouTube视频之后， 如何展示; 它让我看到了战斗的工作方式以及与之相关的更深层次的技巧。 我了解了启动动画，这在战斗中非常重要。 这是动画，攻击发生时，就在您的举动撞击敌人之前。 轻度攻击比重度攻击要快，因为它们在启动动画中的帧数较少，而您则更容易受到攻击，并有可能遭受来自具有重度攻击的敌人的攻击。

Once I learned the mechanics, I inherited a new perspective on combat. If the mechanics of something are revealed, I could understand the how. Ever tried to take a mechanical pencil apart? I’ve done this when I was younger to get the lead unstuck. After doing this several times, I had a general understanding of how the pencil worked.

学习了机械技巧后，我就继承了战斗的新观点。 如果某事的机制被揭示，我能理解的方式 。 曾经尝试过拆开自动铅笔吗？ 我年轻的时候就这样做了，以消除潜在的麻烦。 这样几次之后，我的铅笔是如何工作的一个大致的了解。

How can data visualization teach readers how to think about something? I don’t have a thorough answer as I’m still in the early stages of thinking about this. However, through my own reflection, one way is to compartmentalize information. Like how scientists look to understand the world through the microscopic scale, information is no different.

数据可视化如何教会读者如何思考问题？ 我还没有一个完整的答案，因为我仍处于思考的早期阶段。 但是，通过我自己的反思，一种方法是分隔信息。 就像科学家通过微观尺度看待世界的方式一样，信息也不例外。

I first started exploring this idea with a board game called Bang! I broke down how the game works by visualizing the distribution of the cards. A simple histogram revealed which cards were more valuable due to scarcity. I’ve played Bang! over 100 times, but I’ve never taken a moment to think about which cards were more rare. Furthermore, this visualization shows how the game was designed, favouring offensive play.

我首先从一个名为Bang的棋盘游戏开始探索这个想法。 我通过可视化纸牌的分布来细分游戏的工作方式。 一个简单的直方图显示哪些卡因稀缺而更有价值。 我玩过Bang！ 超过100次，但我从来没有花时间去思考哪些牌更为稀有。 此外，此可视化效果显示了游戏的设计方式，有利于进攻性比赛。

While The Tekken project was an evolutionary step in my desire to show the how, I never expected it to also completely change my design process.

而铁拳项目是我的愿望，展示如何演化而来，没想到它也彻底改变我的设计过程。

《铁拳》如何改变我的流程 (How Tekken Changed My Process)

I stumbled on an amazing video about how Mick Gordon creates music for the latest installments of the Doom games. In his talk, he repeats the following insight:

我偶然发现了一段精彩的视频，内容涉及Mick Gordon如何为最新版《毁灭战士》游戏创作音乐。 在演讲中，他重复了以下见解 ：

“If you want to get a different outcome, you must change the process.”

“如果要获得不同的结果，则必须更改流程。”

Tekken catalyzed the shift in how I work and there were several things I discovered in the new process.

《铁拳》催生了我的工作方式的转变，在新过程中我发现了几件事。

多维数据 (Multidimensional data)

The most seductive aspect of exploring Tekken as a data visualization project was its multidimensionality.

探索Tekken作为数据可视化项目的最诱人的方面是它的多维性。

In Tekken 7 Season 3, you can play as one of 30+ characters. Each character has over 100 fighting moves you can use. Generally, players start by having one ‘main’ or the character they will specialize in. This is important because they have to learn and master their character’s most effective moves, and gain a general sense on how to combat the other 30+ characters. It’s extremely difficult to play this game well due to the amount of knowledge and experience it demands.

在《铁拳7》第3季中，您可以扮演30多个角色之一。 每个角色可以使用100多种战斗动作。 通常，玩家从拥有一个“主要”角色或他们将要擅长的角色开始。这很重要，因为他们必须学习并掌握其角色最有效的动作，并对如何与其他30个以上的角色作斗争有一个普遍的认识。 由于需要大量的知识和经验，因此很难很好地玩这个游戏。

Players have voluntarily collected data for each move per character. A popular website players reference is rbnorway. In addition, many use Tekken Chicken on mobile to access this data on-the-go.

玩家已自愿收集每个角色的每个动作的数据。 流行的网站播放器参考是rbnorway 。 另外，许多用途 Tekken Chicken在移动设备上可以随时访问此数据。

In general, each move is defined by the following properties:

通常，每个动作均由以下属性定义：

Command input: The buttons you press on your controller to execute the move. Hit level: In general, a move can hit three areas on the body. High, middle, and low hits will hit the head, torso, and legs respectively. High and middle hits can be blocked while standing, but low hits require you to block while crouching. In addition, you can dodge high hits by crouching, and you can dodge low hits by jumping. Keep in mind this is a general rule.Damage: The amount of health you would deplete if your move successfully hits your opponent.Start-up frames: Every move has an animation before the move hits the opponent. Some moves have shorter animation (i.e. fewer frames). The fewer the frames, the faster the move, the less vulnerable you will be.Follow-up frames on hit: When your attack hits an opponent who isn’t blocking or in the middle of an animation, you could have plus or minus frames that could help you link moves more effectively. This phenomenon is also known as frame advantage/disadvantage.Follow-up frames on block: Similar to frames on hit, but this applies when you opponent blocks your move.Follow-up frames on counter hit: Similar to frames on hit, but this applies to when your move lands when your opponent is in the middle of an animation.

命令输入：在控制器上按下以执行移动的按钮。 命中等级：通常，一招可以击中身体的三个区域。 高，中和低击将分别击中头部，躯干和腿部。 站立时可以阻挡高中击，但蹲下时低击则需要阻挡。 此外，您可以通过蹲伏避开高命中，也可以通过跳跃避开低命中。 请记住，这是一个一般规则。 伤害：如果成功击中对手，您将消耗的生命值。 开机画面：每步动作在击中对手之前都会有一个动画。 一些动作的动画时间较短(即帧数较少)。 帧数越少，移动速度越快，您越容易受到攻击。 击中跟进帧：当攻击击中没有阻挡或处于动画中间的对手时，您可以使用正负帧来帮助您更有效地链接动作。 这种现象也称为框架优势/劣势。 格挡中的跟进框：与命中时的框相似，但是在对手挡住自己的举动时适用。 反击时的后续帧：与击中时的帧相似，但是这适用于当对手处于动画中间时您的移动着陆的情况。

If that was too confusing, here’s a short video I made describing this data. I have added one more variable, effective range. This refers to the maximum distance you can be for the move to still hit.

如果这太令人困惑，那么这是我制作的一段简短的视频，描述了这些数据。 我添加了另一个可变的有效范围。 这是指您仍然可以移动的最大距离。

It’s simply not enough to look at these variables in isolation, when every move has a variety of properties that define it. The data in this game presents the perfect playground for data visualization. There’s no other medium that works better than data visualization to explain these moves in a holistic way. Every video or article I’ve come across would use this data to help explain why it’s effective in certain situations. They present the data as plain text. Though, there are limitations with this because it’s hard to compare these moves for context.

当每一步都有定义它的各种属性时，单单查看这些变量是远远不够的。 此游戏中的数据为数据可视化提供了理想的场所。 没有其他方法可以比数据可视化更好地整体解释这些动作了。 我遇到的每个视频或文章都将使用此数据来帮助解释为什么它在某些情况下有效。 它们以纯文本形式显示数据。 但是，这样做有局限性，因为很难根据上下文比较这些举动。

I notice I’m often drawn to datasets that can be visualized as a form of small multiples. Earlier in 2020, I’ve been making experimental projects that can summarize characteristics of something through small multiples:

我注意到我经常被可以可视化为小倍数形式的数据集吸引。 2020年早些时候，我一直在进行实验性项目，可以通过较小的倍数总结某些事物的特征：

I was drawn to the idea that the world is multifaceted. There isn’t one thing that describes anything. If someone has a medical condition, to understand it, various lifestyle factors needs to be considered: diet, sleep routine, work, relationships, religion, exercise, childhood upbringing, relationships, trauma, and the list goes on. Reality is complex.

我被这个世界多方面的想法所吸引。 没有一件事可以描述任何东西。 要了解某人的病情，要理解它，就需要考虑各种生活方式因素：饮食，睡眠习惯，工作，人际关系，宗教信仰，锻炼，童年抚养，人际关系，外伤，并且清单还在继续。 现实是复杂的。

与《铁拳》社区一起设计 (Designing with the Tekken community)

While developing this project, I engaged with a public forum for the first time. I accessed Tekken players by joining the community where they hung out: the Tekken subreddit. I wasn’t a regular Reddit user so I had to learn how the platform worked as I engaged with the community.

在开发此项目时，我第一次参加了公共论坛。 我通过加入他们参加的社区来访问Tekken玩家： Tekken subreddit 。 我不是Reddit的常规用户，因此我必须学习与社区互动时平台的工作方式。

In total, I engaged with the community four times.

我总共与社区互动了四次。

1. The first was to ask for suggestions and check if what I wanted to make already exists. See it here.

   首先是征求建议，并检查我是否想提出的建议已经存在。 在这里看到它 。

2. The second was to validate my prototype and understand if it was worth pursuing. See it here.

   第二个是验证我的原型并了解它是否值得追求。 在这里看到它 。

3. I reached out to one of the users who had been giving me feedback. I asked them to provide one-on-one feedback because receiving feedback from many users at once was overwhelming and took a toll on me. They gave me feedback on Version 2.
   我与提供反馈的用户之一联系。 我要求他们提供一对一的反馈，因为立即收到许多用户的反馈很不方便，并给我造成了巨大的损失。 他们给了我有关版本2的反馈。

4. I declared the project complete and shared the final version as a public post in order to gauge people’s reactions. See it here.

   我宣布该项目已完成，并以公开形式分享了最终版本，以评估人们的React。 在这里看到它 。

I was naive to engage with people who weren’t familiar with data visualization this way because the response was brutal. All kinds of feedback were thrown at me and I had to pick which suggestions to filter out. Still, I knew that I was onto something interesting. This chaotic process was simply good user-centered design. In order to eliminate the one-sidedness of things and improve overall accessibility, I listened to user problems, understood how they read data, figured out ways to solve their problems.

我很天真地与那些不熟悉数据可视化的人打交道，因为这种回应是残酷的。 我收到了各种各样的反馈，因此我不得不选择要过滤掉的建议。 不过，我知道我正在做一些有趣的事情。 这个混乱的过程仅仅是以用户为中心的良好设计。 为了消除事物的一面性并改善总体可访问性，我听了用户的问题，了解了他们如何读取数据，找到了解决问题的方法。

像公众一样获得公众的反馈 (Taking feedback from the public like a champ)

In designing the first version of this project, I made three mistakes:

在设计该项目的第一个版本时，我犯了三个错误：

1. I made too many assumptions about what my target audience needs. E.g. creating something that helps people build combos, when in reality, learning sample combos provided in the game was sufficient.
   我对目标受众的需求做了很多假设。 例如，创建某种东西可以帮助人们建立连击，而实际上，学习游戏中提供的示例连击就足够了。
2. My target audience was too vague.
   我的目标受众太模糊了。
3. I made a legend that was too complicated.
   我创造了一个太复杂的传说。

My initial goal was to create something that would help beginner to intermediate players better understand the game and help them build tactics and combos to win the game. But, this shifted after creating Version 1.

我最初的目标是创造一种东西，以帮助初学者和中级玩家更好地理解游戏，并帮助他们建立战术和连胜才能赢得游戏。 但是，这在创建版本1后发生了变化。

This visualization shows five moves that Josie Rizal can play. They are part of a list of her most effective moves.

该可视化显示了Josie Rizal可以播放的五种动作。 它们是她最有效的举动清单的一部分。

I shared Version 1 with the Tekken Reddit community and received interesting responses. There were a few that particularly struck me.

我与Tekken Reddit社区共享了版本1， 并收到了有趣的回复 。 有一些特别让我震惊。

The feedback below is negative. But, I found it interesting to read. It hit on something that I always think about. Do people in general find complicated data visualizations overwhelming? Their main point was that I was simply visualizing a data table that people can simply reference. Why bother learning how to read a data visualization if a data table is more accessible? These are interesting points and I think it questions how visualizations need to add a lot of value for people to learn how to read it and access insights.

下面的反馈是负面的。 但是，我发现阅读有趣。 它击中了我一直在想的东西。 总的来说，人们是否发现复杂的数据可视化不胜枚举？ 他们的主要观点是，我只是在可视化人们可以简单引用的数据表。 如果更易于访问数据表，为什么还要学习如何读取数据可视化？ 这些都是有趣的观点，我认为这对可视化如何为人们学习如何阅读和获取见解的附加价值提出了疑问。

This feedback about clarifying my goals is constructive and positive. If the problem and the goal aren’t clearly defined, the result would feel ambiguous. And, this user used the term ‘data viz’. I never used that term in any of my posts to avoid confusing jargon. It seems to me they have familiarity with it and that’s why their feedback was constructive.

关于澄清我的目标的反馈是建设性的和积极的。 如果没有明确定义问题和目标，结果将是模棱两可的。 并且，该用户使用了术语“数据可视化”。 我从未在任何文章中使用该术语来避免混淆术语。 在我看来，他们对此很熟悉，因此他们的反馈具有建设性。

I really liked the honest feedback here. It seems that this user isn’t part of the data visualization or design world, but they did their best describing how I could improve my visualization while tackling the technicalities. It was nice to see them try to break down what worked and what didn’t.

我真的很喜欢这里的诚实反馈。 该用户似乎并不属于数据可视化或设计领域，但他们尽了最大的努力来描述我如何在解决技术问题的同时改善可视化。 很高兴看到他们尝试分解哪些有效，哪些无效。

I’ll be honest, it wasn’t easy reading through all the feedback I got. I felt like I missed the mark on something and wasn’t sure if it was worth it to keep pushing forward. After taking a few days to mentally recover, I adjusted my perspective and started to see the silver lining: I was lucky enough to tap into a community that cared enough to give me such detailed feedback. They love Tekken and that’s why they are active on Reddit. Feedback is never easy to accept and unfortunately, it’s much easier to focus on the negative comments rather than the most insightful ones. I was adamant that I wanted to listen and focus on what people were trying to say. I took a deep breath and began working on the next iteration.

老实说，要阅读我得到的所有反馈并不容易。 我觉得自己错过了某件事的记号，不确定继续前进是否值得。 经过几天的精神恢复之后，我调整了看法并开始看到一线希望：我很幸运地进入了一个社区，这个社区足够关心我，并提供了详尽的反馈。 他们喜欢《铁拳》，这就是为什么他们活跃于Reddit的原因。 反馈绝不容易被接受，不幸的是，关注负面评论而不是最有见地的评论要容易得多。 我坚持要倾听并专注于人们试图说的话。 我深吸了一口气，开始进行下一个迭代。

验证用户需求 (Validating a user need)

It was clear to me what I had to do. I had to start over and reinvent my process. I didn’t have a clear goal because I didn’t have a clear problem to solve. So, I went to the beginner’s megathread on Tekken and went through the questions new players asked, I needed a way to synthesize them. Somehow, I started recording each question I came across on a sticky note.

对我来说很清楚我该怎么做。 我不得不重新开始，重新发明我的过程。 我没有明确的目标，因为我没有明确的问题要解决。 因此，我进入了《铁拳》上初学者的大线程 ，并经历了新玩家提出的问题，我需要一种综合方法。 不知何故，我开始在便笺上记录我遇到的每个问题。

I had about fifteen noted and began arranging them on my desk. The next step was to synthesize. I clustered similar sticky notes together and named each cluster based on their shared similarities. I noticed a trend: all the questions varied on a spectrum of specificity. Questions ranged from being very vague (looking for direction on how to start learning) or very specific (need help with technical aspects of the game). I decided to focus on challenges people had related to character-specific tactics.

我大约有十五个笔记，并开始将它们放在我的书桌上。 下一步是合成。 我将相似的便笺聚在一起，并根据它们的共同相似性为每个聚类命名。 我注意到了一种趋势：所有问题都在特定范围内有所不同。 问题范围从非常模糊(寻找有关如何开始学习的方向)到非常具体(需要游戏技术方面的帮助)。 我决定专注于人们与角色特定策略相关的挑战。

I finally had a breakthrough. I finally felt like I was walking on even ground. I was so lost the whole time because I had no idea what I was doing. Once I identified a specific problem pertaining to a specific target audience, I felt relief. Now, I could back up claims pertaining to why I was doing this and test every possible solution against its problem.

我终于有了突破。 我终于觉得自己在平坦的地面上行走。 我一直迷茫，因为我不知道自己在做什么。 一旦确定了与特定目标受众有关的特定问题，我就会感到宽慰。 现在，我可以备份有关为何执行此操作的声明，并针对其问题测试所有可能的解决方案。

设计什么数据可视化效果最好：物理卡的情况 (Design what data visualization does best: the case for physical cards)

There are a lot of better ways to tell a good story, such as movies, stand-up comedy, graphic novels, music, vlogs, and so much more. Why do it through data?

有很多更好的方法来讲述一个好故事，例如电影，单口喜剧，图画小说，音乐，视频博客等等。 为什么要通过数据呢？

Before I tackled this project, I challenge if data visualization makes sense. For example, the video below is a timeline of the history of women’s rights. It could also exist as a large infographic. Whichever is more appropriate would depend on the context.

在处理这个项目之前，我会挑战数据可视化是否有意义。 例如，以下视频是妇女权利历史的时间表。 它也可以作为大型信息图存在。 哪个更合适将取决于上下文。

What makes a static infographic different from a video in this case? The main difference is comparison, a fundamental function that data visualization enables. In this video, each year is shown one at a time. If you made a large infographic, you could compare multiple years at a time to allow for deeper analysis.

在这种情况下，静态图表与视频有何不同？ 主要区别是比较 ，这是数据可视化实现的基本功能。 在此视频中，每年一次显示一次。 如果您制作了一个大型的信息图，则可以一次比较多年，以便进行更深入的分析。

When working on the Tekken project, I had to understand why data visualization made more sense as a communication medium over other ones. The most important factor to consider was the sequential nature of how information is communicated.

在进行《铁拳》项目时，我必须理解为什么数据可视化作为一种​​交流媒体比其他方式更有意义。 要考虑的最重要因素是信息交流的顺序性。

Let’s break this down.

让我们分解一下。

When it comes to learning about Tekken, players access information through online forums, articles, and YouTube videos. In terms of content, I am competing with articles and videos. Articles and videos are extremely valuable in providing structure. However, there are limitations: the flow of information is sequential and cannot be changed.

在学习铁拳方面，玩家可以通过在线论坛，文章和YouTube视频访问信息。 在内容方面，我正在与文章和视频竞争。 文章和视频在提供结构方面非常有价值。 但是，存在局限性：信息流是顺序的，无法更改。

A video flows scene by scene. The viewer has no control over how information is presented.

视频逐场景流动。 观看者无法控制如何呈现信息。

Similarly, an article is structured by paragraphs. One follows the next and the reader has no control over this.

同样，文章由段落构成。 下一个跟随，读者对此无能为力。

In data visualization, this format is usually seen through scrollytelling visualizations.

在数据可视化中，通常通过滚动式可视化看到这种格式。

To change the nature of information flow so that it’s not sequential, there needs to be a component of interactivity so people can control what they see. This example from The Pudding explores masked wrestlers. There are two ways to experience the data: 1) guided stories and 2) skip the story and explore the data on your own.

为了改变信息流的性质，使其不连续，需要有交互性的组成部分，以便人们可以控制他们看到的东西。 《布丁》中的这个例子探讨了蒙面摔跤手。 可以通过两种方法来体验数据：1)引导故事，以及2)跳过故事并自己探索数据。

The guided stories are sequential and follow a specific order.

引导的故事是连续的，并遵循特定的顺序。

Explore mode allows users to filter as they please based on nationality, decade, and theme of the masks. You have freedom to see all the masks.

探索模式允许用户根据国籍，口罩和口罩主题随意过滤。 您可以自由查看所有口罩。

This is the power of interactivity, it allows for control. In this case, the user can determine what they learn and they can make their own conclusions. This is a healthy mix of both explanatory and exploratory data visualization. Users can follow a pre-determined story and also have a chance to further explore. This is also commonly known as the Martini Glass Structure.

这是交互的力量，它允许控制。 在这种情况下，用户可以确定他们所学的内容，并可以得出自己的结论。 这是说明性和探索性数据可视化的完美结合。 用户可以遵循预定的故事，也有机会进一步探索。 这通常也称为马提尼玻璃结构。

A challenge with a web-based solution in the case of Tekken is that it requires a computer. Players can play Tekken on their computers or on a console. Players who play on their computers will have difficulty referencing data visualization online.

对于Tekken，基于Web的解决方案面临的挑战是它需要一台计算机。 玩家可以在其计算机或控制台上玩《铁拳》。 在其计算机上玩的玩家将难以在线参考数据可视化。

The other option is to create a poster or a printed guide that can be used on-the-go. The challenge here is that information is fixed in place and there’s little control on how it’s presented. The final option I came up with were physical cards. Information is not sequential and it’s still easy to reference while practicing.

另一种选择是创建可随时使用的海报或印刷指南。 这里的挑战是信息固定在适当的位置，几乎没有控制如何呈现的。 我想到的最后一个选择是物理卡。 信息不是连续的，在练习时仍然很容易参考。

What’s interesting about this solution was that the cards are essentially small multiples. Ultimately, the function of these cards is to enable comparison. You can rank them through any variable, create your own categories, and learn how to think about Tekken. I think this format suits this need very well and it adds value in ways that other mediums of communication cannot.

该解决方案有趣的是，这些卡实际上是较小的倍数。 最终，这些卡的功能是进行比较。 您可以通过任何变量对它们进行排名，创建自己的类别以及学习如何思考《铁拳》。 我认为这种格式非常适合此需求，并且以其他通信媒介无法提供的方式增加了价值。

吸引新社区 (Engaging new communities)

I have always been interested in injecting data visualization into the world and letting lay audiences see a different way to view information. Data visualization practitioners are already aware of this potential. The rest of society, not so much. I call what I do a success if I can get people outside of my peer community to see its usefulness. Comedian Ms. Pat describes this really well:

我一直对将数据可视化注入世界并让外行观众看到查看信息的另一种方式感兴趣。 数据可视化从业人员已经意识到了这一潜力。 社会的其余部分，没有那么多。 如果可以让其他人看到同辈社区之外的人，我会说成功就是成功。 喜剧演员Pat女士对此描述得非常好：

If I can make that crazy looking white boy laugh and that old white man laugh, and that thug black boy laugh, that’s when I know the joke gonna work. Cus’ women gonna support women regardless, when I can go out and get a man to laugh at me going through menopause, to me, that’s funny.

如果我可以让那个看上去疯狂的白人男孩笑，那个白人老人笑，那个暴徒黑人男孩笑，那我就知道这个笑话会起作用。 Cus的女人会支持女人，无论何时我可以出去让男人嘲笑我的更年期，对我来说，这很有趣。

Source: Comedian Ms. Pat Shares Her Life Story, Selling Crack, Getting Shot & More, Breakfast Club Power 105.1 FM.

资料来源： 喜剧演员Pat女士分享了她的人生故事，卖力，射击和更多 ， Breakfast Club Power 105.1 FM。

Unfortunately, I think a conundrum that tends to happen in any creative industry, is that our work tends to be seen by our peers, more so than our target audience. Data visualization is no exception. On Twitter, data visualization people follow me. On Instagram, it’s designers. As creators, we rarely get followed by people who could be potential clients. It’s usually peers who follow us because they are interested in our process and techniques. Here’s an excerpt that perfectly captures this:

不幸的是，我认为在任何创意产业中都会出现一个难题，那就是我们的作品更容易被同行看到，而不是目标受众。 数据可视化也不例外。 在Twitter上，数据可视化人员关注我。 在Instagram上，是设计师。 作为创作者，我们很少受到可能成为潜在客户的人们的关注。 通常是同行跟随我们，因为他们对我们的流程和技术感兴趣。 这是摘录，完美地捕捉了这一点：

…The goal of the visualization was to engage readers in finding and telling their own stories in the data. It was hoped that residents in various occupations would engage in social data analysis, sharing expertise from their respective industries. Despite good intentions, the visualization largely failed in this goal. A total of 23 people submitted 62 comments, with 25 of these comments being posted by the producers of the visualization. Other guests pointed out trends of interest and shared pointers to other related data sets; for example, a registered nurse shared his first-hand experiences in the Health sector. However, the majority of posters were not citizens of Minnesota; they were visualization and statistics enthusiasts drawn by the technology (the piece was mentioned on a popular visualization blog) and not by the story.

…可视化的目的是吸引读者在数据中寻找和讲述自己的故事。 希望各种职业的居民参与社会数据分析，共享各自行业的专业知识。 尽管有良好的意图，但可视化在此目标上很大程度上失败了。 共有23个人提交了62条评论，其中25条评论由可视化制作人员发布。 其他嘉宾指出了感兴趣的趋势并共享了其他相关数据集的指针； 例如，一位注册护士分享了他在卫生部门的第一手经验。 但是，大多数张贴者不是明尼苏达州的公民。 他们是该技术吸引的可视化和统计爱好者(该文章在流行的可视化博客中被提及)而不是故事所吸引。

Source: Narrative Visualization: Telling Stories with Data by Edward Segel and Jeffrey Heer.

来源： 叙事可视化：Edward Segel和Jeffrey Heer着的《用数据讲故事》 。

I am thinking more about how to engage relevant audiences to my work. Moving beyond my peer community and going into spaces where I am a nobody. It’s terrifying as heck. But it’s interesting. What made this project worth it at the end was when an experienced Tekken player told me this:

我正在考虑如何使相关受众参与我的工作。 超越我的同伴社区，进入我无人居住的空间。 真可怕。 但这很有趣。 最终使这个项目值得的是一个有经验的《铁拳》玩家告诉我的：

I’ve not seen anyone with your skill set take an interest in Tekken. I’m excited to see the end product. Hopefully, it’ll inspire others to do something similar.

我还没有看到任何具有您的技能的人对《铁拳》感兴趣。 我很高兴看到最终产品。 希望它将激发其他人做类似的事情。

It made my heart skip a beat. It was exactly what I wanted to do. I wanted to explore untapped communities who have never thought that data visualization could do something interesting for them. It’s a small attempt at my vision of visualizing the world.

这让我的心跳了一下。 这正是我想要做的。 我想探索尚未开发的社区，他们从未想到过数据可视化可以为他们带来一些有趣的事情。 这是我对世界进行可视化的一个小尝试。

If you’d like to see more documentation of this project, you can see the five-part blog series and the YouTube process videos I made.

如果您想查看有关该项目的更多文档，则可以查看由五部分组成的博客系列以及我制作的YouTube过程视频 。

Jane Zhang is a data visualization designer based in Toronto, Canada. She designs learning experiences through bespoke visualizations. You can find her work at www.janezhang.ca and connect with her on Twitter, LinkedIn, and Instagram.

Jane Zhang是加拿大多伦多的数据可视化设计师。 她通过定制的可视化设计学习体验。 您可以在 www.janezhang.ca上 找到她的作品， 并通过 Twitter ， LinkedIn 和 Instagram 与她联系 。