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，因為我已經成為一個獨立的數據可視化設計。 我剛開始的時候，遇到的項目與我的興趣或技能無關。 在過去的八個月中，對我來說很清楚，當客戶雇用自由職業者時，他們要么想要：儀表板，交互式可視化或報告。 我不在乎這些。 當我收到此類工作的電子郵件時，總是感到驚訝，因為我的投資組合遠非他們想要的那樣。 我專門研究靜態可視化，旨在實現兩個結果：數據記錄片(如電影記錄片，只是通過數據重新記錄事件)，以及可視化的世界(將數據集成到傳統上不能表達數據的現有格式中，例如食物菜單) )。

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.

我考慮過在2020年6月完全退出數據可視化，然后轉向其他方式。 我最初進入該領域是因為我受到Giorgia Lupi，Nadieh Bremer，Stefanie Posavec，Eleanor Lutz，Mona Chalabi，The Pudding等人的創造性工作的啟發。 我沒有加入這個行業，而是將我的大部分職業生涯都花在了干客戶工作上。 但是，到了八月，我獲得了兩個與我的興趣和技能相匹配的新機會-提醒您，盡管取得了成果，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，獨立市場在可擴展的運營中蓬勃發展：課程，演講，講習班和書籍。 工作通常來自業務分析，研究，健康和非營利組織中的人們。 對于諸如咨詢或服務之類的規模不大的工作，要獲得想要超越標準條形圖的創造性工作的可持續客戶名單是很難的。

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，我該如何前進？ 我回頭看了上一篇文章 然后想起我和之交談的人。 我曾經嘗試過自由職業，教學和演講。 剩下的唯一選擇是開發產品，特別是要出售的印刷作品。 我的技能在靜態數據可視化中蓬勃發展，因此自然而然地打印就可以了。 我不知道要設計預期的東西，例如海報，參考指南或書籍。 但是，我沒有經驗，害怕嘗試一下。

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.

奇跡般地，一個項目構想在6月中旬出現在我的腦海中，它將基于《鐵拳》，這是一款90年代末開始出現的日本3D格斗游戲。 我記得游戲很久了。 當我還是個孩子的時候，哥哥在玩Dreamcast或Playstation 2時會把我放在他旁邊。游戲與我的身份很接近，因此這給了我這個行業另一個機會。

See the full project I did on Tekken.

查看我在《鐵拳》上所做的完整項目 。

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.

我經常認為我們的想法比我們知道的重要 。 我想挑戰數據可視化，看看它是否可以教別人如何思考某件事，而不是專注于事物 。

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.

《金融時報》的圖表顯示了大流行期間在線零售的爆炸性增長。 這個圖表的方法是暴露在大流行期間所發生的網上零售。 讀者可以看到增長Swift，注釋為故事提供了背景。 顯示了什么是數據可視化的一個重要方面的這種做法。 但是，我對展示操作方法更感興趣。

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.

我想到了這樣的想法：在觀看描述視頻游戲戰斗系統的 YouTube視頻之后， 如何展示; 它讓我看到了戰斗的工作方式以及與之相關的更深層次的技巧。 我了解了啟動動畫，這在戰斗中非常重要。 這是動畫，攻擊發生時，就在您的舉動撞擊敵人之前。 輕度攻擊比重度攻擊要快，因為它們在啟動動畫中的幀數較少，而您則更容易受到攻擊，并有可能遭受來自具有重度攻擊的敵人的攻擊。

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.

學習了機械技巧后，我就繼承了戰斗的新觀點。 如果某事的機制被揭示，我能理解的方式 。 曾經嘗試過拆開自動鉛筆嗎？ 我年輕的時候就這樣做了，以消除潛在的麻煩。 這樣幾次之后，我的鉛筆是如何工作的一個大致的了解。

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.

數據可視化如何教會讀者如何思考問題？ 我還沒有一個完整的答案，因為我仍處于思考的早期階段。 但是，通過我自己的反思，一種方法是分隔信息。 就像科學家通過微觀尺度看待世界的方式一樣，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.

我首先從一個名為Bang的棋盤游戲開始探索這個想法。 我通過可視化紙牌的分布來細分游戲的工作方式。 一個簡單的直方圖顯示哪些卡因稀缺而更有價值。 我玩過Bang！ 超過100次，但我從來沒有花時間去思考哪些牌更為稀有。 此外，此可視化效果顯示了游戲的設計方式，有利于進攻性比賽。

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.

而鐵拳項目是我的愿望，展示如何演化而來，沒想到它也徹底改變我的設計過程。

《鐵拳》如何改變我的流程 (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:

我偶然發現了一段精彩的視頻，內容涉及Mick Gordon如何為最新版《毀滅戰士》游戲創作音樂。 在演講中，他重復了以下見解 ：

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

“如果要獲得不同的結果，則必須更改流程。”

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

《鐵拳》催生了我的工作方式的轉變，在新過程中我發現了幾件事。

多維數據 (Multidimensional data)

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

探索Tekken作為數據可視化項目的最誘人的方面是它的多維性。

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《鐵拳7》第3季中，您可以扮演30多個角色之一。 每個角色可以使用100多種戰斗動作。 通常，玩家從擁有一個“主要”角色或他們將要擅長的角色開始。這很重要，因為他們必須學習并掌握其角色最有效的動作，并對如何與其他30個以上的角色作斗爭有一個普遍的認識。 由于需要大量的知識和經驗，因此很難很好地玩這個游戲。

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.

玩家已自愿收集每個角色的每個動作的數據。 流行的網站播放器參考是rbnorway 。 另外，許多用途 Tekken Chicken在移動設備上可以隨時訪問此數據。

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

通常，每個動作均由以下屬性定義：

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.

命令輸入：在控制器上按下以執行移動的按鈕。 命中等級：通常，一招可以擊中身體的三個區域。 高，中和低擊將分別擊中頭部，軀干和腿部。 站立時可以阻擋高中擊，但蹲下時低擊則需要阻擋。 此外，您可以通過蹲伏避開高命中，也可以通過跳躍避開低命中。 請記住，這是一個一般規則。 傷害：如果成功擊中對手，您將消耗的生命值。 開機畫面：每步動作在擊中對手之前都會有一個動畫。 一些動作的動畫時間較短(即幀數較少)。 幀數越少，移動速度越快，您越容易受到攻擊。 擊中跟進幀：當攻擊擊中沒有阻擋或處于動畫中間的對手時，您可以使用正負幀來幫助您更有效地鏈接動作。 這種現象也稱為框架優勢/劣勢。 格擋中的跟進框：與命中時的框相似，但是在對手擋住自己的舉動時適用。 反擊時的后續幀：與擊中時的幀相似，但是這適用于當對手處于動畫中間時您的移動著陸的情況。

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.

如果這太令人困惑，那么這是我制作的一段簡短的視頻，描述了這些數據。 我添加了另一個可變的有效范圍。 這是指您仍然可以移動的最大距離。

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.

當每一步都有定義它的各種屬性時，單單查看這些變量是遠遠不夠的。 此游戲中的數據為數據可視化提供了理想的場所。 沒有其他方法可以比數據可視化更好地整體解釋這些動作了。 我遇到的每個視頻或文章都將使用此數據來幫助解釋為什么它在某些情況下有效。 它們以純文本形式顯示數據。 但是，這樣做有局限性，因為很難根據上下文比較這些舉動。

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:

我注意到我經常被可以可視化為小倍數形式的數據集吸引。 2020年早些時候，我一直在進行實驗性項目，可以通過較小的倍數總結某些事物的特征：

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.

我被這個世界多方面的想法所吸引。 沒有一件事可以描述任何東西。 要了解某人的病情，要理解它，就需要考慮各種生活方式因素：飲食，睡眠習慣，工作，人際關系，宗教信仰，鍛煉，童年撫養，人際關系，外傷，并且清單還在繼續。 現實是復雜的。

與《鐵拳》社區一起設計 (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.

在開發此項目時，我第一次參加了公共論壇。 我通過加入他們參加的社區來訪問Tekken玩家： Tekken subreddit 。 我不是Reddit的常規用戶，因此我必須學習與社區互動時平臺的工作方式。

In total, I engaged with the community four times.

我總共與社區互動了四次。

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

   首先是征求建議，并檢查我是否想提出的建議已經存在。 在這里看到它 。

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

   第二個是驗證我的原型并了解它是否值得追求。 在這里看到它 。

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.
   我與提供反饋的用戶之一聯系。 我要求他們提供一對一的反饋，因為立即收到許多用戶的反饋很不方便，并給我造成了巨大的損失。 他們給了我有關版本2的反饋。

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.

   我宣布該項目已完成，并以公開形式分享了最終版本，以評估人們的React。 在這里看到它 。

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.

我很天真地與那些不熟悉數據可視化的人打交道，因為這種回應是殘酷的。 我收到了各種各樣的反饋，因此我不得不選擇要過濾掉的建議。 不過，我知道我正在做一些有趣的事情。 這個混亂的過程僅僅是以用戶為中心的良好設計。 為了消除事物的一面性并改善總體可訪問性，我聽了用戶的問題，了解了他們如何讀取數據，找到了解決問題的方法。

像公眾一樣獲得公眾的反饋 (Taking feedback from the public like a champ)

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

在設計該項目的第一個版本時，我犯了三個錯誤：

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.
   我對目標受眾的需求做了很多假設。 例如，創建某種東西可以幫助人們建立連擊，而實際上，學習游戲中提供的示例連擊就足夠了。
2. My target audience was too vague.
   我的目標受眾太模糊了。
3. I made a legend that was too complicated.
   我創造了一個太復雜的傳說。

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.

我最初的目標是創造一種東西，以幫助初學者和中級玩家更好地理解游戲，并幫助他們建立戰術和連勝才能贏得游戲。 但是，這在創建版本1后發生了變化。

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

該可視化顯示了Josie Rizal可以播放的五種動作。 它們是她最有效的舉動清單的一部分。

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

我與Tekken Reddit社區共享了版本1， 并收到了有趣的回復 。 有一些特別讓我震驚。

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.

下面的反饋是負面的。 但是，我發現閱讀有趣。 它擊中了我一直在想的東西。 總的來說，人們是否發現復雜的數據可視化不勝枚舉？ 他們的主要觀點是，我只是在可視化人們可以簡單引用的數據表。 如果更易于訪問數據表，為什么還要學習如何讀取數據可視化？ 這些都是有趣的觀點，我認為這對可視化如何為人們學習如何閱讀和獲取見解的附加價值提出了疑問。

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.

關于澄清我的目標的反饋是建設性的和積極的。 如果沒有明確定義問題和目標，結果將是模棱兩可的。 并且，該用戶使用了術語“數據可視化”。 我從未在任何文章中使用該術語來避免混淆術語。 在我看來，他們對此很熟悉，因此他們的反饋具有建設性。

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.

我真的很喜歡這里的誠實反饋。 該用戶似乎并不屬于數據可視化或設計領域，但他們盡了最大的努力來描述我如何在解決技術問題的同時改善可視化。 很高興看到他們嘗試分解哪些有效，哪些無效。

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.

老實說，要閱讀我得到的所有反饋并不容易。 我覺得自己錯過了某件事的記號，不確定繼續前進是否值得。 經過幾天的精神恢復之后，我調整了看法并開始看到一線希望：我很幸運地進入了一個社區，這個社區足夠關心我，并提供了詳盡的反饋。 他們喜歡《鐵拳》，這就是為什么他們活躍于Reddit的原因。 反饋絕不容易被接受，不幸的是，關注負面評論而不是最有見地的評論要容易得多。 我堅持要傾聽并專注于人們試圖說的話。 我深吸了一口氣，開始進行下一個迭代。

驗證用戶需求 (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.

對我來說很清楚我該怎么做。 我不得不重新開始，重新發明我的過程。 我沒有明確的目標，因為我沒有明確的問題要解決。 因此，我進入了《鐵拳》上初學者的大線程 ，并經歷了新玩家提出的問題，我需要一種綜合方法。 不知何故，我開始在便箋上記錄我遇到的每個問題。

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.

我大約有十五個筆記，并開始將它們放在我的書桌上。 下一步是合成。 我將相似的便箋聚在一起，并根據它們的共同相似性為每個聚類命名。 我注意到了一種趨勢：所有問題都在特定范圍內有所不同。 問題范圍從非常模糊(尋找有關如何開始學習的方向)到非常具體(需要游戲技術方面的幫助)。 我決定專注于人們與角色特定策略相關的挑戰。

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.

我終于有了突破。 我終于覺得自己在平坦的地面上行走。 我一直迷茫，因為我不知道自己在做什么。 一旦確定了與特定目標受眾有關的特定問題，我就會感到寬慰。 現在，我可以備份有關為何執行此操作的聲明，并針對其問題測試所有可能的解決方案。

設計什么數據可視化效果最好：物理卡的情況 (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?

有很多更好的方法來講述一個好故事，例如電影，單口喜劇，圖畫小說，音樂，視頻博客等等。 為什么要通過數據呢？

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.

在處理這個項目之前，我會挑戰數據可視化是否有意義。 例如，以下視頻是婦女權利歷史的時間表。 它也可以作為大型信息圖存在。 哪個更合適將取決于上下文。

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.

在這種情況下，靜態圖表與視頻有何不同？ 主要區別是比較 ，這是數據可視化實現的基本功能。 在此視頻中，每年一次顯示一次。 如果您制作了一個大型的信息圖，則可以一次比較多年，以便進行更深入的分析。

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.

在進行《鐵拳》項目時，我必須理解為什么數據可視化作為一種??交流媒體比其他方式更有意義。 要考慮的最重要因素是信息交流的順序性。

Let’s break this down.

讓我們分解一下。

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.

在學習鐵拳方面，玩家可以通過在線論壇，文章和YouTube視頻訪問信息。 在內容方面，我正在與文章和視頻競爭。 文章和視頻在提供結構方面非常有價值。 但是，存在局限性：信息流是順序的，無法更改。

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

視頻逐場景流動。 觀看者無法控制如何呈現信息。

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

同樣，文章由段落構成。 下一個跟隨，讀者對此無能為力。

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

在數據可視化中，通常通過滾動式可視化看到這種格式。

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.

為了改變信息流的性質，使其不連續，需要有交互性的組成部分，以便人們可以控制他們看到的東西。 《布丁》中的這個例子探討了蒙面摔跤手。 可以通過兩種方法來體驗數據：1)引導故事，以及2)跳過故事并自己探索數據。

The guided stories are sequential and follow a specific order.

引導的故事是連續的，并遵循特定的順序。

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.

探索模式允許用戶根據國籍，口罩和口罩主題隨意過濾。 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.

這是交互的力量，它允許控制。 在這種情況下，用戶可以確定他們所學的內容，并可以得出自己的結論。 這是說明性和探索性數據可視化的完美結合。 用戶可以遵循預定的故事，也有機會進一步探索。 這通常也稱為馬提尼玻璃結構。

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.

對于Tekken，基于Web的解決方案面臨的挑戰是它需要一臺計算機。 玩家可以在其計算機或控制臺上玩《鐵拳》。 在其計算機上玩的玩家將難以在線參考數據可視化。

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.

另一種選擇是創建可隨時使用的海報或印刷指南。 這里的挑戰是信息固定在適當的位置，幾乎沒有控制如何呈現的。 我想到的最后一個選擇是物理卡。 信息不是連續的，在練習時仍然很容易參考。

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.

該解決方案有趣的是，這些卡實際上是較小的倍數。 最終，這些卡的功能是進行比較。 您可以通過任何變量對它們進行排名，創建自己的類別以及學習如何思考《鐵拳》。 我認為這種格式非常適合此需求，并且以其他通信媒介無法提供的方式增加了價值。

吸引新社區 (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:

我一直對將數據可視化注入世界并讓外行觀眾看到查看信息的另一種方式感興趣。 數據可視化從業人員已經意識到了這一潛力。 社會的其余部分，沒有那么多。 如果可以讓其他人看到同輩社區之外的人，我會說成功就是成功。 喜劇演員Pat女士對此描述得非常好：

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.

如果我可以讓那個看上去瘋狂的白人男孩笑，那個白人老人笑，那個暴徒黑人男孩笑，那我就知道這個笑話會起作用。 Cus的女人會支持女人，無論何時我可以出去讓男人嘲笑我的更年期，對我來說，這很有趣。

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

資料來源： 喜劇演員Pat女士分享了她的人生故事，賣力，射擊和更多 ， 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，我認為在任何創意產業中都會出現一個難題，那就是我們的作品更容易被同行看到，而不是目標受眾。 數據可視化也不例外。 在Twitter上，數據可視化人員關注我。 在Instagram上，是設計師。 作為創作者，我們很少受到可能成為潛在客戶的人們的關注。 通常是同行跟隨我們，因為他們對我們的流程和技術感興趣。 這是摘錄，完美地捕捉了這一點：

…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.

…可視化的目的是吸引讀者在數據中尋找和講述自己的故事。 希望各種職業的居民參與社會數據分析，共享各自行業的專業知識。 盡管有良好的意圖，但可視化在此目標上很大程度上失敗了。 共有23個人提交了62條評論，其中25條評論由可視化制作人員發布。 其他嘉賓指出了感興趣的趨勢并共享了其他相關數據集的指針； 例如，一位注冊護士分享了他在衛生部門的第一手經驗。 但是，大多數張貼者不是明尼蘇達州的公民。 他們是該技術吸引的可視化和統計愛好者(該文章在流行的可視化博客中被提及)而不是故事所吸引。

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

來源： 敘事可視化：Edward Segel和Jeffrey Heer著的《用數據講故事》 。

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:

我正在考慮如何使相關受眾參與我的工作。 超越我的同伴社區，進入我無人居住的空間。 真可怕。 但這很有趣。 最終使這個項目值得的是一個有經驗的《鐵拳》玩家告訴我的：

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.

我還沒有看到任何具有您的技能的人對《鐵拳》感興趣。 我很高興看到最終產品。 希望它將激發其他人做類似的事情。

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.

這讓我的心跳了一下。 這正是我想要做的。 我想探索尚未開發的社區，他們從未想到過數據可視化可以為他們帶來一些有趣的事情。 這是我對世界進行可視化的一個小嘗試。

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.

如果您想查看有關該項目的更多文檔，則可以查看由五部分組成的博客系列以及我制作的YouTube過程視頻 。

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是加拿大多倫多的數據可視化設計師。 她通過定制的可視化設計學習體驗。 您可以在 www.janezhang.ca上 找到她的作品， 并通過 Twitter ， LinkedIn 和 Instagram 與她聯系 。