The following post is a summarized version of the article accepted to the 2020 Visualization for Communication workshop as part of the 2020 IEEE VIS conference to be held in October 2020. The full paper has been published as an OSF Preprint and can be accessed here.

以下文章是 作為2020年10月舉行的2020 IEEE VIS會議的一部分而 被 2020可視化通信研討會 接受的文章的摘要 。全文已作為OSF預印本發布，可在 此處 訪問 。

The Urban Institute’s mission is to open minds, shape decisions, and offer solutions. To advance that mission, we must think intentionally about how we can learn from and speak to audiences who reflect the rich diversity of America’s communities, and we must foster a culture where employees from different backgrounds and perspectives enjoy mutual respect, inclusivity, and collegiality.

城市學院的使命是開放思想，制定決策并提供解決方案。 為了實現這一使命，我們必須認真思考如何向反映美國社區豐富多樣性的聽眾學習和與之交談，并且我們必須培育一種文化，使來自不同背景和不同視角的員工享有相互尊重，包容性和合作性。

For the past few months, we have been working on revising and updating Urban’s Data Visualization Style Guide. In addition to reorganizing the guide and updating various style decisions, we have also been thinking critically about how we communicate data and information about the groups we study. Just as Urban has carefully considered the words we use in our written reports and platforms, we should be equally careful in how we visually present data to our readers, users, and audiences and what words we should use in and around those visuals. (We are also working on a separate section about accessibility in data visualization, which we will share at a later date.)

在過去的幾個月中，我們一直在致力于修訂和更新Urban的《 數據可視化樣式指南》 。 除了重新組織指南和更新各種樣式決策外，我們還一直在認真思考如何交流有關所研究小組的數據和信息。 正如Urban仔細考慮了我們在書面報告和平臺中使用的詞語一樣，我們也應同樣謹慎地以視覺方式將數據呈現給讀者，用戶和聽眾，以及在這些視覺內容中和周圍使用哪些詞語。 (我們還在研究數據可視化中的可訪問性的單獨部分，稍后將進行分享。)

Although more people are thinking and writing about these issues, there hasn’t been much agreement around best practices for taking an equity lens to data visualization, especially as it applies to setting standards for entire organizations. As best we can, we have been reading a variety of posts and papers (a short list can be found below) and discussing ways we can develop a more diverse, equitable, and inclusive approach to presenting and visualizing data. We view this effort as just the beginning of our process and anticipate growing and expanding our work as we learn more and receive feedback from colleagues, partners, and readers.

盡管有更多的人正在思考和寫這些問題，但是關于最佳實踐的共識還不足以使數據可視化，特別是因為它適用于為整個組織設置標準。 盡我們所能，我們一直在閱讀各種文章和論文(可以在下面找到一個簡短列表)，并討論了我們可以開發出一種更加多樣化，公平和包容的方法來呈現和可視化數據的方法。 我們認為這項工作只是我們過程的開始，隨著我們了解更多并收到同事，合作伙伴和讀者的反饋，我們預計我們的工作會不斷擴大和擴展。

To that end, we have identified eight areas in which researchers, analysts, and anyone working with data can be more inclusive in how they present their data.

為此，我們確定了八個領域，在這些領域中，研究人員，分析人員以及任何處理數據的人都可以在他們如何展示數據時更具包容性。

Using language with a racial equity awareness. Titles, text, and labels are among the first things readers scan (PDF) when encountering a chart, so they present an important opportunity to apply racial equity awareness thinking. Urban researchers often rely on our organization’s guides for accessible and inclusive language for labeling and annotating graphs. Consistent with Urban style, we want our researchers to label their data using people-first language, such as “people with disabilities” rather than “disabled people.” Terms should also refer to people and not strictly to their skin color; for example, “Black people” not “Blacks.”

使用具有種族平等意識的語言。 標題，文本和標簽是讀者在遇到圖表時首先掃描 (PDF)的內容，因此它們為應用種族平等意識提供了重要的機會。 城市研究人員通常依靠我們組織的指南來獲取易于使用且具有包容性的語言來標記和注釋圖形。 與城市風格一致，我們希望研究人員使用以人為本的語言來標記數據，例如“殘疾人”而不是“殘疾人”。 術語還應指人，而不是指膚色。 例如，“黑人”而不是“黑人”。

As just one example, in a June 2020 project, a series of bivariate choropleth maps showed the relationship between race and poverty. In the original visualization, the labels along the legend were “More Poverty” and “More Black.” That language is not inclusive of different groups: poverty refers to an experience not a static description and “More Black” references skin color, not people. A more inclusive way to label the legend might be “Larger proportion of people experiencing poverty” and “Larger Black Population” (the author of the visualization did later change “More Black” to “Larger Black Population”).

僅舉一個例子，在2020年6月的一個項目中 ，一系列的雙變量Choroppleth圖顯示了種族與貧困之間的關系。 在原始可視化中，圖例上的標簽是“更多貧困”和“更多黑色”。 該語言不包括不同的群體：貧窮是指一種體驗，而不是一成不變的描述，“更多黑人”是指膚色，而不是人。 標記傳說的一種更具包容性的方式可能是“更多的貧困人口”和“更大的黑人人口”(可視化的作者后來將“更多黑人”改為“更大的黑人人口”)。

The terms and phrases we use continue to change. In writing about terminology around and about people with disabilities, Nicolas Steenhout writes, “Disability language is never straightforward. It’s always nuanced. It always evolves.” That sentiment can extend to any underrepresented group, so as researchers, developers, and designers, we need to monitor the current lexicon and reflect the experiences of the people we study and the people we communicate with. Graph creators may want to consider including a footnote or endnote explaining why a particular term was used. We also encourage researchers to talk to their target audiences to give them the option to self-identify their preferred terminology.

我們使用的術語和短語會不斷變化。 Nicolas Steenhout在撰寫有關殘疾人的術語時，寫道：“殘疾人語言從來都不是簡單易懂的。 總是很細微。 它總是在發展。” 這種情緒可以擴展到任何代表性不足的人群，因此，作為研究人員，開發人員和設計師，我們需要監視當前的詞典，并反映我們研究的人員和與之交流的人員的經驗。 圖形創建者可能需要考慮添加腳注或尾注，以說明使用特定術語的原因。 我們還鼓勵研究人員與目標受眾交談，以使他們可以選擇自己確定自己喜歡的術語。

Ordering data labels in a purposeful way. Many of our graphs and tables include different demographic groups, ordered simply as they appear in the raw data. There is likely little thought given to how estimates in tables or bars in graphs or small multiples are ordered, such as “White,” “Black,” “Hispanic,” and “Other.” As graph producers, however, we should take a more active role in choosing how to order and present data values for different groups. Which group we choose to show as the first row in a table or the first bar in a graph can affect how readers perceive the relationship or hierarchy between groups; always starting with “White” or “Men” can make these groups appear as the default against which other groups should be compared, suggesting they’re the most important populations. How we choose to order may also reflect who we view as the intended audience for our visualizations. Starting with “White” or “Men” can make it seem as though those are the most important groups we are trying to communicate with. Urban does not have a universal rule that applies to all visuals, but a few issues are worth considering:

有目的地訂購數據標簽。 我們的許多圖形和表格都包含不同的人口統計組，其排序方式與原始數據中的顯示方式相同。 幾乎沒有考慮過如何排列表或條形圖中的估計值或小的倍數，例如“白色”，“黑色”，“西班牙裔”和“其他”。 但是，作為圖形制作者，我們應該在選擇如何排序和顯示不同組的數據值方面扮演更積極的角色。 我們選擇顯示哪個組作為表的第一行或圖形的第一條，會影響讀者對組之間關系或層次結構的理解； 始終以“白人”或“男性”開頭可以使這些組作為應與其他組進行比較的默認值，表明它們是最重要的人群。 我們選擇訂購的方式也可能反映出我們將哪些人視為可視化的目標受眾。 以“白人”或“男子”開頭可以使他們似乎是我們嘗試與之交流的最重要的群體。 Urban沒有適用于所有視覺效果的通用規則，但有幾個問題值得考慮：

· Does your study focus on a particular community? If it does, that group should be presented first.

·您的研究重點是特定社區嗎？ 如果確實如此，則應首先提出該小組。

· Is there a particular argument or story you are trying to tell? If so, the order or presentation of results should reflect that argument.

·您要講一個特定的論點或故事嗎？ 如果是這樣，結果的順序或表示應反映該論點。

· Is there a quantitative relationship that can guide how the groups are ordered? Can they be sorted alphabetically or by population size, sample size (weighted or unweighted), or magnitude or effect of the results?

·是否存在可以指導各組訂購方式的定量關系？ 可以按字母順序或按人口規模，樣本規模(加權或未加權)或結果的大小或影響對它們進行排序嗎？

Considering the missing groups. It is also important to acknowledge who is and is not included in our data and charts. Many charts on race and ethnicity only show white people, Black people, and Latinx people but not smaller racial or ethnic groups. Often this is because of data limitations; in particular, small sample sizes. But even in those cases, how can researchers be more proactive to help organizations conducting surveys be more inclusive? How can we communicate to those organizations to help them conduct better surveys? Just because it may be harder to obtain data about certain groups doesn’t mean we shouldn’t still try to better understand their lives.

考慮缺少的群體。 同樣重要的是要確認數據和圖表中包含和不包含誰。 許多種族和族裔圖表僅顯示白人，黑人和拉丁裔，但沒有顯示較小的種族或族裔群體。 通常這是由于數據限制； 特別是樣本量小。 但是即使在那種情況下，研究人員又如何能更加主動地幫助組織進行調查，使其更具包容性？ 我們如何與這些組織溝通以幫助他們進行更好的調查？ 僅僅因為獲取有關某些群體的數據可能更加困難并不意味著我們不應該仍然試圖更好地了解他們的生活。

Similarly, charts showing breakdowns by gender often neglect nonbinary and transgender people by only presenting males and females. Again, many major surveys, especially at the federal level, do not include these groups as response options, but we should be asking ourselves how and to what extent we should note omitted groups and when. Should researchers assume readers will recognize that certain datasets treat gender as binary? How can we anticipate that assumption?

同樣，顯示按性別細分的圖表通常只顯示男性和女性而忽略了非二元和跨性別的人。 同樣，許多大型調查，尤其是在聯邦一級，并未將這些群體作為應對方案，但我們應該問自己，如何以及在何種程度上應注意被遺漏的群體以及何時出現。 研究人員應該假設讀者會認識到某些數據集將性別視為二元嗎？ 我們如何預計這個假設？

Another topic we’ve been considering is how to avoid lumping groups with few observations into the seemingly innocuous “Other” category. In some cases, the “Other” category may be necessary to achieve sufficient sample size for statistical analyses — in which case, we should ask ourselves if it is appropriate to lump these groups together (do they show the same trends in the data?) and be sure to clearly define in the notes section of the chart who is included in this category. But in other cases, we may use the “Other” category as a shortcut to make the presentation and related writing easier. Should we start considering labels besides “Other,” which can have an exclusionary connotation? Even though it might take more words, maybe we should use the appropriate name for each group in our tables, charts, and text.

我們一直在考慮的另一個主題是，如何避免在看似無害的“其他”類別中很少觀察到的結成團。 在某些情況下，可能需要“其他”類別才能獲得足夠的樣本量進行統計分析-在這種情況下，我們應該問自己是否適合將這些組放在一起(它們在數據中顯示出相同的趨勢嗎？)并確保在圖表的“注釋”部分中明確定義此類別中的對象。 但是在其他情況下，我們可以使用“其他”類別作為捷徑，以簡化演示文稿和相關寫作。 除了“其他”以外，我們是否應該開始考慮可能具有排他性含義的標簽？ 即使可能需要更多的單詞，也許我們應該為表格，圖表和文本中的每個組使用適當的名稱。

In cases where data were collected about a specific group but that group was not presented separately in the chart, it may be worth listing in the chart’s notes all racial and ethnic groups included in the original dataset. This can both inform readers that data do exist for these smaller groups (even though they may be limited) and offer transparency about the chart maker’s decision about groups they included in their visualization.

如果收集的是有關特定人群的數據，但圖表中未單獨顯示該人群，則可能值得在圖表注釋中列出原始數據集中包含的所有種族和民族。 這既可以告知讀者這些較小的組確實存在數據(即使它們可能是有限的)，也可以使圖表制作者對其可視化中所包含的組的決策保持透明。

Using colors with a racial equity awareness. Urban’s color palette is consistent for people with certain color vision deficiencies, and the contrast between those colors and white and black text meet basic accessibility guidelines. Urban does not use color palettes that reinforce gender or racial stereotypes, such as baby pink and baby blue to represent women and men. We have not set a specific standard for which Urban color refers to which gender group or racial group, although we certainly recommend that researchers avoid using colors associated with skin tones (or worse, racial stereotypes).

使用具有種族平等意識的色彩。 Urban的調色板對于具有某些色覺缺陷的人們是一致的，并且這些顏色與黑白文本之間的對比度符合基本的輔助功能準則 。 Urban不使用會增強性別或種族刻板印象的調色板，例如淡粉紅色和淡藍色代表女性和男性。 盡管我們當然建議研究人員避免使用與膚色有關的顏色(或更糟的是種族刻板印象)，但我們尚未為城市色彩指的是哪個性別組或種族組制定具體標準。

As an example, the Massachusetts Institute of Technology Office of the Provost’s “Diversity Dashboard” enables users to explore demographic characteristics of the school’s students, faculty, and staff. The June 2020 version of the dashboard used three distinct hues to represent nine racial and ethnic groups. Five of the distinct race and ethnicity groups were represented with red, a similar lighter pink color for the “Two or more races” category, two shades of gray for the “International” and “Unknown” groups, and blue for the “White” group.

例如，麻省理工學院教務長的“多元化儀表板”辦公室使用戶能夠探索學校學生，教職員工的人口統計學特征。 2020年6月版的儀表板使用了三種不同的色調來代表9個種族和族裔群體。 五個不同的種族和種族組分別用紅色表示，“兩個或多個種族”類別用類似的淺粉色表示，“國際”和“未知”組用兩種灰色表示，“白色”用藍色表示組。

There are multiple problems with this design. First, the red color ramp for the five groups on the left side of the legend others those groups and creates a visual divide that seems to position nonwhite students against white students. Secondly, the gray colors tend to fade to the background, which diminishes international students and students whose race or ethnicity is unknown. All of this creates an effect where the “White” category moves to the foreground and is highlighted, as if it is the most important group and the norm against which all other groups should be compared. An update to the dashboard in July 2020 partly remedied the color issues by using more hues and creating larger breaks between the now-blue color ramp.

這種設計存在多個問題。 首先，圖例左側的五個組的紅色漸變與其他組一樣，并創建了視覺上的鴻溝，似乎使非白人學生與白人學生相對應。 其次，灰色趨于淡化為背景 ，從而減少了國際學生和種族或種族未知的學生。 所有這一切都產生了一種效果，“白色”類別移到了最前面并被突出顯示，好像它是最重要的組以及所有其他組都應與之進行比較的規范。 2020年7月對儀表盤進行的更新部分通過使用更多的色調并在現在的藍色漸變之間創建了較大的間隔來部分解決了顏色問題。

In general, as data visualization producers, we need to be aware of how our use of colors, words, and categorizations can perpetuate or exacerbate inequities.

通常，作為數據可視化生成器，我們需要意識到我們對顏色，文字和分類的使用如何使不平等現象長期存在或加劇。

Using icons and shapes with a racial equity awareness. We want to be careful and thoughtful when using icons in any data visualization. When showing groups of people, we should consider a mix of genders, races, and ethnicities. We don’t often use icons at Urban, but when we do, we should consider to whom we are presenting our results and how our icons might be perceived. We need to be conscious of how certain icons may not correspond to the content, such as an icon of a baby in a chart about child mortality.

使用具有種族平等意識的圖標和形狀。 在任何數據可視化中使用圖標時，我們都需要謹慎周到。 在顯示人群時，我們應考慮性別，種族和種族的混合。 我們在Urban并不經常使用圖標，但是當我們這樣做時，我們應該考慮向誰展示我們的結果以及如何看待我們的圖標。 我們需要意識到某些圖標可能與內容不符，例如關于兒童死亡率的圖表中的嬰兒圖標。

Mis- or underrepresentation of certain groups in imagery and iconography also fails to take a racial or gender equity awareness perspective toward our data visualization work. A 2018 study by the Pew Research Center found that “men are overrepresented in online image search results across a majority of jobs examined” and that “women appear lower than men in such search results for many jobs.” This disparity continues to this day; the image on the left shows Google search results for the phrase “nurse icon,” and the image on the right shows results for the phrase “boss icon.” Notice how nearly all the images on the top are similar to images we might imagine as feminine and the images on the bottom we might traditionally consider masculine.

在圖像和圖像學中某些群體的代表性不足或代表性不足，也無法從種族或性別平等意識的角度來對待我們的數據可視化工作。 皮尤研究中心(Pew Research Center)于2018年進行的一項研究發現，“在所檢查的大多數工作中，男性在在線圖像搜索結果中所占的比例過高”，并且“在許多工作中，女性在此類搜索結果中的表現低于男性。” 這種差距一直持續到今天。 左圖顯示短語“護士圖標”的Google搜索結果，右圖顯示短語“老板圖標”的搜索結果。 請注意，頂部幾乎所有圖像與我們可能想象為女性的圖像相似，而底部傳統上我們通常認為是男性的圖像幾乎相似。

Data visualization creators should ensure a variety of races and genders are depicted when using icons and avoid icons that make inappropriate depictions of people or communities or reinforce stereotypes.

數據可視化創建者應確保在使用圖標時能夠描繪出各種種族和性別，并避免使用圖標對人或社區進行不恰當的描繪或加強刻板印象。

Demonstrating empathy. One of the big challenges in visualizing data, and quantitative research in general, is the ability to help readers connect with the content. Standard graphs like bar charts, line charts, and pie charts, while informative, can abstract from the content and people being represented. Taking an empathetic view of the reader’s needs as they read or perceive information is one step to better data communication. This kind of empathy is often couched in terms of producing specific graphs that meet the needs and expertise of our readers. But viewing empathy through a diversity, equitability, and inclusivity (DEI) lens would mean considering how the specific lived experiences and perspectives of our readers (not to mention the actual study populations) will perceive the information. As journalist Kim Bui wrote in 2019, “approaching stories — and people — with more empathy creates better relationships with marginalised communities, builds trust and increases diverse coverage.”

表現出同理心。 通常，在可視化數據和進行定量研究方面，最大的挑戰之一是如何幫助讀者與內容建立聯系。 條形圖，折線圖和餅圖之類的標準圖形雖然內容豐富，但可以從所代表的內容和人物中抽象出來。 對讀者在閱讀或感知信息時的需求持同理心的態度是更好地進行數據交流的第一步 。 這種同理心通常表現在產生滿足我們讀者需求和專業知識的特定圖表方面。 但是，通過多樣性，公平性和包容性(DEI)視角來觀察同情心，就意味著要考慮讀者的具體生活經歷和觀點(更不用說實際的研究人群)將如何理解這些信息。 正如記者金布伊 ( Kim Bui )在2019年寫道，“以更多的同情心對待故事和人們，可以與邊緣化社區建立更好的關系，建立信任并擴大覆蓋面。”

Empathy in data visualization also extends to when we use abstract shapes versus shapes that resemble people or communities, which can evoke a little more humanity. Graphics that specifically represent people — the anthropomorphizing of data graphics, or, as Jeremy Boy and colleagues (PDF) refer to them, “anthropographics” — is sometimes seen as a way to evoke empathy (though Boy and his coauthors do not find this to be the case; see also Groeger). The idea of helping readers understand the “near and far” — a wide lens of overall metrics or data paired with individual- or group-level data — may be a way to help readers connect with content. The guiding principle here is to put people first and help the reader better understand and recognize the people behind the data. “One possibility,” web developer Jacob Harris wrote, “is that if your data is about people, make it extremely clear who they are or were.”

數據可視化的同理心也延伸到我們使用抽象形狀而不是類似于人或社區的形狀時，這可以喚起更多人性化。 專門代表人的圖形-數據圖形的擬人化，或者，正如Jeremy Boy及其同事 (PDF)所稱的“人類學”-有時被視為喚起共情的一種方式(盡管Boy和他的合著者并未發現這種共鳴)實際情況；另請參閱Groeger )。 幫助讀者理解“ 近與遠 ”的想法(可以將總??體指標或數據與個人或團體級別的數據配對使用)可以幫助讀者與內容建立聯系 。 這里的指導原則是以人為本，幫助讀者更好地理解和認識數據背后的人。 網絡開發人員雅各布·哈里斯(Jacob Harris)寫道：“一種可能性是，如果您的數據是關于人的，則要非常清楚他們是誰。”

Another way to think about empathy in data visualization is whether particular chart types lend themselves to more of a human connection than others. For example, bar charts represent each data value with a single rectangle. On the other hand, unit charts and waffle charts visualize each data point using a collection of smaller individual shapes.

在數據可視化中思考移情的另一種方法是，特定的圖表類型是否比其他圖表類型更適合人類。 例如，條形圖用單個矩形表示每個數據值。 另一方面，單位圖和華夫餅圖使用較小的單個形狀的集合可視化每個數據點。

The former approach could be seen as more abstract, collapsing all of the people reflected in that data point into just one shape, while the latter chart type might offer more of an opportunity to connect with the subject by reminding readers of the number of people represented through the use of multiple individual shapes, particularly if each dot represents one person.

前一種方法可以看作是更抽象的方法，它將反映在該數據點中的所有人員都折疊成一個形狀，而后一種圖表類型可以通過提醒讀者代表的人數來提供更多與該主題聯系的機會通過使用多個單獨的形狀，特別是如果每??個點代表一個人時。

Connecting directly with people and communities and trying to better understand their lived experiences can help content producers create visualizations and tell stories that better reflect the true experiences of different people. This connection may come more naturally for journalists than for data scientists, but we believe this is a key dimension for researchers to explore to help them put their work into the hands of policymakers, stakeholders, community members, and changemakers who can use it to affect change. Furthermore, inclusive and thoughtful data visualization that respectfully reflects the people we are studying can also help us build trust with those communities.

直接與人和社區聯系并嘗試更好地了解他們的生活經驗，可以幫助內容制作者創建可視化效果并講故事，以更好地反映不同人的真實經歷。 對于記者而言，這種聯系可能比對數據科學家而言更自然，但是我們認為，這是研究人員探索以幫助他們將工作交給決策者，利益相關者，社區成員和變革者手中的一個關鍵方面，他們可以使用它來影響更改。 此外，具有包容性和周到的數據可視化能夠恭敬地反映我們正在研究的人員，也可以幫助我們建立與這些社區的信任。

Questioning default visualization approaches. It is often reflexive when presenting data disaggregated by race or ethnicity to plot all of the groups on the same chart. However, as Pieta Blakely writes, doing so “encourages [the reader] to compare each of the groups to the highest performing groups” which can lead to a “deficit-based perspective” that focuses attention on what low-performing groups are lacking when compared with the high performers. Instead, Blakely proposes plotting each race on its own chart as a set of small multiples, which can better encourage readers to think about the specific needs and challenges facing each group.

質疑默認的可視化方法。 當呈現按種族或族裔分類的數據時，通常將其反映出來，以在同一圖表上繪制所有組。 然而，正如皮塔·布雷克利 ( Pieta Blakely)所寫的那樣，這樣做“鼓勵[讀者]將每個小組與表現最好的小組進行比較”，這可能導致形成“基于赤字的觀點”，從而將注意力集中在缺乏表現的小組缺少什么與高績效的人相比。 相反，布雷克利(Blakely)建議將每場比賽以一組小的倍數繪制在自己的圖表上，這可以更好地鼓勵讀者考慮每組面臨的特定需求和挑戰。

This was the approach we took with Urban’s Tracking COVID-19’s Effects by Race and Ethnicity data tool, which seeks to present the disparate impact of the COVID-19 pandemic by race and ethnicity for various metrics. Initially, line charts were proposed that displayed all of the races in the same chart.

這是我們采用Urban的“ 按種族和種族劃分COVID-19效果”數據工具所采用的方法，該工具旨在按種族和種族呈現各種指標的COVID-19大流行的不同影響。 最初，提出了折線圖，該折線圖在同一張圖表中顯示所有比賽。

But as our work on the project progressed, we applied a racial equity awareness lens to how we were visualizing the data and began to question our initial approach. For one, putting all of the races on the same chart focused attention on only the hardest-hit groups, rather than on how members from all races and ethnicities have been negatively affected by the pandemic. Another issue was that plotting all of the races on one graph made it seem like the least-affected group was the goal the other races and ethnicities should seek to achieve. But why should that be the case? Upon further questioning, we decided a better comparison would show the state- or metrowide average. This approach had a couple benefits. First, it offered a consistent standard all groups could be measured against. Secondly, it meant we did not promote any particular racial or ethnic group as being the default group against which everyone else must be compared.

但是隨著我們在該項目上的工作進展，我們將種族平等意識鏡頭應用于如何可視化數據并開始質疑我們的初始方法。 一方面，將所有種族放在同一圖表上，僅將注意力集中在受災最重的群體上，而不是關注各個種族和種族的成員如何受到這種流行病的負面影響。 另一個問題是，將所有種族繪制在一張圖表上似乎使受影響最小的群體似乎是其他種族和種族應尋求實現的目標。 但是為什么會這樣呢？ 經過進一步的詢問，我們決定更好地進行比較，以顯示州或都市范圍內的平均值。 這種方法有很多好處。 首先，它提供了可以衡量所有群體的一致標準。 其次，這意味著我們沒有提倡任何特定的種族或族裔群體作為必須與其他所有人進行比較的默認群體。

Our final design presents the data using a set of small multiples faceted by each racial and ethnic group with the relevant local average included as a benchmark. The estimates are shown as points surrounded by confidence interval blocks because of statistical and data collection concerns. The distance between the grey and green or grey and blue blocks reflects how much better or worse the group is doing compared with the average, and the change in that distance week to week indicates whether the disparity is worsening or improving over time. In weeks when the difference was not statistically significant, the estimates are shown using a lower opacity to reflect our uncertainty about the magnitude of the gap. We also made a conscious decision to sort the groups alphabetically rather than by data value. Because users can select different metrics in the final project, sorting alphabetically maintains the same order across all views rather than having the graphs shift around.

我們的最終設計使用每個種族和族裔群體面對的一組小的倍數來顯示數據，并以相關的本地平均值作為基準。 由于統計和數據收集方面的考慮，估計值顯示為由置信區間塊包圍的點。 灰色和綠色或灰色和藍色塊之間的距離反映了該組與平均水平相比好還是壞，并且該距離的逐周變化表明差距是否隨著時間的推移而加劇或改善。 在差異沒有統計學意義的幾周內，使用較低的不透明度顯示估算值，以反映出我們對差距幅度的不確定性。 我們還做出了明智的決定，即按字母順序而不是按數據值對組進行排序。 由于用戶可以在最終項目中選擇不同的指標，因此按字母順序排序可以在所有視圖中保持相同的順序，而不是使圖形四處移動。

Engaging or reflecting lived experience. Ideally, chart makers would not just implement our guidelines above but would also reach out to members of the communities being visualized and ask for their feedback and advice. Are we using language consistent with how they refer to themselves and others? What have we missed in our visuals that are inconsistent with a DEI framework? How can we take a more empathetic approach to creating data visualizations that accurately and respectfully account for other people’s lived experiences? It’s important to remember that data are a reflection of the lives of real people, not just a sterile abstraction.

參與或反映生活經驗。 理想情況下，圖表制作者不僅要執行上述準則，還應與可視化社區的成員聯系，并征求他們的反饋和建議。 我們使用的語言是否與他們對自己和他人的稱呼一致？ 我們錯過了與DEI框架不一致的視覺效果嗎？ 我們如何才能采用一種更具同理心的方法來創建數據可視化，以準確，尊重地考慮其他人的生活經歷？ 重要的是要記住，數據是真實人生活的反映，而不僅僅是無菌的抽象。

Looking forward

期待

As we continue to revise our data visualization style guide, we’ve noticed few, if any, style guides from other organizations mention accessibility, inclusion, or diversity. Our approach has been to create a set of recommendations and issues to consider rather than a set of rules that researchers must follow. Ultimately, we hope researchers will be thoughtful and deliberate about their design choices, not just relying on software defaults or the status quo. If you or your organization has gone about setting guidelines for DEI in your visualizations, we would love to hear about them.

隨著我們繼續修訂數據可視化樣式指南，我們注意到其他組織的樣式指南 (如果有的話)很少提及可訪問性，包含性或多樣性。 我們的方法是創建一組要考慮的建議和問題，而不是研究人員必須遵循的一組規則。 最終，我們希望研究人員能夠對設計選擇進行深思熟慮，而不是僅僅依靠軟件默認值或現狀。 如果您或您的組織打算在可視化中設置DEI準則，我們將很樂意為您提供指導。

-Jonathan Schwabish

喬納森·施瓦比什

-Alice Feng

馮愛麗

The authors wish to thank Shena Ashley, Kreg Steven Brown, Kilolo Kijakazi, Heather Kraus, and Alexandra Tammaro for their thoughtful comments and feedback.

作者要感謝Shena Ashley，Kreg Steven Brown，Kilolo Kijakazi，Heather Kraus和Alexandra Tammaro的周到評論和反饋。

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想了解更多？ 訂閱 Data @ Urban 新聞通訊。

Further reading:

進一步閱讀：

· “Presenting data for a Targeted Universalist approach”

· “為有針對性的普遍主義方法提供數據”

· Design Justice: towards an intersectional feminist framework for design theory and practice (PDF)

· 設計正義：走向設計理論與實踐的交叉女性主義框架 (PDF)

· “Designing data visualisations with empathy”

·“ 以同理心設計數據可視化 ”

· “Can Visualization Elicit Empathy? Our Experiments with ‘Anthropographics”

· “可視化可以激發移情作用嗎？ 我們的“人類學 ” 實驗

· “Connecting with the Dots”

· “連接點”