The golden ratio is originally a mathematical term. But art, architecture, and design are inconceivable without this math. Everyone aspires to golden proportions as beautiful and unattainable perfection. By visualizing data, we challenge ourselves to strike a balance between design and analysis; finding a similar harmony in the visual perception of a graph much like the harmony of the golden ratio in the world around us. How do we bring numbers and dry logical conclusions to life? How do we get them to tell a fascinating story without losing its meaning? Perhaps we can consider this the “new” Golden Ratio.

黄金比例最初是一个数学术语。 但是，没有这种数学，艺术，建筑和设计是不可想象的。 每个人都渴望获得美丽和无法达到的完美的黄金比例。 通过可视化数据，我们挑战自己，在设计和分析之间取得平衡。 在图形的视觉感知中找到相似的和谐，就像我们周围世界的黄金比例的和谐一样。 我们如何将数字和干燥的逻辑结论带入生活？ 我们如何让他们讲一个有趣的故事而又不失去其含义？ 也许我们可以认为这是“新的”黄金分割率。

In search for the answers to these questions, I investigated the best examples of modern data visualization by searching through the category “Greatest” in the Tableau Public gallery of works. The work “How Safe Are Ivy League Schools?” by authors Alex Dixon & Tarannum Ansari caught my eye. The authors asked the non-standard question: How safe is it to study at Ivy League universities?

在寻找这些问题的答案时，我通过在Tableau公共作品库中搜索“最伟大”类别来研究现代数据可视化的最佳示例。 常春藤联盟学校有多安全？ ”作者Alex Dixon和Tarannum Ansari引起了我的注意。 作者提出了一个非标准的问题： 在常春藤盟校学习安全吗？

For those unfamiliar, the Ivy League is an association of the eight oldest American universities — often used as a shorthand for prestigious higher education in the United States. To answer their question, the authors used open data on the website of the US Department of Education.

对于那些陌生的人来说，常春藤联盟是美国八所最古老大学的协会，通常被用作美国享有声望的高等教育的简写。 为了回答他们的问题，作者使用了美国教育部网站上的开放数据。

Despite the age of the visualization (it was released in 2017 and features data from 2001–2014), I decided to choose this piece for my investigation of the balance between analysis and visual design. I believe that paying attention to nuances that can confuse users is the best way to learn how to avoid misleading context in my own work, as well as to discuss differences in perception with other specialists.

尽管可视化的年代很久(它于2017年发布，并具有2001-2014年的数据)，但我还是决定选择此作品来调查分析与视觉设计之间的平衡。 我认为，关注可能使用户感到困惑的细微差别是学习如何避免在我自己的作品中产生误导性上下文以及与其他专家讨论看法差异的最佳方法。

报告格式 (Report Layout)

At first glance, it’s pleasing that the visualization is made with the ideology of a dashboard — on one screen without scrolling. Three vertical logical blocks are highlighted:

乍看之下，令人愉悦的是，可视化是通过仪表板的思想进行的–在一个屏幕上无需滚动。 突出显示了三个垂直逻辑块：

• for administrative violations,
  对于行政违规，
• for administrative penalties,
  对于行政处罚，
• for criminal offenses.
  刑事犯罪。

Vertical blocks are united by a horizontal strip at the top: eight logos and the short names of the universities. The logos make it possible to reference the universities throughout the dashboard without having to rely on the written name. The logos also work as filters: by clicking, you can select the desired university and view only its information.

垂直块由顶部的水平条组合在一起：八个徽标和大学的简称。 徽标使您可以在整个仪表板中引用大学，而不必依赖书面名称。 徽标也可以用作过滤器：通过单击，您可以选择所需的大学并仅查看其信息。

Under the logos there is a time slicer by years — you can select the period of interest and the data will update accordingly. Traditional and understandable visual solutions are used for visualization:

徽标下有一个按年划分的时间切片器-您可以选择感兴趣的时间段，数据将相应地更新。 传统且易于理解的视觉解决方案用于可视化：

• bar chart,
  条形图
• filter with a choice from the drop-down list,
  从下拉列表中选择一个选项进行过滤，
• heat chart,
  热图，
• area diagram.
  区域图。

字体和配色方案 (Fonts and Colors Scheme)

The authors use fonts very skillfully: two catchy bold fonts are chosen for the headers and a concise, well-read sans-serif font for the description and numbers.

作者非常熟练地使用字体：标题选择了两种醒目的粗体字体，描述和数字选择了简洁易读的sans-serif字体。

The color scheme is a harmonious combination of milky cream and green (perhaps as a reference to the color of ivy leaves). While from a design standpoint this color palette is quite pleasing, it raises a few questions for our inner Analyst. For many people green is intuitively perceived as positive — the greener the better. Accordingly, a saturated green color should indicate a more positive situation (low level of danger). As a result, some users may potentially be confused due to the choice of this palette.

配色方案是乳白色和绿色(可能是常春藤叶颜色的参考)的和谐组合。 从设计的角度来看，此调色板非常令人满意，但它给我们的内部分析师带来了一些问题。 对于许多人来说，绿色在直观上被认为是积极的-绿色越好。 因此，饱和的绿色应表示更积极的情况(危险程度低)。 结果，由于选择此调色板，某些用户可能会感到困惑。

In addition, the heat map visualizations have a somewhat confusing color logic, which leads to color and data conflicts. For example, the data for Cornell University is distributed as follows:

此外，热图可视化还具有一些令人困惑的颜色逻辑，从而导致颜色和数据冲突。 例如，康奈尔大学的数据分布如下：

• 100–200 offenses — standard green,
  100–200次进攻-标准果岭，
• 200–500 offenses — light green color,
  200–500次进攻-浅绿色，
• over 500 offenses — dark green color.
  超过500次进攻-深绿色。

What could make the Analyst feel a little more comfortable? Perhaps adding an alternative red spectrum color to the report to create a transition between negative and positive. And this color exists. The Ivy League has several signature colors and one of it — Harvard crimson — just the “crimson” shade we need.

是什么让分析师感到更舒服？ 也许在报告中添加其他红色光谱颜色以在负值和正值之间创建过渡。 并且这种颜色存在。 常春藤盟军有几种标志性的颜色，其中之一是哈佛深红色，这正是我们需要的“深红色”色调。

Please note: This solution is not the best for all people due to color vision deficiency (CVD). It is just one of the suggestions to make the visualization more clear, though one should always check to ensure a viz is using colorblind-friendly palette to avoid misunderstandings due to color perception (for example, Color Blindness Simulator)

请注意：由于色觉不足(CVD)，此解决方案并非对所有人都适用。 这只是使可视化效果更清晰的建议之一，尽管应该始终检查以确保可视对象正在使用对色盲友好的调色板，以避免由于色觉而引起的误解(例如，“ 色盲模拟器” )。

可视化：分析师感到困惑 (Visualization: The Analyst is Confused)

As already mentioned, simple and intuitive graphs have been chosen for the visualizations. The authors brilliantly managed to reflect a large amount of data without excessive fragmentation and visual weight. Pop-up tips played a significant role — there are lots of them and they are quite informative.

如前所述，为可视化选择了简单直观的图形。 作者出色地设法反映了大量数据，而没有过多的碎片和视觉负担。 弹出提示起着重要作用-其中很多提示都非常有用。

However, there is an ambiguous logic in the choice of the measurement which crosses out all the ease of the visual perception for the Analyst. The authors chose absolute values as the unit of measurement: the total number of violations or offenses. It seems to me that it’s not entirely informative to use absolute values in such studies.

但是，在选择度量时存在一个含糊不清的逻辑，这种逻辑与分析人员的所有视觉感觉都难以相提并论。 作者选择绝对值作为度量单位：违法或违法的总数。 在我看来，在此类研究中使用绝对值并不完全有益。

For example, 691 law violations were committed at Cornell University in 2014 — is this a lot or a little? It seems to be a lot. That same year there were only 386 violations at Dartmouth College. It looks like things are really going bad at Cornell University after all, doesn’t it? Moreover, the current logic of the heat map tells us this too— Cornell University is shown with darker green than Dartmouth College.

例如，2014年在康奈尔大学犯下了691起违反法律的行为-是多少？ 似乎很多。 同年，达特茅斯学院只有386起违规事件。 毕竟，康奈尔大学看起来真的真的很糟糕，不是吗？ 此外，热图的当前逻辑也告诉我们这一点–康奈尔大学的绿色深于达特茅斯学院。

But this conclusion may be wrong. To fully understand the situation, I would recommend taking into account the total number of students. And the authors have this data — when you hover over the histograms (left visual block) informative tips with this data for each university appear.

但是这个结论可能是错误的。 为了充分了解这种情况，我建议考虑学生总数。 作者拥有这些数据-当您将鼠标悬停在直方图(左图块)上时，就会出现每所大学的数据提示性提示。

So, 691 law violations occurred at Cornell University, which has 21,679 students, and 386 law violations occurred at Dartmouth College with 6,298 students. That’s 31.9 law violations per 1,000 students at Cornell University and 61.3 law violations per 1,000 students at Dartmouth College — with a convincing advantage… Cornell University wins!

因此，康奈尔大学发生了691起违反法律的行为，有21,679名学生，达特茅斯学院发生了386起违反法律的行为，有6,298名学生。 这就是康奈尔大学每1,000名学生违反法律的31.9例，达特茅斯大学每1000名学生违反法律的11.3项–具有令人信服的优势……康奈尔大学获胜！

The authors do attempt to compensate for this difference in volume in the bar graphs on the left, which use thickness to designate the number of students. However, this makes the visual less readily understandable at a glance and adds more cognitive load to the reader.

作者的确尝试补偿左侧条形图中的音量差异，该条形图使用厚度来指定学生人数。 然而，这使视觉一眼就不易理解，并给读者增加了更多的认知负担。

Our internal Explorers also lacked clear color legends — at least one for each block. Finally, the Analytist tore up something else about the horizontal scale for heat maps — because it was only from the prompts that he could understand that there is aggregate year-by-year data for each of the universities.

我们的内部资源管理器也缺少清晰的颜色说明-每个块至少一个。 最后，分析家对热图的水平比例进行了其他修改，因为只有从提示中他才能了解每所大学的年度汇总数据。

您批评-您提供！ (You Criticize — You Offer!)

This report has deservedly entered the category “Greatest” in Tableau — the authors set a fairly complex and voluminous task and have effectively realized it. If it wasn’t for the mentioned remarks on the measurement logic and color choice, the report would be almost flawless from the user’s point of view.

该报告当之无​​愧地进入了Tableau的“最伟大”类别-作者设定了一个相当复杂和繁琐的任务，并有效地实现了这一目标。 如果不是针对测量逻辑和颜色选择的上述说明，那么从用户的角度来看，该报告几乎是完美无缺的。

At the same time, we don’t necessarily get the answer to the basic question: how safe is it to study in the Ivy League? Trying to answer, our Analyst suggested the report be supplemented with a consolidated safety indicator for the entire Ivy League, also normalized to provide the data in the form of a percentage of events per 1,000 students. Our Designer enthusiastically released all that. The result of their collaborative search for the new golden ratio is this visualization:

同时，我们不一定能回答基本问题： 在常春藤联盟学习安全吗？ 为了回答这个问题，我们的分析师建议为报告添加一个针对整个常春藤联盟的综合安全指标，并进行标准化，以每1000名学生事件百分比的形式提供数据。 我们的设计师热情地发布了所有这些内容。 他们共同寻找新的黄金比例的结果是这种可视化效果：

So what do we see? The leader among all types of law violations is liquor law violations. Among the criminal offenses, burglary and robbery have a sad leadership .

那我们看到了什么？ 在所有类型的违法行为中，领导者是酒业违法行为。 在刑事犯罪中，入室盗窃和抢劫行为可悲。

The already mentioned Dartmouth College and its affiliated Princeton University and Brown University are the most “red” participants in our table.

已经提到的达特茅斯学院及其附属的普林斯顿大学和布朗大学是我们表中最“红”的参与者。

In contrast, Columbia University in the City of New York, Cornell University, Harvard University, and the University of Pennsylvania are mostly in the safe “green” zone.

相反，纽约市的哥伦比亚大学，康奈尔大学，哈佛大学和宾夕法尼亚大学则大多位于安全的“绿色”区域。

Harvard University, however, proved to be an unexpected leader in burglary and robbery. But if you look closely at Criminal Offenses’ heat map 2001–2014, it is clear that these problems have been experienced by Harvard University in the past — since 2009, the university has moved to the “green” team and has never left it again.

然而，事实证明，哈佛大学是抢劫和抢劫领域的出人意料的领导者。 但是，如果您仔细查看2001-2014年《刑事犯罪》的热图，很显然，哈佛大学过去曾遇到过这些问题-自2009年以来，该大学已转入“绿色”团队，并且再也没有离开过。

结论 (Conclusions)

Modern data is called Big Data for a reason — the more data, the more difficult it is to visualize. Volumetric data requires detail, and this affects the visual component of the analysis. The original report successfully avoided visualization problems by proposing a constructive graphical solution for a massive data block.

现代数据之所以被称为大数据，是因为一个原因-数据越多，可视化就越困难。 体积数据需要详细信息，这会影响分析的视觉组成。 原始报告通过为海量数据块提出了建设性的图形解决方案，成功避免了可视化问题。

But the completeness of the data presented has replaced the analysis itself.

但是，所提供数据的完整性已取代了分析本身。

Not claiming the truth and armed with Occam’s razor (yes, analysts have a lot of surprising techniques in the arsenal), I grouped the data on crimes into the category “criminal offenses” and got rid of the details of the sanctions in “law violations”.

我没有声称真相，而是用奥卡姆的剃刀武装(是的，分析家在武器库中有很多令人惊讶的技术)，我将犯罪数据归为“犯罪罪”一类，并摆脱了“违反法律的行为”中制裁的细节。 ”。

This allowed me to calculate aggregated security indicators, compare Ivy League universities, and find some answers. Maybe it wasn’t as elegant as the original version — that’s something for my inner Designer to think about.

这使我能够计算汇总的安全指标，比较常春藤盟校，并找到一些答案。 也许它没有原始版本那么优雅-这是我的内部设计师要考虑的事情。

My final word of advice — when working on data visualization switch internally from Analyst to Designer and vice versa. (Don’t forget to involve the User however, since he will be the one who will evaluate the final result.) By switching between these two modes of thinking, we work towards that perfect balance between design and analysis — our “new” Golden Ratio.

我最后的建议是-在进行数据可视化时，在内部从Analyst切换到Designer，反之亦然。 (但是，不要忘了让用户参与，因为他将是评估最终结果的人。)通过在两种思维方式之间切换，我们努力在设计和分析之间实现完美的平衡-我们的“新” Golden比。