Visual design is hard, brought to you by NYC subway

This poster showed up in a NY subway train recently.

Visual design is hard!

What is the message? The intention is, of course, to say Rootine is better than others. (That's the Q corner, if you're following the Trifecta Checkup.)

What is the visual telling us (V corner)? It says Rootine is yellow while Others are purple. What do these color mean? There is no legend to help decipher it. And yellow-purple doesn't have a canonical interpretation (unlike say, red-green). In theory, purple can be better than yellow.

The other mystery is the black dot on the fifth item. (This is the NYC subway so the poster could have been vandalized.) It could mean "diet + lifestyle analyzed" is a unique feature of Rootine, not available on any other platform. That implies purple to mean available but not as effective, which significantly lessnes the impact of the chart.

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Finally, let's imagine the data that may exist to support this chart.

The aggregation of all competitors to "Others" imposes a major challenge. If yellow means yes, and purple means no, we'd expect few if any purple dots because across all competitors, there is a good chance that at least one of them has a particular feature.

Next, I'm dubious about the claim of "precision dosed, unique to you". I'm imagining they are selling some kind of medicine or health food, which can be "dosed". Predictive modelers like to market their models as "personalized," unique to each person but such a thing is impractical. Before you start using their products, they have no data on you, or your response to those products. How could the recommendation be "precision dosed, unique to you"?

Even if you've used the product for a while, it will be tough to achieve a good level of optimality with so little data. In fact, given that your past data are used to generate actions intended to improve your health - that is to say, to cause the future data to diverge from the past data, how do you know that any change you observe next period is caused by the actions you took? The pre-post difference is both affected by temporal shifts and the actions you've taken. If the next period's metric improves, you may want to believe that the actions worked. If the next period's metric declines, are you willing to conclude that the actions you took backfired?

"Formulas improve with you". This makes me more worried than relieved.

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Problems like these can be solved by showing our work to others. Sometimes, we're too immersed in our own world we don't see we have left off key information.

 

 

Getting to first before going to second

Happy holidays to all my readers! A special shutout to those who've been around for over 15 years.

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The following enhanced data table appeared in Significance magazine (August 2021) under an article titled "Winning an election, not a popularity contest" (link, paywalled)

It's surprising hard to read and there are many reasons contributing to this.

First is the antiquated style guide of academic journals, in which they turn legends into text, and insert the text into a caption. This is one of the worst journalistic practices that continue to be followed.

The table shows 50 states plus District of Columbia. The authors are interested in the extreme case in which a hypothetical U.S. presidential candidate wins the electoral college with the lowest possible popular vote margin. If you've been following U.S. presidential politics, you'd know that the electoral college effectively deflates the value of big-city votes so that the electoral vote margin can be a lot larger than the popular vote margin.

The two sub-tables show two different scenarios: Scenario A is a configuration computed by NPR in one of their reports. Scenario B is a configuration created by the authors (Leinwand, et. al.).

The table cells are given one of four colors: green = needed in the winning configuration; white = not needed; yellow = state needed in Scenario B but not in Scenario A; grey = state needed in Scenario A but not in Scenario B.

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The second problem is that the above description of the color legend is not quite correct. Green, it turns out, is only correctly explained for Scenario A. Green for Scenario B encodes those states that are needed for the candidate to win the electoral college in Scenario B minus those states that are needed in Scenario B but not in Scenario A (shown in yellow). There is a similar problem with interpreting the white color in the table for Scenario B.

To fix this problem, start with the Q corner of the Trifecta Checkup.

The designer wants to convey an interlocking pair of insights: the winning configuration of states for each of the two scenarios; and the difference between those two configurations.

The problem with the current design is that it elevates the second insight over the first. However, the second insight is a derivative of the first so it's hard to get to the second spot without reaching the first.

The following revision addresses this problem:

[12/30/2021: Replaced chart and corrected the blue arrow for NJ.]

 

 

Designs of two variables: map, dot plot, line chart, table

The New York Times found evidence that the richest segments of New Yorkers, presumably those with second or multiple homes, have exited the Big Apple during the early months of the pandemic. The article (link) is amply assisted by a variety of data graphics.

The first few charts represent different attempts to express the headline message. Their appearance in the same article allows us to assess the relative merits of different chart forms.

First up is the always-popular map.

The advantage of a map is its ease of comprehension. We can immediately see which neighborhoods experienced the greater exoduses. Clearly, Manhattan has cleared out a lot more than outer boroughs.

The limitation of the map is also in view. With the color gradient dedicated to the proportions of residents gone on May 1st, there isn't room to express which neighborhoods are richer. We have to rely on outside knowledge to make the correlation ourselves.

The second attempt is a dot plot.

We may have to take a moment to digest the horizontal axis. It's not time moving left to right but income percentiles. The poorest neighborhoods are to the left and the richest to the right. I'm assuming that these percentiles describe the distribution of median incomes in neighborhoods. Typically, when we see income percentiles, they are based on households, regardless of neighborhoods. (The former are equal-sized segments, unlike the latter.)

This data graphic has the reverse features of the map. It does a great job correlating the drop in proportion of residents at home with the income distribution but it does not convey any spatial information. The message is clear: The residents in the top 10% of New York neighborhoods are much more likely to have left town.

In the following chart, I attempted a different labeling of both axes. It cuts out the need for readers to reverse being home to not being home, and 90th percentile to top 10%.

The third attempt to convey the income--exit relationship is the most successful in my mind. This is a line chart, with time on the horizontal axis.

The addition of lines relegates the dots to the background. The lines show the trend more clearly. If directly translated from the dot plot, this line chart should have 100 lines, one for each percentile. However, the closeness of the top two lines suggests that no meaningful difference in behavior exists between the 20th and 80th percentiles. This can be conveyed to readers through a short note. Instead of displaying all 100 percentiles, the line chart selectively includes only the 99th , 95th, 90th, 80th and 20th percentiles. This is a design choice that adds by subtraction.

Along the time axis, the line chart provides more granularity than either the map or the dot plot. The exit occurred roughly over the last two weeks of March and the first week of April. The start coincided with New York's stay-at-home advisory.

This third chart is a statistical graphic. It does not bring out the raw data but features aggregated and smoothed data designed to reveal a key message.

I encourage you to also study the annotated table later in the article. It shows the power of a well-designed table.

[P.S. 6/4/2020. On the book blog, I have just published a post about the underlying surveillance data for this type of analysis.]

 

 

The time of bird seeds and chart tuneups

The recent post about multi-national companies reminded me of an older post, in which I stepped through data table enhancements.

Here is a video of the process. You can use any tool to implement the steps; even Excel is good enough.

 

 

The video is part of a series called "Data science: the Missing Pieces". In these episodes, I cover the parts of data science that are between the cracks, the little things that textbooks and courses do not typically cover - the things that often block students from learning efficiently.

If you have encountered such things, please comment below to suggest future topics. What is something about visualizing data you wish you learned formally?

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P.S. Placed here to please the twitter-bot

 

 

Too much of a good thing

Several of us discussed this data visualization over twitter last week. The dataviz by Aero Data Lab is called “A Bird’s Eye View of Pharmaceutical Research and Development”. There is a separate discussion on STAT News.

Here is the top section of the chart:

We faced a number of hurdles in understanding this chart as there is so much going on. The size of the shapes is perhaps the first thing readers notice, followed by where the shapes are located along the horizontal (time) axis. After that, readers may see the color of the shapes, and finally, the different shapes (circles, triangles,...).

It would help to have a legend explaining the sizes, shapes and colors. These were explained within the text. The size encodes the number of test subjects in the clinical trials. The color encodes pharmaceutical companies, of which the graphic focuses on 10 major ones. Circles represent completed trials, crosses inside circles represent terminated trials, triangles represent trials that are still active and recruiting, and squares for other statuses.

The vertical axis presents another challenge. It shows the disease conditions being investigated. As a lay-person, I cannot comprehend the logic of the order. With over 800 conditions, it became impossible to find a particular condition. The search function on my browser skipped over the entire graphic. I believe the order is based on some established taxonomy.

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In creating the alternative shown below, I stayed close to the original intent of the dataviz, retaining all the dimensions of the dataset. Instead of the fancy dot plot, I used an enhanced data table. The encoding methods reflect what I’d like my readers to notice first. The color shading reflects the size of each clinical trial. The pharmaceutical companies are represented by their first initials. The status of the trial is shown by a dot, a cross or a square.

Here is a sketch of this concept showing just the top 10 rows.

Certain conditions attracted much more investment. Certain pharmas are placing bets on cures for certain conditions. For example, Novartis is heavily into research on Meningnitis, meningococcal while GSK has spent quite a bit on researching "bacterial infections."

The Periodic Table, a challenge in information organization

Reader Chris P. points me to this article about the design of the Periodic Table. I then learned that 2019 is the “International Year of the Periodic Table,” according to the United Nations.

Here is the canonical design of the Periodic Table that science students are familiar with.

(Source: Wikipedia.)

The Periodic Table is an exercise of information organization and display. It's about adding structure to over 100 elements, so as to enhance comprehension and lookup. The canonical tabular design has columns and rows. The columns (Groups) impose a primary classification; the rows (Periods) provide a secondary classification. The elements also follow an aggregate order, which is traced by reading from top left to bottom right. The row structure makes clear the "periodicity" of the elements: the "period" of recurrence is not constant, tending to increase with the heavier elements at the bottom.

As with most complex datasets, these elements defy simple organization, due to a curse of dimensionality. The general goal is to put the similar elements closer together. Similarity can be defined in an infinite number of ways, such as chemical, physical or statistical properties. The canonical design, usually attributed to Russian chemist Mendeleev, attained its status because the community accepted his organizing principles, that is, his definitions of similarity (subsequently modified).

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Of interest, there is a list of unsettled issues. According to Wikipedia, the most common arguments concern:

• Hydrogen: typically shown as a member of Group 1 (first column), some argue that it doesn’t belong there since it is a gas not a metal. It is sometimes placed in Group 17 (halogens), where it forms a nice “triad” with fluorine and chlorine. Other designers just float hydrogen up top.
• Helium: typically shown as a member of Group 18 (rightmost column), the  halogens noble gases, it may also be placed in Group 2.
• Mercury: usually found in Group 12, some argue that it is not a metal like cadmium and zinc.
• Group 3: other than the first two elements , there are various voices about how to place the other elements in Group 3. In particular, the pairs of lanthanum / actinium and lutetium / lawrencium are sometimes shown in the main table, sometimes shown in the ‘f-orbital’ sub-table usually placed below the main table.

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Over the years, there have been numerous attempts to re-design the Periodic table. Some of these are featured in the article that Chris sent me (link).

I checked how these alternative designs deal with those unsettled issues. The short answer is they don't settle the issues.

Wide Table (Janet)

The key change is to remove the separation between the main table and the f-orbital (pink) section shown below, as a "footnote". This change clarifies the periodicity of the elements, especially the elongating periods as one moves down the table. This form is also called "long step".

As a tradeoff, this table requires more space and has an awkward aspect ratio.

In this version of the wide table, the designer chooses to stack lutetium / lawrencium in Group 3 as part of the main table. Other versions place lanthanum / actinium in Group 3 as part of the main table. There are even versions that leave Group 3 with two elements.

Hydrogen, helium and mercury retain their conventional positions.

 

Spiral Design (Hyde)

There are many attempts at spiral designs. Here is one I found on this tumblr:

The spiral leverages the correspondence between periodic and circular. It is visually more pleasing than a tabular arrangement. But there is a tradeoff. Because of the increasing "diameter" from inner to outer rings, the inner elements are visually constrained compared to the outer ones.

In these spiral diagrams, the designer solves the aspect-ratio problem by creating local loops, sometimes called peninsulas. This is analogous to the footnote table solution, and visually distorts the longer periodicity of the heavier elements.

For Hyde's diagram, hydrogen is floated, helium is assigned to Group 2, and mercury stays in Group 12.

 

Racetrack

I also found this design on the same tumblr, but unattributed. It may have come from Life magazine.

It's a variant of the spiral. Instead of peninsulas, the designer squeezes the f-orbital section under Group 3, so this is analogous to the wide table solution.

The circular diagrams convey the sense of periodic return but the wide table displays the magnitudes more clearly.

This designer places hydrogen in group 18 forming a triad with fluorine and chlorine. Helium is in Group 17 and mercury in the usual Group 12 .

 

Cartogram (Sheehan)

This version is different.

The designer chooses a statistical property (abundance) as the primary organizing principle. The key insight is that the lighter elements in the top few rows are generally more abundant - thus more important in a sense. The cartogram reveals a key weakness of the spiral diagrams that draw the reader's attention to the outer (heavier) elements.

Because of the distorted shapes, the cartogram form obscures much of the other data. In terms of the unsettled issues, hydrogen and helium are placed in Groups 1 and 2. Mercury is in Group 12. Group 3 is squeezed inside the main table rather than shown below.

 

Network

The centerpiece of the article Chris sent me is a network graph.

This is a complete redesign, de-emphasizing the periodicity. It's a result of radically changing the definition of similarity between elements. One barrier when introducing entirely new displays is the tendency of readers to expect the familiar.

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I found the following articles useful when researching this post:

The Conversation

Royal Chemistry Society

 

A data graphic that solves a consumer problem

Saw this great little sign at Ippudo, the ramen shop, the other day:

It's a great example of highly effective data visualization. The names on the board are sake brands. 

The menu (a version of a data table) is the conventional way of displaying this information.

The Question

Customers are selecting a sake. They don't have a favorite, or don't recognize many of these brands. They know a bit about their preferences: I like full-bodied, or I want the dry one. 

The Data

On a menu, the key data are missing. So the first order of business is to find data on full- and light-bodied, and dry and sweet. The pricing data are omitted, possibly because it clutters up the design, or because the shop doesn't want customers to focus on price - or both.

The Visual

The design uses a scatter plot. The customer finds the right quartet, thus narrowing the choices to three or four brands. Then, the positions on the two axes allow the customer to drill down further. 

This user experience is leaps and bounds above scanning a list of names, and asking someone who may or may not be an expert.

Back to the Data

The success of the design depends crucially on selecting the right data. Baked into the scatter plot is the assumption that the designer knows the two factors most influential to the customer's decision. Technically, this is a "variable selection" problem: of all factors determining the brand choice, which two are the most important? 

Think about the downside of selecting the wrong factors. Then, the scatter plot makes it harder to choose the sake compared to the menu. 

 

Beauty is in the eyes of the fishes

Reader Patrick S. sent in this old gem from Germany.

He said:

It displays the change in numbers of visitors to public pools in the German city of Hanover. The invisible y-axis seems to be, um, nonlinear, but at least it's monotonic, in contrast to the invisible x-axis.

There's a nice touch, though: The eyes of the fish are pie charts. Black: outdoor pools, white: indoor pools (as explained in the bottom left corner).

It's taken from a 1960 publication of the city of Hanover called *Hannover: Die Stadt in der wir leben*.

This is the kind of chart that Ed Tufte made (in)famous. The visual elements do not serve the data at all, except for the eyeballs. The design becomes a mere vessel for the data table. The reader who wants to know the growth rate of swimmers has to do a tank of work.

The eyeballs though.

I like the fact that these pie charts do not come with data labels. This part of the chart passes the self-sufficiency test. In fact, the eyeballs contain the most interesting story in this chart. In those four years, the visitors to public pools switched from mostly indoor pools to mostly outdoor pools. These eyeballs show that pie charts can be effective in specific situations.

Now, Hanover fishes are quite lucky to have free admission to the public pools!

Playfulness in data visualization

The Newslab project takes aggregate data from Google's various services and finds imaginative ways to enliven the data. The Beautiful in English project makes a strong case for adding playfulness to your data visualization.

The data came from Google Translate. The authors look at 10 languages, and the top 10 words users ask to translate from those languages into English.

The first chart focuses on the most popular word for each language. The crawling snake presents the "worldwide" top words.

The crawling motion and the curvature are not required by the data but it inserts a dimension of playfulness into the data that engages the reader's attention.

The alternative of presenting a data table loses this virtue without gaining much in return.

Readers are asked to click on the top word in each country to reveal further statistics on the word.

For example, the word "good" leads to the following:

 

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The second chart presents the top 10 words by language in a lollipop style:

The above diagram shows the top 10 Japanese words translated into English. This design sacrifices concise in order to achieve playful.

The standard format is a data table with one column for each country, and 10 words listed below each country header in order of decreasing frequency.

The creative lollipop display generates more extreme emotions - positive, or negative, depending on the reader. The data table is the safer choice, precisely because it does not engage the reader as deeply.

 

 

The art of arranging bars

Twitter friend Janie H. asked how I would visualize a hypothetical third column of this chart that contains the change from 2016 to 2017:

This table records the results from a survey question by eMarketer, asking respondents ("marketers") to identify their top 5 technology priorities in the next 12 months.

I suggested the following:

A hype-chasing phenomemon is clearly at play. Internet of Things and wearable technology are so last year. This year, it's all about A.I. Interestingly, something like "Big data" has been able to sustain the hype for another year.

A design decision I made is to encode the magnitude of the change in the bar lengths while encoding the direction of the change in the colors. One can of course follow the more canonical design of placing the negative bars on the left side of the data labels. My decision is a subtle way of imposing the hierarchy - first I care about magnitude, then I care about direction.

Here is a third way:

This design imposes a different hierarchy. Your eyes are drawn to the top/bottom of the chart.

Any of these designs beat the data table by a mile. It's just too much work for the reader to figure out the value of the changes from the table.