Longest life, shortest length

Racetrack charts refuse to die. For old time's sake, here is a blog post from 2005 in which I explain why they don't make good dataviz.

Our latest example comes from Visual Capitalist (link), which publishes a fair share of nice dataviz. In this infographics, they feature a racetrack chart, just because the topic is the lifespan of cars.

Visualcapitalist_lifespan_cars_top

The whole infographic has four parts, each a racetrack chart. I'll focus on the first racetrack chart (shown above), which deals with the product category of sedans and hatchbacks.

The first thing I noticed is the reference value of 100,000 miles, which is described as the expected lifespan of a typical car made in the 1970s. This is of dubious value since the top of the page informs us the current relevant reference value is 200,000 miles, which is unlabeled. We surmise that 200,000 miles is indicated by the end of the grey sections of the racetrack. (This is eventually confirmed in the next racettrack chart for SUVs in the second sectiotn of the infographic.)

Now let's zoom in on the brown section of the track. Each of the four sections illustrates the same datum = 100,000 miles and yet they exhibit different lengths. From this, we learn that the data are not encoded in the lengths of these tracks -- but rather the data are to be found in the angle sustained at the centre of the concentric circles. The problem with racetrack charts is that readers are drawn to the lengths of the tracks rather than the angles at the center, which are not explicitly represented.

The Avalon model has the longest life span on this chart, and yet it is shown as the shortest curve.

***

The most baffling part of this chart is not the visual but the analysis methodology.

I quote:

iSeeCars analyzed over 2M used cars on the road between Jan. and Oct. 2022. Rankings are based on the mileage that the top 1% of cars within each model obtained.

According to this blurb, the 245,710 miles number for Avalon is the average mileage found in the top 1% of Avalons within the iSeeCars sample of 2M used cars.

The word "lifespan" strikes me as incorporating a date of death, and yet nothing in the above text indicates that any of the sampled cars are at end of life. The cars they really need are not found in their sample at all.

I suppose taking the top 1% is meant to exclude younger cars but why 1%? Also, this sample completely misses the cars that prematurely died, e.g. the cars that failed after 100,000 miles but before 200,000 miles. This filtering also ensures that newer models are excluded from the sample.

_trifectacheckup_imageIn the Trifecta Checkup, this qualifies as Type DV. The dataset does not answer the question of concern while the visual form distorts the data.


Following this pretty flow chart

Bloomberg did a very nice feature on how drought has been causing havoc with river transportation of grains and other commodities in the U.S., which included several well-executed graphics.

Mississippi_sankeyI'm particularly attracted to this flow chart/sankey diagram that shows the flows of grains from various U.S. ports to foreign countries.

It looks really great.

Here are some things one can learn from this chart:

  • The Mississippi River (blue flow) is by far the most important conduit of American grain exports
  • China is by far the largest importer of American grains
  • Mexico is the second largest importer of American grains, and it has a special relationship with the "interior" ports (yellow). Notice how the Interior almost exclusively sends grains to Mexico
  • Similarly, the Puget Sound almost exclusively trades with China

The above list is impressive for one chart.

***

Some key questions are not as easy to see from this layout:

  • What proportion of the total exports does the Mississippi River account for? (Turns out to be almost exactly half.)
  • What proportion of the total exports go to China? (About 40%. This question is even harder than the previous one because of all the unlabeled values for the smaller countries.)
  • What is the relative importance of different ports to Japan/Philippines/Indonesia/etc.? (Notice how the green lines merge from the other side of the country names.)
  • What is the relative importance of any of the countries listed, outside the top 5 or so?
  • What is the ranking of importance of export nations to each port? For Mississippi River, it appears that the countries may have been drawn from least important (up top) to most important (down below). That is not the case for the other ports... otherwise the threads would tie up into knots.

***

Some of the features that make the chart look pretty are not data-driven.

See this artificial "hole" in the brown branch.

Bloomberg_mississippigrains_branchgap

In this part of the flow, there are two tiny outflows to Myanmar and Yemen, so most of the goods that got diverted to the right side ended up merging back to the main branch. However, the creation of this hole allows a layering effect which enhances the visual cleanliness.

Next, pay attention to the yellow sub-branches:

Bloomberg_mississippigrains_subbranching

At the scale used by the designer, all of the countries shown essentially import about the same amount from the Interior (yellow). Notice the special treatment of Singapore and Phillippines. Instead of each having a yellow sub-branch coming off the "main" flow, these two countries share the sub-branch, which later splits.

 

 

 


A German obstacle course

Tagesschau_originalA twitter user sent me this chart from Germany.

It came with a translation:

"Explanation: The chart says how many car drivers plan to purchase a new state-sponsored ticket for public transport. And of those who do, how many plan to use their car less often."

Because visual language should be universal, we shouldn't be deterred by not knowing German.

The structure of the data can be readily understood: we expect three values that add up to 100% from the pie chart. The largest category accounts for 58% of the data, followed by the blue category (40%). The last and smallest category therefore has 2% of the data.

The blue category is of the most interest, and the designer breaks that up into four sub-groups, three of which are roughly similarly popular.

The puzzle is the identities of these categories.

The sub-categories are directly labeled so these are easy for German speakers. From a handy online translator, these labels mean "definitely", "probably", "rather not", "definitely not". Well, that's not too helpful when we don't know what the survey question is.

According to our correspondent, the question should be "of those who plan to buy the new ticket, how many plan to use their car less often?"

I suppose the question is found above the column chart under the car icon. The translator dutifully outputs "Thus rarer (i.e. less) car use". There is no visual cue to let readers know we are supposed to read the right hand side as a single column. In fact, for this reader, I was reading horizontally from top to bottom.

Now, the two icons on the left and the middle of the top row should map to not buying and buying the ticket. The check mark and cross convey that message. But... what do these icons map to on the chart below? We get no clue.

In fact, the will-buy ticket group is the 40% blue category while the will-not group is the 58% light gray category.

What about the dark gray thin sector? Well, one needs to read the fine print. The footnote says "I don't know/ no response".

Since this group is small and uninformative, it's fine to push it into the footnote. However, the choice of a dark color, and placing it at the 12-o'clock angle of the pie chart run counter to de-emphasizing this category!

Another twitter user visually depicts the journey we take to understand this chart:

Tagesschau_reply

The structure of the data is revealed better with something like this:

Redo_tagesschau_newticket

The chart doesn't need this many colors but why not? It's summer.

 

 

 

 


Visual design is hard, brought to you by NYC subway

This poster showed up in a NY subway train recently.

Rootin-sm

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.

***

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.

***

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.

 

 


One of the most frequently produced maps is also one of the worst

Summer is here, many Americans are putting the pandemic in their rear-view mirrors, and gas prices are soaring. Business Insider told the story using this map:

Businessinsider_gasprices_1

What do we want to learn about gas prices this summer?

Which region has the highest / lowest prices?

How much higher / lower than the national average are the regional prices?

How much has prices risen, compared to last year, or compared to the last few weeks?

***

How much work did you have to do to get answers to those questions from the above map?

Unfortunately, this type of map continues to dominate the popular press. It merely delivers a geography lesson and not much else. Its dominant feature tells readers how to classify the 50 states into regions. Its color encodes no data.

Not surprisingly, this map fails the self-sufficiency test (link). The entire dataset is printed on the map, and if those numbers were removed, we would be left with a map of the regions of the U.S. The graphical elements of the chart are not doing much work.

***

In the following chart, I used the map as a color legend. Also, an additional plot shows each region's price level against the national average.

Junkcharts_redo_businessinsider_gasprices2021

One can certainly ditch the map altogether, which makes having seven colors unnecessary. To address other questions, just stack on other charts, for example, showing the price increase versus last year.

***

_trifectacheckup_imageFrom a Trifecta Checkup perspective, we find that the trouble starts with the Q corner. There are several important questions not addressed by the graphic. In the D corner, no context is provided to interpret the data. Are these prices abnormal? How do they compare to the national average or to a year ago? In the V corner, the chart takes too much effort to comprehend a basic fact, such as which region has the highest average price.

For more on the Trifecta Checkup, see this guide.

 


Did the pandemic drive mass migration?

The Wall Street Journal ran this nice compact piece about migration patterns during the pandemic in the U.S. (link to article)

Wsj_migration

I'd look at the chart on the right first. It shows the greatest net flow of people out of the Northeast to the South. This sankey diagram is nicely done. The designer shows restraint in not printing the entire dataset on the chart. If a reader really cares about the net migration from one region to a specific other region, it's easy to estimate the number even though it's not printed.

The maps succinctly provide readers the definition of the regions.

To keep things in perspective, we are talking around 100,000 when the death toll of Covid-19 is nearing 600,000. Some people have moved but almost everyone else haven't.

***

The chart on the left breaks down the data in a different way - by urbanicity. This is a variant of the stacked column chart. It is a chart form that fits the particular instance of the dataset. It works only because in every month of the last three years, there was a net outflow from "large metro cores". Thus, the entire series for large metro cores can be pointed downwards.

The fact that this design is sensitive to the dataset is revealed in the footnote, which said that the May 2018 data for "small/medium metro" was omitted from the chart. Why didn't they plot that number?

It's the one datum that sticks out like a sore thumb. It's the only negative number in the entire dataset that is not associated with "large metro cores". I suppose they could have inserted a tiny medium green slither in the bottom half of that chart for May 2018. I don't think it hurts the interpretation of the chart. Maybe the designer thinks it might draw unnecessary attention to one data point that really doesn't warrant it.

***

See my collection of posts about Wall Street Journal graphics.


Dreamy Hawaii

I really enjoyed this visual story by ProPublica and Honolulu Star-Advertiser about the plight of beaches in Hawaii (link).

The story begins with a beautiful invitation:

Propublica_hawaiibeachesfrontimage

This design reminds me of Vimeo's old home page. (It no longer looks like this today but this screenshot came from when I was the data guy there.) In both cases, the images are not static but moving.

Vimeo-homepage

The tour de force of this visual story is an annotated walk along the Lanikai Beach. Here is a snapshot at one of the stops:

Propublica_hawaiibeaches_1368MokuluaDr_small

This shows a particular homeowner who, according to documents, was permitted to rebuild a destroyed seawall even though officials were supposed to disallow reconstruction in order to protect beaches from eroding. The property is marked on the map above. The image inside the box is a gif showing waves smashing the seawall.

As the reader scrolls down, the image window runs through a carousel of gifs of houses along the beach. The images are synchronized to the reader's progress along the shore. The narrative makes stops at specific houses at which point a text box pops up to provide color commentary.

***

The erosion crisis is shown in this pair of maps.

Propublica_hawaiibeaches_oldnewshoreline-sm

There's some fancy work behind the scenes to patch together images, and estimate the boundaries of th beaches.

***

The following map is notable for its simplicity. There are no unnecessary details and labels. We don't need to know the name of every street or a specific restaurant. Removing excess details makes readers focus on the informative parts. 

Propublica_hawaiibeaches_simplemap-sm

Clicking on the dots brings up more details.

***

Enjoy the entire story here.


Unlocking the secrets of a marvellous data visualization

Scmp_coronavirushk_paperThe graphics team in my hometown paper SCMP has developed a formidable reputation in data visualization, and I lapped every drop of goodness on this beautiful graphic showing how the coronavirus spread around Hong Kong (in the first wave in April). Marcelo uploaded an image of the printed version to his Twitter. This graphic occupied the entire back page of that day's paper.

An online version of the chart is found here.

The data graphic is a masterclass in organizing data. While it looks complicated, I had no problem unpacking the different layers.

Cases were divided into imported cases (people returning to Hong Kong) and local cases. A small number of cases are considered in-betweens.

Scmp_coronavirushk_middle

The two major classes then occupy one half page each. I first looked at the top half, where my attention is drawn to the thickest flows. The majority of imported cases arrived from the U.K., and most of those were returning students. The U.S. is the next largest source of imported cases. The flows are carefully ordered by continent, with the Americas on the left, followed by Europe, Middle East, Africa, and Asia.

Junkcharts_scmpcoronavirushk_americas1

Where there are interesting back stories, the flow blossoms into a flower. An annotation explains the cluster of cases. Each anther represents a case. Eight people caught the virus while touring Bolivia together.

Junkcharts_scmpcoronavirushk_bolivia

One reads the local cases in the same way. Instead of flowers, think of roots. The biggest cluster by far was a band that played at clubs in three different parts of the city, infecting a total of 72 people.

Junkcharts_scmpcoronavirushk_localband

Everything is understood immediately, without a need to read text or refer to legends. The visual elements carry that kind of power.

***

This data graphic presents a perfect amalgam of art and science. For a flow chart, the data are encoded in the relative thickness of the lines. This leaves two unused dimensions of these lines: the curvature and lengths. The order of the countries and regions take up the horizontal axis, but the vertical axis is free. Unshackled from the data, the designer introduced curves into the lines, varied their lengths, and dispersed their endings around the white space in an artistic manner.

The flowers/roots present another opportunity for creativity. The only data constraint is the number of cases in a cluster. The positions of the dots, and the shape of the lines leading to the dots are part of the playground.

What's more, the data visualization is a powerful reminder of the benefits of testing and contact tracing. The band cluster led to the closure of bars, which helped slow the spread of the coronavirus. 

 


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.

Nytimes_newyorkersleft_overallmap

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.

Nytimes_newyorksleft_percentathome

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%.

Redo_nyt_newyorkerslefttown

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.

Nyt_newyorkersleft_percenthomebyincome

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.]

 

 


Consumption patterns during the pandemic

The impact of Covid-19 on the economy is sharp and sudden, which makes for some dramatic data visualization. I enjoy reading the set of charts showing consumer spending in different categories in the U.S., courtesy of Visual Capitalist.

The designer did a nice job cleaning up the data and building a sequential story line. The spending are grouped by categories such as restaurants and travel, and then sub-categories such as fast food and fine dining.

Spending is presented as year-on-year change, smoothed.

Here is the chart for the General Commerce category:

Visualcapitalist_spending_generalcommerce

The visual design is clean and efficient. Even too sparse because one has to keep returning to the top to decipher the key events labelled 1, 2, 3, 4. Also, to find out that the percentages express year-on-year change, the reader must scroll to the bottom, and locate a footnote.

As you move down the page, you will surely make a stop at the Food Delivery category, noting that the routine is broken.

Visualcapitalist_spending_fooddelivery

I've featured this device - an element of surprise - before. Remember this Quartz chart that depicts drinking around the world (link).

The rule for small multiples is to keep the visual design identical but vary the data from chart to chart. Here, the exceptional data force the vertical axis to extend tremendously.

This chart contains a slight oversight - the red line should be labeled "Takeout" because food delivery is the label for the larger category.

Another surprise is in store for us in the Travel category.

Visualcapitalist_spending_travel

I kept staring at the Cruise line, and how it kept dipping below -100 percent. That seems impossible mathematically - unless these cardholders are receiving more refunds than are making new bookings. Not only must the entire sum of 2019 bookings be wiped out, but the records must also show credits issued to these credit (or debit) cards. It's curious that the same situation did not befall the airlines. I think many readers would have liked to see some text discussing this pattern.

***

Now, let me put on a data analyst's hat, and describe some thoughts that raced through my head as I read these charts.

Data analysis is hard, especially if you want to convey the meaning of the data.

The charts clearly illustrate the trends but what do the data reveal? The designer adds commentary on each chart. But most of these comments count as "story time." They contain speculation on what might be causing the trend but there isn't additional data or analyses to support the storyline. In the General Commerce category, the 50 to 100 percent jump in all subcategories around late March is attributed to people stockpiling "non-perishable food, hand sanitizer, and toilet paper". That might be true but this interpretation isn't supported by credit or debit card data because those companies do not have details about what consumers purchased, only the total amount charged to the cards. It's a lot more work to solidify these conclusions.

A lot of data do not mean complete or unbiased data.

The data platform provided data on 5 million consumers. We don't know if these 5 million consumers are representative of the 300+ million people in the U.S. Some basic demographic or geographic analysis can help establish the validity. Strictly speaking, I think they have data on 5 million card accounts, not unique individuals. Most Americans use more than one credit or debit cards. It's not likely the data vendor have a full picture of an individual's or a family's spending.

It's also unclear how much of consumer spending is captured in this dataset. Credit and debit cards are only one form of payment.

Data quality tends to get worse.

One thing that drives data analyst nuts. The spending categories are becoming blurrier. In the last decade or so, big business has come to dominate the American economy. Big business, with bipartisan support, has grown by (a) absorbing little guys, and (b) eliminating boundaries between industry sectors. Around me, there is a Walgreens, several Duane Reades, and a RiteAid. They currently have the same owner, and increasingly offer the same selection. In the meantime, Walmart (big box), CVS (pharmacy), Costco (wholesale), etc. all won regulatory relief to carry groceries, fresh foods, toiletries, etc. So, while CVS or Walgreens is classified as a pharmacy, it's not clear that what proportion of the spending there is for medicines. As big business grows, these categories become less and less meaningful.