Excess delay

The hot topic in New York at the moment is congestion pricing for vehicles entering Manhattan, which is set to debut during the month of June. I found this chart (link) that purports to prove the effectiveness of London's similar scheme introduced a while back.

This is a case of the visual fighting against the data. The visual feels very busy and yet the story lying beneath the data isn't that complex.

This chart was probably designed to accompany some text which isn't available free from that link so I haven't seen it. The reader's expectation is to compare the periods before and after the introduction of congestion charges. But even the task of figuring out the pre- and post-period is taking more time than necessary. In particular, "WEZ" is not defined. (I looked this up, it's "Western Extension Zone" so presumably they expanded the area in which charges were applied when the travel rates went back to pre-charging levels.)

The one element of the graphic that raises eyebrows is the legend which screams to be read.

Why are there four colors for two items? The legend is not self-sufficient. The reader has to look at the chart itself and realize that purple is the pre-charging period while green (and blue) is the post-charging period (ignoring the distinction between CCZ and WEZ).

While we are solving this puzzle, we also notice that the bottom two colors are used to represent an unchanging quantity - which is the definition of "no congestion". This no-congestion travel rate is a constant throughout the chart and yet a lot of ink of two colors have been spilled on it. The real story is in the excess delay, which the congestion charging scheme was supposed to reduce.

The excess on the chart isn't harmless. The excess delay on the roads has been transferred to the chart reader. It actually distracts from the story the analyst is wanting to tell. Presumably, the story is that the excess delays dropped quite a bit after congestion charging was introduced. About four years later, the travel rates had creeped back to pre-charging levels, whereupon the authorities responded by extending the charging zone to WEZ (which as of the time of the chart, wasn't apparently bringing the travel rate down.)

Instead of that story, the excess of the chart makes me wonder... the roads are still highly congested with travel rates far above the level required to achieve no congestion, even after the charging scheme was introduced.

***

I started removing some of the excess from the chart. Here's the first cut:

This is better but it is still very busy. One problem is the choice of columns, even though the data are found strictly on the top of each column. (Besides, when I chop off the unchanging sections of the columns, I created a start-not-from-zero problem.) Also, the labeling of the months leaves much to be desired, there are too many grid lines, etc.

***

Here is the version I landed on. Instead of columns, I use lines. When lines are used, there is no need for month labels since we can assume a reader knows the structure of months within a year.

A priniciple I hold dear is not to have legends unless it is absolutely required. In this case, there is no need to have a legend. I also brought back the notion of a uncongested travel speed, with a single line (and annotation).

***

The chart raises several questions about the underlying analysis. I'd interested in learning more about "moving car observer surveys". What are those? Are they reliable?

Further, for evidence of efficacy, I think the pre-charging period must be expanded to multiple years. Was 2002 a particularly bad year?

Thirdly, assuming WEZ indicates the expansion of the program to a new geographical area, I'm not sure whether the data prior to its introduction represents the travel rate that includes the WEZ (despite no charging) or excludes it. Arguments can be made for each case so the key from a dataviz perspective is to clarify what was actually done.

 

P.S. [6-6-24] On the day I posted this, NY State Governer decided to cancel the congestion pricing scheme that was set to start at the end of June.

Messing with expectations

A co-worker sent me to the following map, found in Forbes:

It shows the amount of state tax surcharge per gallon of gas in the U.S. And it's got one of the most common issues found in choropleth maps - the color scheme runs opposite to reader expectations.

Typically, if we see a red-green color scale, we would expect red to represent large numbers and green, small numbers. This map reverses the typical setup: California, the state with the heftiest gas tax, is shown green.

I know, I know - if we apply the typical color scheme, California would bleed red, and it's a blue state, damn it.

The solution is to avoid the red color. Just don't use red or blue.

There is no need to use two colors either.

***

A few minor fixes. Given that all dollar amounts on the map are shown to two decimal places, the legend labels should also be shown to 2 decimal places, and with dollar signs.

The subtitle should read "Dollars per gallon" instead of "Cents per gallon". Alternatively, keep "Cents per gallon" but convert all data labels into cents.

Some of the states are missing data labels.

***

I recast this as a small-multiples by categorizing states into four subgroups.

With this change, one can almost justify using maps because there is sort of a spatial pattern.

 

 

The choice to encode data using colors

NBC News published the following heatmap that shows inflation by product category in the last year or so:

The general story might be that inflation was rampant in airfare and electricity prices about a year ago but these prices have moderated recently, especially in airfare. Gas prices appear to have inflated far less than overall inflation during these months.

***

Now, if you're someone who cares about the magnitude of differences, not just the direction, then revisit the above statements, and you'll feel a sense of inadequacy.

When we choose to encode data in colors, we're giving up on showing magnitudes or precision. The color scale shown up top sends the message that the continuous nature of the number line is being displayed but it really isn't.

The largest value of the chart is found on the left side of the airfare row:

The value is about 36% which strangely enough is far larger than the maximum value shown in the legend above. Even if those values align, it is still impossible to guess what values the different colors and shades in the cells map to from the legend.

***

The following small-multiples chart shows the underlying values more precisely:

I have transformed the data differently. In these line charts, the data are indexed to the first month (100) so each chart shows the cumulative change in prices from that month to the current month, for each category, compared to the overall.

The two most interesting categories are airfare and gas. Airfare has recently decreased quite drastically relative to September 2022, and thus the line is far below the overall inflation trend. Gas prices moved in reverse: they dropped in the last quarter of 2022 but have steadily risen over 2023, and in the most recent month, is tracking overall inflation.

 

 

An elaborate data vessel

I recently came across the following dataviz showing global oil production (link).

This is an ambitious graphic that addresses several questions of composition.

The raw data show the amount of production by country adding up to the global total. The countries are then grouped by region. Further, the graph presents an oil-and-gas specific grouping, as indicated by the legend shown just below the chart title. This grouping is indicated by the color of the circumference of the circle containing the flag of the country.

This chart form is popular in modern online graphics programs. It is like an elaborate data vessel. Because the countries are lined up around the barrel, a space has been created on three sides to admit labels and text annotations. This is a strength of this chart form.

***

The chart conveys little information about the underlying data. Each country is given a unique odd shaped polygon, making it impossible to compare sizes. It’s definitely possible to pick out U.S., Russia, Saudi Arabia as the top producers. But in presenting the ranks of the data, this chart form pales in comparison to a straightforward data table, or a bar chart. The less said about presenting values, the better.

Indeed, our self-sufficiency test exposes the inability of these polygons to convey the data. This is precisely why almost all values of the dataset are present on the chart.

***

The dataviz subtly presumes some knowledge on the part of the readers.

The regions are not directly labeled. The readers must know that Saudi Arabia is in the Middle East, U.S. is part of North America, etc. Admittedly this is not a big ask, but it is an ask.

It is also assumed that readers know their flags, especially those of smaller countries. Some of the small polygons have no space left for country names and they are labeled with just flags.

In addition, knowing country acronyms is required for smaller countries as well. For example, in Africa, we find AGO, COG and GAB.

For this chart form the designer treats each country according to the space it has on the chart (except those countries that found themselves on the edges of the barrel). Font sizes, icons, labels, acronyms, data labels, etc. vary.

The readers are assumed to know the significance of OPEC and OPEC+. This grouping is given second fiddle, and can be found via the color of the circumference of the flag icons.

I’d have not assigned a color to the non-OPEC countries, and just use the yellow and blue for OPEC and OPEC+. This is a little edit but makes the search for the edges more efficient.

***

Let’s now return to the perception of composition.

In exactly the same manner as individual countries, the larger regions are represented by polygons that have arbitrary shapes. One can strain to compile the rank order of regions but it’s impossible to compare the relative values of production across regions. Perhaps this explains the presence of another chart at the bottom that addresses this regional comparison.

The situation is worse for the OPEC/OPEC+ grouping. Now, the readers must find all flag icons with edges of a specific color, then mentally piece together these arbitrarily shaped polygons, then realizing that they won’t fit together nicely, and so must now mentally morph the shapes in an area-preserving manner, in order to complete this puzzle.

This is why I said earlier this is an elaborate data vessel. It’s nice to look at but it doesn’t convey information about composition as readers might expect it to.

Dataviz in camouflage

This subway timetable in Tokyo caught my eye:

It lists the departure times of all trains going toward Shibuya on Saturdays and holidays.

It's a "stem and leaf" plot.

The stem-and-leaf plot is a crude histogram. In this version, the stem is the hour of the day (24-hour clock) and the leaf is the minute (between 0 and 59). The longer the leaf, the higher the frequency of trains.

We can see that there isn't one peak but rather a plateau between hours 9 and 18.

***

Contrast this with the weekday schedule in blue:

We can clearly see two rush hours, one peak at hour 8 and a second one at hours 17-18.

Love seeing dataviz in camouflage!

 

Parsons Student Projects

I had the pleasure of attending the final presentations of this year's graduates from Parsons's MS in Data Visualization program. You can see the projects here.

***

A few of the projects caught my eye.

A project called "Authentic Food in NYC" explores where to find "authentic" cuisine in New York restaurants. The project is notable for plowing through millions of Yelp reviews, and organizing the information within. Reviews mentioning "authentic" or "original" were extracted.

During the live presentation, the student clicked on Authentic Chinese, and the name that popped up was Nom Wah Tea Parlor, which serves dim sum in Chinatown that often has lines out the door.

Curiously, the ranking is created from raw counts of authentic reviews, which favors restaurants with more reviews, such as restaurants that have been operating for a longer time. It's unclear what rule is used to transfer authenticity from reviews to restaurants: does a single review mentioning "authentic" qualify a restaurant as "authentic", or some proportion of reviews?

Later, we see a visualization of the key words found inside "authentic" reviews for each cuisine. Below are words for Chinese and Italian cuisines:

These are word clouds with a twist. Instead of encoding the word counts in the font sizes, she places each word inside a bubble, and uses bubble sizes to indicate relative frequency.

Curiously, almost all the words displayed come from menu items. There isn't any subjective words to be found. Algorithms that extract keywords frequently fail in the sense that they surface the most obvious, uninteresting facts. Take the word cloud for Taiwanese restaurants as an example:

The overwhelming keyword found among reviews of Taiwanese restaurants is... "taiwanese". The next most important word is "taiwan". Among the remaining words, "886" is the name of a specific restaurant, "bento" is usually associated with Japanese cuisine, and everything else is a menu item.

Getting this right is time-consuming, and understandably not a requirement for a typical data visualization course.

The most interesting insight is found in this data table.

It appears that few reviewers care about authenticity when they go to French, Italian, and Japanese restaurants but the people who dine at various Asian restaurants, German restaurants, and Eastern European restaurants want "authentic" food. The student concludes: "since most Yelp reviewers are Americans, their pursuit of authenticity creates its own trap: Food authenticity becomes an americanized view of what non-American food is."

This hits home hard because I know what authentic dim sum is, and Nom Wah Tea Parlor it ain't. Let me check out what Yelpers are saying about Nom Wah:

1. Everything was so authentic and delicious - and cheap!!!
2. Your best bet is to go around the corner and find something more authentic.
3. Their dumplings are amazing everything is very authentic and tasty!
4. The food was delicious and so authentic, and the staff were helpful and efficient.
5. Overall, this place has good authentic dim sum but it could be better.
6. Not an authentic experience at all.
7. this dim sum establishment is totally authentic
8. The onions, bean sprouts and scallion did taste very authentic and appreciated that.
9. I would skip this and try another spot less hyped and more authentic.
10. I would have to take my parents here the next time I visit NYC because this is authentic dim sum.

These are the most recent ten reviews containing the word "authentic". Seven out of ten really do mean authentic, the other three are false friends. Text mining is tough business! The student removed "not authentic" which helps. As seen from above, "more authentic" may be negative, and there may be words between "not" and "authentic". Also, think "not inauthentic", "people say it's authentic, and it's not", etc.

One thing I learned from this project is that "authentic" may be a synonym for "I like it" when these diners enjoy the food at an ethnic restaurant. I'm most curious about what inauthentic onions, bean sprouts and scallion taste like.

I love the concept and execution of this project. Nice job!

***

Another project I like is about tourism in Venezuela. The back story is significant. Since a dictatorship took over the country, the government stopped reporting tourism statistics. It's known that tourism collapsed, and that it may be gradually coming back in recent years.

This student does not have access to ready-made datasets. But she imaginatively found data to pursue this story. Specifically, she mentioned grabbing flight schedules into the country from the outside.

The flow chart is a great way to explore this data:

A map gives a different perspective:

I'm glad to hear the student recite some of the limitations of the data. It's easy to look at these visuals and assume that the data are entirely reliable. They aren't. We don't know that what proportion of the people traveling on those flights are tourists, how full those planes are, or the nationalities of those on board. The fact that a flight originated from Panama does not mean that everyone on board is Panamanian.

***

The third project is interesting in its uniqueness. This student wants to highlight the effect of lead in paint on children's health. She used the weight of lead marbles to symbolize the impact of lead paint. She made a dress with two big pockets to hold these marbles.

It's not your standard visualization. One can quibble that dividing the marbles into two pockets doesn't serve a visualziation purpose, and so on. But at the end, it's a memorable performance.

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.

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.

In 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.

I'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.

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:

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

A 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:

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

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.

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.