A little stitch here, a great graphic is knitted

The Wall Street Journal used the following graphic to compare hurricanes Ida and Katrina (link to paywalled article).

Wsj_ida_katrina_hurricanes

This graphic illustrates the power of visual communications. Readers can learn a lot from it.

The paths of the storms can be compared. The geographical locations of the landfalls are shown. The strengthening of wind speeds as the hurricanes moved toward Louisiana is also displayed. Ida is clearly a lesser storm than Katrina: its wind speed never reached Category 5, and is generally lower at comparable time points.

The greatest feature of the WSJ graphic is how the designer stitches the two plots into one graphic. The anchors are two time points: when each storm attained enough wind speed to be classified as a hurricane (indicated by open dots), and when each storm made landfall in Louisiana. It is this little-noticed feature that makes it so easy to place each plot in context of the other.

Bravo!


Did prices go up or down? Depends on how one looks at the data

The U.S. media have been flooded with reports of runaway inflation recently, and it's refreshing to see a nice article in the Wall Street Journal that takes a second look at the data. Because as my readers know, raw data can be incredibly deceptive.

Inflation typically describes the change in price level relative to the prior year. The month-on-month change in price levels is a simple seasonal adjustment used to remove the effect of seasonality that masks the true change in price levels. (See this explainer of seasonal adjustment.)

As the pandemic enters the second year, this methodology is comparing 2021 price levels to pandemic-impacted price levels of 2020. This produces a very confusing picture. As the WSJ article explains, prices can be lower than they were in 2019 (pre-pandemic) and yet substantially higher than they were in 2020 (during the pandemic). This happens in industry sectors that were heavily affected by the economic shutdown, e.g. hotels, travel, entertainment.

Wsj_pricechangehotels_20192021Here is how they visualized this phenomenon. Amusingly, some algorithm estimated that it should take 5 minutes to read the entire article. It may take that much time to understand properly what this chart is showing.

Let me save you some time.

The chart shows monthly inflation rates of hotel price levels.

The pink horizontal stripes represent the official inflation numbers, which compare each month's hotel prices to those of a year prior. The most recent value for May of 2021 says hotel prices rose by 9% compared to May of 2020.

The blue horizontal stripes show an alternative calculation which compares each month's hotel prices to those of two years prior. Think of 2018-9 as "normal" years, pre-pandemic. Using this measure, we find that hotel prices for May of 2021 are about 4% lower than for May of 2019.

(This situation affects all of our economic statistics. We may see an expansion in employment levels from a year ago which still leaves us behind where we were before the pandemic.)

What confused me on the WSJ chart are the blocks of color. In a previous chart, the readers learn that solid colors mean inflation rose while diagonal lines mean inflation decreased. It turns out that these are month-over-month changes in inflation rates (notice that one end of the column for the previous month touches one end of the column of the next month).

The color patterns become the most dominant feature of this chart, and yet the month-over-month change in inflation rates isn't the crux of the story. The real star of the story should be the difference in inflation rates - for any given month - between two reference years.

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In the following chart, I focus attention on the within-month, between-reference-years comparisons.

Junkcharts_redo_wsj_inflationbaserate

Because hotel prices dropped drastically during the pandemic, and have recovered quite well in recent months as the U.S. reopens the economy, the inflation rate of hotel prices is almost 10%. Nevertheless, the current price level is still 7% below the pre-pandemic level.

 



 


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.

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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.

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See my collection of posts about Wall Street Journal graphics.


Aligning the visual and the data

The Washington Post reported a surge in donations to the Democrats after the death of Justice Ruth Ginsberg (link). A secondary effect, perhaps unexpected, was that donors decided to spread the money around; the proportion of donors who gave to six or more candidates jumped to 65%, where normally it is at 5%.

Wapo_donations

The text tells us what to look for, and the axis labels are commendably restrained. The color scheme is also intuitive.

There is something frustrating about this chart, though. It's that the spike is shown upside down. The level that the arrow points at is 45%, which is the total of the blue columns. The visual suggests the proportion of multiple beneficiaries (2 or more) should be 55%. There is a divergence between what the visual is saying and what the data are saying. Whichever number is correct, the required proportion is the inverse of the level shown on the percentage axis!

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This is the same chart flipped over.

Junkcharts_redo_wapo_donations

Now, the number we need can be read off the vertical axis.

I also moved the color legend to the right side so that the entries can be printed vertically, in the same direction as the data. This is one of the unspoken rules of data visualization I featured in my feature for DataJournalism.com.

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In the Trifecta Checkup (link), the issue is with the green arrow between the D corner and the V corner. The data and the visual are not in sync. 

 


Bubble charts, ratios and proportionality

A recent article in the Wall Street Journal about a challenger to the dominant weedkiller, Roundup, contains a nice selection of graphics. (Dicamba is the up-and-comer.)

Wsj_roundup_img1


The change in usage of three brands of weedkillers is rendered as a small-multiples of choropleth maps. This graphic displays geographical and time changes simultaneously.

The staircase chart shows weeds have become resistant to Roundup over time. This is considered a weakness in the Roundup business.

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In this post, my focus is on the chart at the bottom, which shows complaints about Dicamba by state in 2019. This is a bubble chart, with the bubbles sorted along the horizontal axis by the acreage of farmland by state.

Wsj_roundup_img2

Below left is a more standard version of such a chart, in which the bubbles are allowed to overlap. (I only included the bubbles that were labeled in the original chart).

Redo_roundupwsj0

The WSJ’s twist is to use the vertical spacing to avoid overlapping bubbles. The vertical axis serves a design perogative and does not encode data.  

I’m going to stick with the more traditional overlapping bubbles here – I’m getting to a different matter.

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The question being addressed by this chart is: which states have the most serious Dicamba problem, as revealed by the frequency of complaints? The designer recognizes that the amount of farmland matters. One should expect the more acres, the more complaints.

Let's consider computing directly the number of complaints per million acres.

The resulting chart (shown below right) – while retaining the design – gives a wholly different feeling. Arkansas now owns the largest bubble even though it has the least acreage among the included states. The huge Illinois bubble is still large but is no longer a loner.

Redo_dicambacomplaints1

Now return to the original design for a moment (the chart on the left). In theory, this should work in the following manner: if complaints grow purely as a function of acreage, then the bubbles should grow proportionally from left to right. The trouble is that proportional areas are not as easily detected as proportional lengths.

The pair of charts below depict made-up data in which all states have 30 complaints for each million acres of farmland. It’s not intuitive that the bubbles on the left chart are growing proportionally.

Redo_dicambacomplaints2

Now if you look at the right chart, which shows the relative metric of complaints per million acres, it’s impossible not to notice that all bubbles are the same size.


Pay levels in the U.S.

The Wall Street Journal published a graphic showing the median pay levels at "most" public companies in the U.S. here.

Wsj_mediancompanypay

People who attended my dataviz seminar might recognize the similarity with the graphic showing internet download speeds by different broadband technologies. It's a clean, clear way of showing multiple comparisons on the same chart.

You can see the distribution of pay levels of companies within each industry grouping, and the vertical lines showing the sector medians allow comparison across sectors. The median pay levels are quite similar with the energy sector leaning higher, and consumer sector leaning lower.

The consumer sector is extremely heavy on the low side of the pay range. Companies like Universal, Abercrombie, Skechers, Mattel, Gap, etc. all pay at least half their employees less than $6,000. The data is sourced to MyLogIQ. I have no knowledge of how reliable or valid the data are. It's curious to me that Dunkin Brands showed a median of $110K while Starbucks showed $13K.

Wsj_medianpay_dunkinstarbucks

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I like the interactive features.

The window control lets the user zoom in to different parts of the pay range. This is necessary because of the extremely high salaries. The control doubles as a presentation of the overall distribution of median salaries.

The text box can be used to add data labels to specific companies.

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See previous discussion of WSJ Graphics.

 


The ebb and flow of an effective dataviz showing the rise and fall of GE

Wsj_ebbflowGE_800A WSJ chart caught my eye the other day – I spotted someone looking at it in a coffee shop, and immediately got a hold of a copy. The chart plots the ebb and flow of GE’s revenues from the 1980s to the present.

What grabbed my attention? The less-used chart form, and the appealing but not too gaudy color scheme.

The chart presents a highly digestible view of the structure of GE’s revenues. We learn about GE’s major divisions, as well as how certain segments split from or merged with others over time. Major acquisitions and divestitures are also depicted; if these events are the main focus, the designer should find ways to make these moments stand out more.

An interesting design decision concerns the sequence of the divisions. One possible order is by increasing or decreasing importance, typically indicated by proportional revenues. This is complicated by the changing nature of the business over the decades. So financial services went from nothing to the largest division by far to almost disappearing.

The sequencing need not be data-driven; it can be design-constrained. The merging and splitting of business units are conveyed via linking arrows. Longer arrows are unsightly, and meshes of arrows are confusing.

On this chart, the long arrow pointing from the orange to the gray around 2004 feels out of place. What if the financial services block is moved to the right of the consumer block? That will significantly shorten the long arrow. It won’t create other entanglements as the media block is completely disjoint and there are no other arrows tying financial services to another division.

 

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To improve readability, the bars are spaced out horizontally. The addition of whitespace distorts the proportionality. So, in 2001, the annotation states that financial services (orange) accounted for “about half of the revenues,” which is directly contradicted by the visual perception – readers find the orange bar to be clearly shorter than the total length of the other bars. This is a serious deficiency of the chart form but this chart conveys the "ebb and flow" very well.


Making people jump over hoops

Take a look at the following chart, and guess what message the designer wants to convey:

Wsj_brokercensus

This chart accompanied an article in the Wall Street Journal about Wells Fargo losing brokers due to the fake account scandal, and using bonuses to lure them back. Like you, my first response to the chart was that little has changed from 2015 to 2017.

It is a bit mysterious the intention of the whitespace inserted to split the four columns into two pairs. It's not obvious that UBS and Merrill are different from Wells Fargo and Morgan Stanley. This device might have been used to overcome the difficulty of reading four columns side by side.

The additional challenge of this dataset is the outlier values for UBS, which elongates the range of the vertical axis, squeezing together the values of the other three banks.

In this first alternative version, I play around with irregular gridlines.

Jc_redo_wsjbrokercensus1

Grouped column charts are not great at conveying changes over time, as they cause our eyes to literally jump over hoops. In the second version, I use a bumps chart to compactly highlight the trends. I also zoom in on the quarterly growth rates.

Jc_redo_wsjbrokercensus2

The rounded interpolation removes the sharp angles from the typical bumps chart (aka slopegraph) but it does add patterns that might not be there. This type of interpolation however respects the values at the "knots" (here, the quarterly values) while a smoother may move those points. On balance, I like this treatment.

 

PS. [6/2/2017] Given the commentary below, I am including the straight version of the chart, so you can compare. The straight-line version is more precise. One aspect of this chart form I dislike is the sharp angles. When there are more lines, it gets very entangled.

Jc_redo_wsjbrokercensus3


Lines that delight, lines that blight

This WSJ graphic caught my eye. The accompanying article is here.

Wsj_ipo_dealdrought_full

The article (judging from the sub-header) makes two separate points, one about the total amount of money raised in IPOs in a year, and the change in market value of those newly-public companies one year from the IPO date.

The first metric is shown by the size of the bubbles while the second metric is displayed as distances from the horizontal axis. (The second metric is further embedded, in a simplified, binary manner, in the colors of the bubbles.)

The designer has decided that the second metric - performance after IPO - to be more important. Therefore, it is much easier for readers to know how each annual cohort of IPOs has performed. The use of color to map to the second metric (and not the first) also helps to emphasize the second metric.

There are details on this chart that I admire. The general tidiness of it. The restraint on the gridlines, especially along the horizontal ones. The spatial balance. The annotation.

And ah, turning those bubbles into lollipops. Yummy! Those dotted lines allow readers to find the center of each bubble, which is where the values of the second metrics lie. Frequently, these bubble charts are presented without those guiding lines, and it is often hard to find the circles' anchors.

That leaves one inexplicable decision - why did they place two vertical gridlines in the middle of two arbitrary years?


Story within story, bar within bar

This Wall Street Journal offering caught my eye.

Wsj_gender_workforce_sm

It's the unusual way of displaying proportions.

Your first impression is to interpret the graphic as a bar chart. But it really is a bar within a bar: the crux of the matter - gender balance - is embedded in individual bars.

Instead of pie charts or stacked bar charts, we see  stacked columns within each bar.

I see what the designer is attempting to accomplish. The first message is the sharp decline in gender equality at higher job titles. The next message is the sharp drop in the frequency of higher job titles.

This chart is a variant of the "Marimekko" chart (beloved by management consultants), also called the mosaic chart. The only difference being how the distribution of jobs in the work force is coded.

The Marimekko is easier to understand:

Redo_wsjgenderworkforce_mekko2

A key advantage of this version is to be found in the thin columns.

Here is another way to visualize this data, drawing attention to the gender gap.

Redo_wsjgenderworkforce_lines

In the other versions, the reader must do subtractions to figure out the size of the gaps.