Gazing at petals

Reader Murphy pointed me to the following infographic developed by Altmetric to explain their analytics of citations of journal papers. These metrics are alternative in that they arise from non-academic media sources, such as news outlets, blogs, twitter, and reddit.

The key graphic is the petal diagram with a number in the middle.

Altmetric_tetanus

I have a hard time thinking of this object as “data visualization”. Data visualization should visualize the data. Here, the connection between the data and the visual design is tenuous.

There are eight petals arranged around the circle. The legend below the diagram maps the color of each petal to a source of data. Red, for example, represents mentions in news outlets, and green represents mentions in videos.

Each petal is the same size, even though the counts given below differ. So, the petals are like a duplicative legend.

The order of the colors around the circle does not align with its order in the table below, for a mysterious reason.

Then comes another puzzle. The bluish-gray petal appears three times in the diagram. This color is mapped to tweets. Does the number of petals represent the much higher counts of tweets compared to other mentions?

To confirm, I pulled up the graphic for a different paper.

Altmetric_worldwidedeclineofentomofauna

Here, each petal has a different color. Eight petals, eight colors. The count of tweets is still much larger than the frequencies of the other sources. So, the rule of construction appears to be one petal for each relevant data source, and if the total number of data sources fall below eight, then let Twitter claim all the unclaimed petals.

A third sample paper confirms this rule:

Altmetric_dnananodevices

None of the places we were hoping to find data – size of petals, color of petals, number of petals – actually contain any data. Anything the reader wants to learn can be directly read. The “score” that reflects the aggregate “importance” of the corresponding paper is found at the center of the circle. The legend provides the raw data.

***

Some years ago, one of my NYU students worked on a project relating to paper citations. He eventually presented the work at a conference. I featured it previously.

Michaelbales_citationimpact

Notice how the visual design provides context for interpretation – by placing each paper/researcher among its peers, and by using a relative scale (percentiles).

***

I’m ignoring the D corner of the Trifecta Checkup in this post. For any visualization to be meaningful, the data must be meaningful. The type of counting used by Altmetric treats every tweet, every mention, etc. as a tally, making everything worth the same. A mention on CNN counts as much as a mention by a pseudonymous redditor. A pan is the same as a rave. Let’s not forget the fake data menace (link), which  affects all performance metrics.


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.

***

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.

***

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.


Tennis greats at the top of their game

The following chart of world No. 1 tennis players looks pretty but the payoff of spending time to understand it isn't high enough. The light colors against the tennis net backdrop don't work as intended. The annotation is well done, and it's always neat to tug a legend inside the text.

Tableautennisnumberones

The original is found at Tableau Public (link).

The topic of the analysis appears to be the ages at which tennis players attained world #1 ranking. Here are the male players visualized differently:

Redo_junkcharts_no1tennisplayers

Some players like Jimmy Connors and Federer have second springs after dominating the game in their late twenties. It's relatively rare for players to get to #1 after 30.


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.

Wiki-Simple_Periodic_Table_Chart-en.svg

(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).

***

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.

***

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".

Mg32190402_long_conventional

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:

Hyde_periodictable

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.

Tumblr_n3tbz5rIKk1s3r80lo3_1280

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.

Elements_relative_abundance

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.

Periodic-bonds_1024

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.

***

I found the following articles useful when researching this post:

The Conversation

Royal Chemistry Society

 


Bar-density and pie-density plots for showing relative proportions

In my last post, I described a bar-density chart to show paired data of proportions with an 80/20-type rule. The following example illustrates that a small proportion of Youtubers generate a large proportion of views.

Jc_redo_youtube_bar_2col

Other examples of this type of data include:

  • the top 10% of families own 75% of U.S. household wealth (link)
  • the top 1% of artists earn 77% of recorded music income (link)
  • Five percent of AT&T customers consume 46% of the bandwidth (link)

In all these examples, the message of the data is the importance of a small number of people (top earners, superstars, bandwidth hogs). A good visual should call out this message.

The bar-density plot consists of two components:

  • the bar chart which shows the distribution of the data (views, wealth, income, bandwidth) among segments of people;
  • The embedded Voronoi diagram within each bar that encodes the relative importance of each people segment, as measured by the (inverse) density of the population among these segments - a people segment is more important if each individual accounts for more of the data, or in other words, the density of people within the group is lower.

The bar chart can adopt a more conventional horizontal layout.

Jc_redo_youtube_bar_h_2col

Voronoi tessellation

To understand the Voronoi diagram, think of a fixed number (say, 100) of randomly placed points inside a bar. Then, for any point inside the bar area, it has a nearest neighbor among those 100 fixed points. Assign every point on the surface to its nearest neighbor. From this, one can draw a boundary around each of the 100 points to include all its nearest neighbors. The resulting tessellation is the Voronoi diagram. (The following illustration comes from this AMS column.)

Ams_voronoi

 

The density of points in the respective bars encodes the relative proportions of people within those groups. For my example, I placed 6 points in the red bar, 666 points in the yellow bar, and ~2000 points in the gray bar, which precisely represents the relative proportions of creators in the three segments.

Density is represented statistically

Notice that the density is represented statistically, not empirically. According to the annotation on the original chart, the red bar represents 14,000 super-creators. Correspondingly, there are 4.5 million creators in the gray bar. Any attempt to plot those as individual pieces will result in a much less impactful graphic. If the representation is interpreted statistically, as relative densities within each people segment, the message of relative importance of the units within each group is appropriately conveyed.

A more sophisticated way of deciding how many points to place in the red bar is to be developed. Here, I just used the convenient number of 6.

The color shades are randomly applied to the tessellation pieces, and used to facilitate reading of densities.

***

In this section, I provide R code for those who want to explore this some more. This is code used for prototyping, and you're welcome to improve them. The general strategy is as follows:

  • Set the rectangular area (bar) in which the Voronoi diagram is to be embedded. The length of the bar is set to the proportion of views, appropriately scaled. The code utilizes the dirichlet function within the spatstat package to generate the fixed points; this requires setting up the owin parameter to represent a rectangle.
  • Set the number of points (n) to be embedded in the bar, determined by the relative proportion of creators, appropriately scaled. Generate a data frame containing the x-y coordinates of n randomly placed points, within the rectangle defined above.
  • Use the ppp function to generate the Voronoi data
  • Set up a colormap for plotting the Voronoi diagram
  • Plot the Voronoi diagram; assign shades at random to the pieces (in a production code, these random numbers should be set as marks in the ppp but it's easier to play around with the shades if placed here)

The code generates separate charts for each bar segment. A post-processing step is currently required to align the bars to attain equal height. I haven't figured out whether the multiplot option helps here.

library(spatstat)

# enter the scaled proportions of creators and views
# the Youtube example has three creator segments

# number of randomly generated points should be proportional to proportion of creators. Multiply nc by a scaling factor if desired

nc = c(3, 33, 965)*2

# bar widths should be proportional to proportion of views
# total width should be set based on the width of your page

wide = c(378, 276, 346)/2

# set bar height, to attain a particular aspect ratio
bar_h = 50

# define function to generate points
# defines rectangular window

makepoints = function (n, wide, height) {
    df <- data.frame(x = runif(n,0,wide),y = runif(n,0,height))
    W <- owin( c(0, wide), c(0,height) ) # rectangular window
    pp1 <- as.ppp( df, W )
    y <- dirichlet(pp1)
    # y$marks <- sample(0:wide, n, replace=T) # marks are for colors
    return (y)
}

y_red = makepoints(nc[1], wide[1], bar_h) # height of each bar fixed
y_yel = makepoints(nc[2], wide[2], bar_h)
y_gry = makepoints(nc[3], wide[3], bar_h)

# setting colors (4 shades per bar, one color per bar)

cr_red = colourmap(c("lightsalmon","lightsalmon2", "lightsalmon4", "brown"), breaks=round(seq(0, wide[1],length.out=5)))

cr_yel = colourmap(c("burlywood1", "burlywood2", "burlywood3", "burlywood4"), breaks=round(seq(0, wide[2],length.out=5)))

cr_gry = colourmap(c("gray80", "gray60", "gray40", "gray20"), breaks=round(seq(0, wide[3],length.out=5)))

# plotting

par(mar=c(0,0,0,0))


# add png to save image to png

# remove values= if colors set in ppp

plot.tess(y_red, main="", border="pink3", do.col=T, values = sample(0:wide[1], nc[1], replace=T), col=cr_red, xlim=c(0, wide[1]), ylim=c(0,bar_h), ribbon=F)

plot.tess(y_yel, main="", border="darkgoldenrod4", do.col=T, values=sample(0:wide[2], nc[2], replace=T), col=cr_yel, xlim=c(0, wide[2]), ylim=c(0,bar_h), ribbon=F)

plot.tess(y_gry, main="", border="darkgray", do.col=T, values=sample(0:wide[3], nc[3], replace=T), col=cr_gry, xlim=c(0, wide[3]), ylim=c(0,bar_h), ribbon=F)

# because of random points, the tessellation looks different each time
# post-processing: make each bar the same height when aligned side by side

***

A cousin of the bar-density plot is the pie-density plot. Since I'm using only three creator segments, which each account for about 30-40% of the total views, it is natural to use a pie chart. In this case, we embed the Voronoi diagrams into the pie sectors.

Jc_redo_youtube_pie_lobsided

If the distribution were more even, that is to say, the creators are more or less equally important, the pie-density plot looks like this:

Redo_jc_youtube_pie_even

***

Something that is more like 80/20

The original chart shows the top 0.3 percent generating almost 40 percent of the views. A more typical insight is top X percent generates 80 percent of the data. For the YouTube data, X is 11 percent. What does the pie-density chart look like if  top 11 percent <-> 80 percent, middle 33 percent <-> 11 percent, bottom 56 percent <-> 8 percent?

Jc_youtube_8020_barh_pie

Roughly speaking, the second segment includes 3 times the people as the largest, and the third has 5 times as the largest.

 

P.S.

1) Check out my first Linkedin "article" on this topic. 

2) The first post on bar-density charts is here.

 

 

 

 

 

 

 

 

 


Visualizing the 80/20 rule, with the bar-density plot

Through Twitter, Danny H. submitted the following chart that shows a tiny 0.3 percent of Youtube creators generate almost 40 percent of all viewing on the platform. He asks for ideas about how to present lop-sided data that follow the "80/20" rule.

Dannyheiman_twitter_youtubedata

In the classic 80/20 rule, 20 percent of the units account for 80 percent of the data. The percentages vary, so long as the first number is small relative to the second. In the Youtube example, 0.3 percent is compared to 40 percent. The underlying reason for such lop-sidedness is the differential importance of the units. The top units are much more important than the bottom units, as measured by their contribution to the data.

I sense a bit of "loss aversion" on this chart (explained here). The designer color-coded the views data into blue, brown and gray but didn't have it in him/her to throw out the sub-categories, which slows down cognition and adds hardly to our understanding.

I like the chart title that explains what it is about.

Turning to the D corner of the Trifecta Checkup for a moment, I suspect that this chart only counts videos that have at least one play. (Zero-play videos do not show up in a play log.) For a site like Youtube, a large proportion of uploaded videos have no views and thus, many creators also have no views.

***

My initial reaction on Twitter is to use a mirrored bar chart, like this:

Jc_redo_youtube_mirrorbar_lobsided

I ended up spending quite a bit of time exploring other concepts. In particular, I like to find an integrated way to present this information. Most charts, such as the mirrored bar chart, a Bumps chart (slopegraph), and Lorenz chart, keep the two series of percentages separate.

Also, the biggest bar (the gray bar showing 97% of all creators) highlights the least important Youtubers while the top creators ("super-creators") are cramped inside a slither of a bar, which is invisible in the original chart.

What I came up with is a bar-density plot, where I use density to encode the importance of creators, and bar lengths to encode the distribution of views.

Jc_redo_youtube_bar_h_2col

Each bar is divided into pieces, with the number of pieces proportional to the number of creators in each segment. This has the happy result that the super-creators are represented by large (red) pieces while the least important creators by little (gray) pieces.

The embedded tessellation shows the structure of the data: the bottom third of the views are generated by a huge number of creators, producing a few views each - resulting in a high density. The top 38% of the views correspond to a small number of super-creators - appropriately shown by a bar of low density.

For those interested in technicalities, I embed a Voronoi diagram inside each bar, with randomly placed points. (There will be a companion post later this week with some more details, and R code.)

Here is what the bar-density plot looks like when the distribution is essentially uniform:

Jc_redo_youtube_bar_even
The density inside each bar is roughly the same, indicating that the creators are roughly equally important.

 

P.S.

1) The next post on the bar-density plot, with some experimental R code, will be available here.

2) Check out my first Linkedin "article" on this topic.

 

 

 

 

 


Pretty circular things

National Geographic features this graphic illustrating migration into the U.S. from the 1850s to the present.

Natgeo_migrationtreerings

 

What to Like

It's definitely eye-catching, and some readers will be enticed to spend time figuring out how to read this chart.

The inset reveals that the chart is made up of little colored strips that mix together. This produces a pleasing effect of gradual color gradation.

The white rings that separate decades are crucial. Without those rings, the chart becomes one long run-on sentence.

Once the reader invests time in learning how to read the chart, the reader will grasp the big picture. One learns, for example, that migrants from the most recent decades have come primarily from Latin America (orange) or Asia (pink). Migrants from Europe (green) and Canada (blue) came in waves but have been muted in the last few decades.

 

What's baffling

Initially, the chart is disorienting. It's not obvious whether the compass directions mean anything. We can immediately understand that the further out we go, the larger numbers of migrants. But what about which direction?

The key appears in the legend - which should be moved from bottom right to top left as it's so important. Apparently, continent/country of origin is coded in the directions.

This region-to-color coding seems to be rough-edged by design. The color mixing discussed above provides a nice artistic effect. Here, the reader finds out that mixing is primarily between two neighboring colors, thus two regions placed side by side on the chart. Thus, because Europe (green) and Asia (pink) are on opposite sides of the rings, those two colors do not mix.

Another notable feature of the chart is the lack of any data other than the decade labels. We won't learn how many migrants arrived in any decade, or the extent of migration as it impacts population size.

A couple of other comments on the circular design.

The circles expand in size for sure as time moves from inside out. Thus, this design only works well for "monotonic" data, that is to say, migration always increases as time passes.

The appearance of the chart is only mildly affected by the underlying data. Swapping the regions of origin changes the appearance of this design drastically.

 

 

 

 

 


Trump resistance chart: cleaning up order, importance, weight, paneling

Morningconsult_gopresistance_trVox featured the following chart when discussing the rise of resistance to President Trump within the GOP.

The chart is composed of mirrored bar charts. On the left side, with thicker pink bars that draw more attention, the design depicts the share of a particular GOP demographic segment that said they'd likely vote for a Trump challenger, according to a Morning Consult poll.

This is the primary metric of interest, and the entire chart is ordered by descending values from African Americans who are most likely (67%) to turn to a challenger to those who strongly support Trump and are the least likely (17%) to turn to someone else.

The right side shows the importance of each demographic, measured by the share of GOP. The relationship between importance and likelihood to defect from Trump is by and large negative but that fact takes a bit of effort to extract from this mirrored bar chart arrangement.

The subgroups are not complete. For example, the only ethnicity featured is African Americans. Age groups are somewhat more complete with under 18 being the only missing category.

The design makes it easy to pick off the most disaffected demographic segments (and the least, from the bottom) but these are disparate segments, possibly overlapping.

***

One challenge of this data is differentiating the two series of proportions. In this design, they use visual cues, like the height and width of the bars, colors, stacked vs not, data labels. Visual variety comes to the rescue.

Also note that the designer compensated for the lack of stacking on the left chart by printing data labels.

***

When reading this chart, I'm well aware that segments like urban residents, income more than $100K, at least college educated are overlapping, and it's hard to interpret the data the way it's been presented.

I wanted to place the different demographics into their natural groups, such as age, income, urbanicity, etc. Such a structure also surfaces demographic patterns, e.g. men are slightly more disaffected than women (not significant), people earning $100K+ are more unhappy than those earning $50K-.

Further, I'd like to make it easier to understand the importance factor - the share of GOP. Because the original form orders the demographics according to the left side, the proportions on the right side are jumbled.

Here is a draft of what I have in mind:

Redo_voxGOPresistance

The widths of the line segments show the importance of each demographic segment. The longest line segments are toward the bottom of the chart (< 40% likely to vote for Trump challenger).

 


McKinsey thinks the data world needs more dataviz talent

Note about last week: While not blogging, I delivered four lectures on three topics over five days: one on the use of data analytics in marketing for a marketing class at Temple; two on the interplay of analytics and data visualization, at Yeshiva and a JMP Webinar; and one on how to live during the Data Revolution at NYU.

This week, I'm back at blogging.

McKinsey publishes a report confirming what most of us already know or experience - the explosion of data jobs that just isn't stopping.

On page 5, it says something that is of interest to readers of this blog: "As data grows more complex, distilling it and bringing it to life through visualization is becoming critical to help make the results of data analyses digestible for decision makers. We estimate that demand for visualization grew roughly 50 percent annually from 2010 to 2015." (my bolding)

The report contains a number of unfortunate graphics. Here's one:

Mckinseyreport_pageiii

I applied my self-sufficiency test by removing the bottom row of data from the chart. Here is what happened to the second circle, representing the fraction of value realized by the U.S. health care industry.

Mckinseyreport_pageiii_inset

What does the visual say? This is one of the questions in the Trifecta Checkup. We see three categories of things that should add up to 100 percent. With a little more effort, we find the two colored categories are each 10% while the white area is 80%. 

But that's not what the data say, because there is only one thing being measured: how much of the potential has already been realized. The two colors is an attempt to visualize the uncertainty of the estimated proportion, which in this case is described as 10 to 20 percent underneath the chart.

If we have to describe what the two colored sections represent: the dark green section is the lower bound of the estimate while the medium green section is the range of uncertainty. The edge between the two sections is the actual estimated proportion (assuming the uncertainty bound is symmetric around the estimate)!

A first attempt to fix this might be to use line segments instead of colored arcs. 

Redo_mckinseyreport_inset_jc_1

The middle diagram emphasizes the mid-point estimate while the right diagram, the range of estimates. Observe how differently these two diagrams appear from the original one shown on the left.

This design only works if the reader perceives the chart as a "racetrack" chart. You have to see the invisible vertical line at the top, which is the starting line, and measure how far around the track has the symbol gone. I have previously discussed why I don't like racetracks (for example, here and here).

***

Here is a sketch of another design:

Redo_mckinseyreport_jc_2

The center figure will have to be moved and changed to a different shape. This design conveys the sense of a goal (at 100%) and how far one is along the path. The uncertainty is represented by wave-like elements that make the exact location of the pointer arrow appear as wavering.

 

 

 

 


Plotted performance guaranteed not to predict future performance

On my flight back from Lyon, I picked up a French magazine, and found the following chart:

French interest rates chart small

A quick visit to Bing Translate tells me that this chart illustrates the rates of return of different types of investments. The headline supposedly says "Only the risk pays". In many investment brochures, after presenting some glaringly optimistic projections of future returns, the vendor legally protects itself by proclaiming "Past performance does not guarantee future performance."

For this chart, an appropriate warning is PLOTTED PERFORMANCE GUARANTEED NOT TO PREDICT THE FUTURE!

***

Two unusual decisions set this chart apart:

1. The tree ring imagery, which codes the data in the widths of concentric rings around a common core

2. The placement of larger numbers toward the middle, and smaller numbers in the periphery.

When a reader takes in the visual design of this chart, what is s/he drawn to?

The designer evidently hopes the reader will focus on comparing the widths of the rings (A), while ignoring the areas or the circumferences. I think it is more likely that the reader will see one of the following:

(B) the relative areas of the tree rings

(C) the areas of the full circles bounded by the circumferences

(D) the lengths of the outer rings

(E) the lengths of the inner rings

(F) the lengths of the "middle" rings (defined as the average of the outer and inner rings)

Here is a visualization of six ways to "see" what is on the French rates of return chart:

Redo_jc_frenchinterestrates_1

Recall the Trifecta Checkup (link). This is an example where "What does the visual say" and "What does the data say" may be at variance. In case (A), if the reader is seeing the ring widths, then those two aspects are in sync. In every other case, the two aspects are disconcordant. 

The level of distortion is visualized in the following chart:

Redo_jc_frenchinterestrates_2

Here, I normalized everything to the size of the SCPI data. The true data is presented by the ring width column, represented by the vertical stripes on the left. If the comparisons are not distorted, the other symbols should stay close to the vertical stripes. One notices there is always distortion in cases (B)-(F). This is primarily due to the placement of the large numbers near the center and the small numbers near the edge. In other words, the radius is inversely proportional to the data!

 The amount of distortion for most cases ranges from 2 to 6 times. 

While the "ring area" (B) version is least distorted on average, it is perhaps the worst of the six representations. The level of distortion is not a regular function of the size of the data. The "sicav monetaries" (smallest data) is the least distorted while the data of medium value are the most distorted.

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To improve this chart, take a hint from the headline. Someone recognizes that there is a tradeoff between risk and return. The data series shown, which is an annualized return, only paints the return part of the relationship.