## The most dangerous day

##### Aug 16, 2024

Our World in Data published this interesting chart about infant mortality in the U.S.

The article that sent me to this chart called the first day of life the "most dangerous day". This dot plot seems to support the notion, as the "per-day" death rate is the highest on the day of birth, and then drops quite fast (note log scale) over the the first year of life.

***

Based on the same dataset, I created the following different view of the data, using the same dot plot form:

By this measure, a baby has 99.63% chance of surviving the first 30 days while the survival rate drops to 99.5% by day 180.

There is an important distinction between these two metrics.

The "per day" death rate is the chance of dying on a given day, conditional on having survived up to that day. The chance of dying on day 2 is lower partly because some of the more vulnerable ones have died on day 1 or day 0,  etc.

The survival rate metric is cumulative: it measures how many babies are still alive given they were born on day 0. The survival rate can never go up, so long as we can't bring back the dead.

***

If we are assessing a 5-day-old baby's chance of surviving day 6, the "per-day" death rate is relevant since that baby has not died in the first 5 days.

If the baby has just been born, and we want to know the chance it might die in the first five days (or survive beyond day 5), then the cumulative survival rate curve is the answer. If we use the per-day death rate, we can't add the first five "per-day" death rates It's a more complicated calculation of dying on day 0, then having not died on day 0, dying on day 1, then having not died on day 0 or day 1, dying on day 2, etc.

## Expert handling of multiple dimensions of data

##### Jul 17, 2024

I enjoyed reading this Washington Post article about immigration in America. It features a number of graphics. Here's one graphic I particularly like:

This is a small multiples of six maps, showing the spatial distribution of immigrants from different countries. The maps reveal some interesting patterns: Los Angeles is a big favorite of Guatamalans while Houston is preferred by Hondurans. Venezuelans like Salt Lake City and Denver (where there are also some Colombians and Mexicans). The breadth of the spatial distribution surprises me.

The dataset behind this graphic is complex. It's got country of origin, place of settlement, and time of arrival. The maps above collapsed the time dimension, while drawing attention to the other two dimensions.

***

They have another set of charts that highlight the time dimension while collapsing the place of settlement dimension. Here's one view of it:

There are various names for this chart form. Stream river is one. I like to call it "inkblot", where the two sides are symmetric around the middle vertical line. The chart shows that "migrants in the U.S. immigration court" system have grown substantially since the end of the Covid-19 pandemic, during which they stopped coming.

I'm not a fan of the inkblot. One reason is visible in the following view, which showcases three Central American countries.

The main message is clear enough. The volume of immigrants from these three countries have been relatively stable over the last decade, with a bulge in the late 2000s. The recent spurt in migrants have come from other places.

But try figuring out what proportion of total immigration is accounted for by these three countries say in 2024. It's a task that is tougher than it should be, and the culprit is that the "other countries" category has been split in half with the two halves separated.

## When should we use bar charts?

##### Jul 03, 2024

Two innocent looking column charts.

These came from an article in Significance magazine (link to paywall) that applies the "difference-in-difference" technique to analyze whether the superstitious act of skipping the number 13 when numbering floors in tall buildings causes an inflation of condo pricing.

The study authors are quite careful in their analysis, recognizing that building managers who decide to relabel the 13th floor as 14th may differ in other systematic ways from those who don't relabel. They use a matching technique to construct comparison groups. The left-side chart shows one effect of matching buildings, which narrowed the gap in average square footage between the relabeled and non-relabeled groups. (Any such gap suggests potential confounding; in a hypothetical, randomized experiment, the average square footage of both groups should be statistically identical.)

The left-side chart features columns that don't start as zero, thus the visualization exaggerates the differences. The degree of exaggeration here is tame: about 150 got chopped off at the bottom, which is about 10% of the total height. But why?

***

The right-side chart is even more problematic.

This chart shows the effect of matching buildings on the average age of the buildings (measured using the average construction year). Again, the columns don't start at zero. But for this dataset, zero is a meaningless value. Never make a column chart when the zero level has no meaning!

The story is simple: by matching, the average construction year in the relabeled group was brought closer to that in the non-relabeled group. The construction year is an ordinal categorical variable, with integer values. I think a comparison of two histograms will show the message clearer, and also provide more information than jut the two average values.

## Prime visual story-telling

##### May 23, 2024

A story from the New York Times about New York City neighborhoods has been making the rounds on my Linkedin feed. The Linkedin post sends me to this interactive data visualization page (link).

Here, you will find a multi-colored map.

The colors show the extant of named neighborhoods in the city. If you look closely, the boundaries between neighborhoods are blurred since it's often not clear where one neighborhood ends and where another one begins. I was expecting this effect when I recognize the names of the authors, who have previously published other maps that obsess over spatial uncertainty.

I clicked on an area for which I know there may be differing opinions:

There was less controversy than I expected.

***

What was the dataset behind this dataviz project? How did they get such detailed data on every block of the city? Wouldn't they have to interview a lot of residents to compile the data?

I'm quite impressed with what they did. They put up a very simple survey (emphasis on: very simple). This survey is only possible with modern browser technology. It asks the respondent to pinpoint the location of where they live, and name their neighborhood. Then it asks the respondent to draw a polygon around their residence to include the extant of the named neighborhood. This consists of a few simple mouse clicks on the map that shows the road network. Finally, the survey collects optional information on alternative names for the neighborhood, etc.

When they process the data, they assign the respondent's neighborhood name to all blocks encircled by the polygon. This creates a lot of data in a few brush strokes, so to speak. This is a small (worthwhile) tradeoff even though the respondent didn't really give an answer for every block.

***

Bear with me, I'm getting to the gist of this blog post. The major achievement isn't the page that was linked to above. The best thing the dataviz team did here is the visual story that walks the reader through insights drawn from the dataviz. You can find the visual story here.

What are the components of a hugely impressive visual story?

• It combines data visualization with old-fashioned archival research. The historical tidbits add a lot of depth to the story.
• It combines data visualization with old-fashioned reporting. The quotations add context to how people think about neighborhoods - something that cannot be obtained from the arms-length process of conducting an online survey.
• It highlights curated insights from the underlying data - even walking the reader step by step through the relevant sections of the dataviz that illustrate these insights.

At the end of this story, some fraction of users may be tempted to go back to the interactive dataviz to search for other insights, or obtain answers to their personalized questions. They are much better prepared to do so, having just seen how to use the interactive tool!

***

The part of the visual story I like best is toward the end. Instead of plotting all the data on the map, they practice some restraint, and filter the data. They show the boundaries that have reached at least a certain level of consensus among the respondents.

The following screenshot shows those areas for which at least 90% agree.

Pardon the white text box, I wasn't able to remove it.

***

One last thing...

Every time an analyst touches data, or does something with data, s/he imposes assumptions, and sometimes, these assumptions are so subtle that even the analyst may not have noticed. Frequently, these assumptions are baked into the analytical "models," which is why they may fall through the cracks.

One such assumption in making this map is that every block in the city belongs to at least one named neighborhood. An alternative assumption is that neighborhoods are named only because certain blocks have things in common, and because these naming events occur spontaneously, it's perfectly ok to have blocks that aren't part of any named neighborhood.

## Chart without an axis

##### May 13, 2024

When it comes to global warming, most reports cite a single number such as an average temperature rise of Y degrees by year X. Most reports also claim the existence of a consensus within scientists. The Guardian presented the following chart that shows the spread of opinions amongst the experts.

Experts were asked how many degrees they expect average global temperature to increase by 2100. The estimates ranged from "below 1.5 degrees" to "5 degrees or more". The most popular answer was 2.5 degrees. Roughly three out of four respondents picked a number at 2.5 degrees or above. The distribution is close to symmetric around the middle.

***

What kind of chart is this?

It's a type of histogram, given that the horizontal axis shows binned ranges of temperature change while the vertical axis shows number of respondents (out of 380).

A (count) histogram typically encodes the count data in the vertical axis. Did you notice there isn't a vertical axis?

That's because the chart has an abnormal axis. Each of the 380 respondents is shown here as a cell. What looks like a "column" is actually two-dimensional. Each row of cells has 10 slots. To find out how many respondents chose the 2.5 celsius category, you count the number of rows and then the number of stray items on top. (It's 132.)

Only the top row of cells can be partially filled so the general shape of the distribution isn't affected much. However, the lack of axis labels makes it hard to learn the count of each column.

It's even harder to know the proportions of respondents, which should be the primary message of the chart. The proportion would have been possible to show if the maximum number of rows was set to 38. The maximum number of rows on the above chart is 22. Using 38 rows leads to a chart with a lot of white space as the tallest column (count of 132) is roughly 35% of the total response.

At the end, I'm not sure this variant of histogram beats the standard histogram.

## Aligning V and Q by way of D

##### Apr 08, 2024

In the Trifecta Checkup (link), there is a green arrow between the Q (question) and V (visual) corners, indicating that they should align. This post illustrates what I mean by that.

I saw the following chart in a Washington Post article comparing dairy milk and plant-based "milks".

The article contains a whole series of charts. The one shown here focuses on vitamins.

The red color screams at the reader. At first, it appears to suggest that dairy milk is a standout on all four categories of vitamins. But that's not what the data say.

Let's take a look at the chart form: it's a grid of four plots, each containing one square for each of four types of "milk". The data are encoded in the areas of the squares. The red and green colors represent category labels and do not reflect data values.

Whenever we make bubble plots (the closest relative of these square plots), we have to solve a scale problem. What is the relationship between the scales of the four plots?

I noticed the largest square is the same size across all four plots. So, the size of each square is made relative to the maximum value in each plot, which is assigned a fixed size. In effect, the data encoding scheme is that the areas of the squares show the index values relative to the group maximum of each vitamin category. So, soy milk has 72% as much potassium as dairy milk while oat and almond milks have roughly 45% as much as dairy.

The same encoding scheme is applied also to riboflavin. Oat milk has the most riboflavin, so its square is the largest. Soy milk is 80% of oat, while dairy has 60% of oat.

***

Let's step back to the Trifecta Checkup (link). What's the question being asked in this chart? We're interested in the amount of vitamins found in plant-based milk relative to dairy milk. We're less interested in which type of "milk" has the highest amount of a particular vitamin.

Thus, I'd prefer the indexing tied to the amount found in dairy milk, rather than the maximum value in each category. The following set of column charts show this encoding:

I changed the color coding so that blue columns represent higher amounts than dairy while yellow represent lower.

From the column chart, we find that plant-based "milks" contain significantly less potassium and phosphorus than dairy milk while oat and soy "milks" contain more riboflavin than dairy. Almond "milk" has negligible amounts of riboflavin and phosphorus. There is vritually no difference between the four "milk" types in providing vitamin D.

***

In the above redo, I strengthen the alignment of the Q and V corners. This is accomplished by making a stop at the D corner: I change how the raw data are transformed into index values.

Just for comparison, if I only change the indexing strategy but retain the square plot chart form, the revised chart looks like this:

The four squares showing dairy on this version have the same size. Readers can evaluate the relative sizes of the other "milk" types.

## Reading log: HBR's specialty bar charts

##### Mar 28, 2024

Today, I want to talk about a type of analysis that I used to ask students to do. I'm calling it a reading log analysis – it's a reading report that traces how one consumes a dataviz work from where your eyes first land to the moment of full comprehension (or abandonment, if that is the outcome). Usually, we do this orally during a live session, but it's difficult to arrive at a full report within the limited class time. A written report overcomes this problem. A stack of reading logs should be a gift to any chart designer.

My report below is very detailed, reflecting the amount of attention I pay to the craft. Most readers won't spend as much time consuming a graphic. The value of the report is not only in what it covers but also in what it does not mention.

***

The chart being analyzed showed up in a Harvard Business Review article (link), and it was submitted by longtime reader Howie H.

First and foremost, I recognized the chart form as a bar chart. It's an advanced bar chart in which each bar has stacked sections and a vertical line in the middle. Now, I wanted to figure out how data enter the picture.

My eyes went to the top legend which tells me the author was comparing the proportion of respondents who said "business should take responsibility" to the proportion who rated "business is doing well". The difference in proportions is called the "performance gap". I glanced quickly at the first row label to discover the underlying survey addresses social issues such as environmental concerns.

Next, I looked at the first bar, trying to figure out its data encoding scheme. The bold, blue vertical line in the middle of the bar caused me to think each bar is split into left and right sections. The right section is shaded and labeled with the performance gap numbers so I focused on the segment to the left of the blue line.

My head started to hurt a little. The green number (76%) is associated with the left edge of the left section of the bar. And if the blue line represents the other number (29%), then the width of the left section should map to the performance gap. This interpretation was obviously incorrect since the right section already showed the gap, and the width of the left section was not equal to that of the right shaded section.

I jumped to the next row. My head hurt a little bit more. The only difference between the two rows is the green number being 74%, 2 percent smaller. I couldn't explain how the left sections of both bars have the same width, which confirms that the left section doesn't display the performance gap (assuming that no graphical mistakes have been made). It also appeared that the left edge of the bar was unrelated to the green number. So I retreated to square one. Let's start over. How were the data encoded in this bar chart?

I scrolled down to the next figure, which applies the same chart form to other data.

I became even more confused. The first row showed labels (green number 60%, blue number 44%, performance gap -16%). This bar is much bigger than the one in the previous figure, even though 60% was less than 76%. Besides, the left section, which is bracketed by the green number on the left and the blue number on the right, appeared much wider than the 16% difference that would have been merited. I again lapsed into thinking that the left section represents performance gaps.

Then I noticed that the vertical blue lines were roughly in proportion. Soon, I realized that the total bar width (both sections) maps to the green number. Now back to the first figure. The proportion of respondents who believe business should take responsibility (green number) is encoded in the full bar. In other words, the left edges of all the bars represent 0%. Meanwhile the proportion saying business is doing well is encoded in the left section. Thus, the difference between the full width and the left-section width is both the right-section width and the performance gap.

Here is an edited version that clarifies the encoding scheme:

***

That's my reading log. Howie gave me his take:

I had to interrupt my reading of the article for quite a while to puzzle this one out. It's sorted by performance gap, and I'm sure there's a better way to display that. Maybe a dot plot, similar to here - https://junkcharts.typepad.com/junk_charts/2023/12/the-efficiency-of-visual-communications.html.

A dot plot might look something like this:

Howie also said:

I interpret the authros' gist to be something like "Companies underperform public expectations on a wide range of social challenges" so I think I'd want to focus on the uniform direction and breadth of the performance gap more than the specifics of each line item.

And I agree.

## The curse of dimensions

##### Mar 20, 2024

Usually the curse of dimensions concerns data with many dimensions. But today I want to talk about a different kind of curse. This is the curse of dimensions in mapping.

We are only talking about a few dimensions, typically between 3 and 6, so small number of dimensions. And yet it's already a curse. Maps are typically drawn in two dimensions. Those two dimensions are usually spoken for: they show the x- and y-coordinate of space. If we want to include a third, fourth or fifth dimension of data on the map, we have to appeal to colors, shapes, and so on. Cartographers have long realized that adding dimensions involves tradeoffs.

***

Andrew featured some colored bubble maps in a recent post. Here is one example:

The above map shows the proportion of population in each U.S. county that is Hispanic. Each county is represented by a bubble pinned to the centroid of the county. The color of the bubble shows the data, divided into demi-deciles so they are using a equal-width binning method. The size of a bubble indicates the size of a county.

The map is sometimes called a "Dorling map" after its presumptive original designer.

I'm going to use this map to explore the curse of dimensions.

***

It's clear from the design that county-level details are regarded as extremely important. As there are about 3,000 counties in the U.S., I don't see how any visual design can satisfy this requirement without giving up clarity.

More details require more objects, which spread readers' attention. More details contain more stories, but that too dilutes their focus.

Another principle of this map is to not allow bubbles to overlap. Of course, having bubbles overlap or print on top of one another is a visual faux pas. But to prevent such behavior on this particular design means the precise locations are sacrificed. Consider the eastern seaboard where there are densely populated counties: they are not pinned to their centroids. Instead, the counties are pushed out of their normal positions, similar to making a cartogram.

I remarked at the start – erroneously but deliberately – that each bubble is centered at the centroid of each county. I wonder how many of you noticed the inaccuracy of that statement. If that rule were followed, then the bubbles in New England would have overlapped and overprinted.

This tradeoff affects how we perceive regional patterns, as all the densely populated regions are bent out of shape.

Another aspect of the data that the designer treats as important is county population, or rather relative county population. Relative – because bubble size don't portray absolutes, plus the designer didn't bother to provide a legend to decipher bubble sizes.

The tradeoff is location. The varying bubble sizes, coupled with the previous stipulation of no overlapping, push bubbles from their proper centroids. This forced displacement disproportionately affects larger counties.

***

What if we are willing to sacrifice county-level details?

In this setting, we are not obliged to show every single county. One alternative is to perform spatial smoothing. Intuitively, think about the following steps: plot all these bubbles in their precise locations, turn the colors slightly transparent, let them overlap, blend away the edges, and then we have a nice picture of where the Hispanic people are located.

I have sacrificed the county-level details but the regional pattern becomes much clearer, and we don't need to deviate from the well-understood shape of the standard map.

This version reminds me of the language maps that Josh Katz made.

Here is an old post about these maps.

This map design only reduces but does not eliminate the geographical inaccuracy. It uses the same trick as the Dorling map: the "vertical" density of population has been turned into "horizontal" span. It's a bit better because the centroids are not displaced.

***

Which map is better depends on what tradeoffs one is making. In the above example, I'd have made different choices.

One final thing – it's minor but maybe not so minor. Most of the bubbles on the map especially in the middle are tiny; as most of them have Hispanic proportions that are on the left side of the scale, they should be showing light orange. However, all of them appear darker than they ought to be. That's because each bubble has a dark border. For small bubbles, the ratio of ink on the border is a high proportion of the ink for the entire object.

## Do you want a taste of the new hurricane cone?

##### Mar 05, 2024

The National Hurricane Center (NHC) put out a press release (link to PDF) to announce upcoming changes (in August 2024) to their "hurricane cone" map. This news was picked up by Miami Herald (link).

The above example is what the map looks like. (The data are probably fake since the new map is not yet implemented.)

The cone map has been a focus of research because experts like Alberto Cairo have been highly critical of its potential to mislead. Unfortunately, the more attention paid to it, the more complicated the map has become.

The bottom layer is the so-called "cone". This is the white patch labeled below as the "potential track area (day 1-5)".  Researchers dislike this element because they say readers tend to misinterpret the cone as predicting which areas would be damaged by hurricane winds when the cone is intended to depict the uncertainty about the path of the hurricane. Prior criticism has led the NHC to add the text at the top of the chart, saying "The cone contains the probable path of the storm center but does not show the size of the storm. Hazardous conditions can occur outside of the cone."

The middle layer are the multi-colored bits. Two of these show the areas for which the NHC has issued "watches" and "warnings". All of these color categories represent wind speeds at different times. Watches and warnings are forecasts while the other colors indicate "current" wind speeds.

The top layer consists of black dots. These provide a single forecast of the most likely position of the storm, with the S, H, M labels indicating the most likely range of wind speeds at forecast times.

***

Let's compare the new cone map to a real hurricane map from 2020. (This older map came from a prior piece also by NHC.)

Can we spot the differences?

To my surprise, the differences were minor, in spite of the pre-announced changes.

The first difference is a simplification. Instead of dividing the white cone (the bottom layer) into two patches -- a white patch for days 1-3, and a dotted transparent patch for days 4-5, the new map aggregates the two periods. Visually, simplifying makes the map less busy but loses the implicit acknowledge found in the old map that forecasts further out are not as reliable.

The second point of departure is the addition of "inland" warnings and watches. Notice how the red and blue areas on the old map hugged the coastline while the red and blue areas on the new map reach inland.

Both changes push the bottom layer, i.e. the cone, deeper into the background. It's like a shrink-flation ice cream cone that has a tiny bit of ice cream stuffed deep in its base.

***

How might one improve the cone map? I'd start by dismantling the layers. The three layers present answers to different problems, albeit connected.

Let's begin with the hurricane forecasting problem. We have the current location of the storm, and current measurements of wind speeds around its center. As a first requirement, a forecasting model predicts the path of the storm in the near future. At any time, the storm isn't a point in space but a "cloud" around a center. The path of the storm traces how that cloud will move, including any expansion or contraction of its radius.

That's saying a lot. To start with, a forecasting model issues the predicted average path -- the expected path of the storm's center. This path is (not competently) indicated by the black dots in the top layer of the cone map. These dots offer only a sampled view of the average path.

Not surprisingly, there is quite a bit of uncertainty about the future path of any storm. Many models simulate future worlds, generating many predictions of the average paths. The envelope of the most probable set of paths is the "cone". The expanding width of the cone over time reflects the higher uncertainty of our predictions further into the future. Confusingly, this cone expansion does not depict spatial expansion of either the storm's size or the potential areas that may suffer the greatest damage. Both of those tend to shrink as hurricanes move inland.

Nevertheless, the cone and the black dots are connected. The path drawn out by the black dots should be the average path of the center of the storm.

The forecasting model also generates estimates of wind speeds. Those are given as labels inside the black dots. The cone itself offers no information about wind speeds. The map portrays the uncertainty of the position of the storm's center but omits the uncertainty of the projected wind speeds.

The middle layer of colored patches also inform readers about model projections - but in an interpreted manner. The colors portray hurricane warnings and watches for specific areas, which are based on projected wind speeds from the same forecasting models described above. The colors represent NHC's interpretation of these model outputs. Each warning or watch simultaneously uses information on location, wind speed and time. The uncertainty of the projected values is suppressed.

I think it's better to use two focused maps instead of having one that captures a bit of this and a bit of that.

One map can present the interpreted data, and show the areas that have current warnings and watches. This map is about projected wind strength in the next 1-3 days. It isn't about the center of the storm, or its projected path. Uncertainty can be added by varying the tint of the colors, reflecting the confidence of the model's prediction.

Another map can show the projected path of the center of the storm, plus the cone of uncertainty around that expected path. I'd like to bring more attention to the times of forecasting, perhaps shading the cone day by day, if the underlying model has this level of precision.

***

Back in 2019, I wrote a pretty long post about these cone maps. Well worth revisiting today!

## Lost in the middle class

##### Feb 20, 2024

Washington Post asks people what it means to be middle class in the U.S. (link; paywall)

The following graphic illustrates one type of definition, purely based on income ranges.

For me, this chart is more taxing to read than it appears.

It can be read column by column. Each column represents a hypotheticial annual income for a family of four. People are asked whether they consider that family lower/working class, middle class or upper class. Be careful as the increments from column to column are not uniform.

Now, what's the question again? We're primarily interested in what incomes constitute middle class.

So, we should be looking at the deep green blocks that hang in the middle of each column. It's not easy to read the proportion of middle blocks in a stacked column chart.

***

I tried separating out the three perceived income classes, using a small-multiples design.

One can more directly see what income ranges are most popularly perceived as being in each income class.

***

The article also goes into alternative definitions of middle class, using more qualitative metrics, such as "able to pay all bills on time without worry". That's a whole other post.