##### Nov 27, 2017

A reader sent this tip in some time ago and I lost track of who he/she is. This graphic looks deceptively complex.

What's complex is not the underlying analysis. The design is complex and so the decoding is complex.

The question of the graphic is a central concern of anyone who's retired: how long will one's savings last? There are two related metrics to describe the durability of the stash, and they are both present on this chart. The designer first presumes that one has saved \$1 million for retirement. Then he/she computes how many years the savings will last. That, of course, depends on the cost of living, which naively can be expressed as a projected annual expenditure. The designer allows the cost of living to vary by state, which is the main source of variability in the computations. The time-based and dollar-based metrics are directly linked to one another via a formula.

The design encodes the time metric in a grid of dots, and the dollar-metric in the color of the dots. The expenditures are divided into eight segments, given eight colors from deep blue to deep pink.

Thirteen of those dots are invariable, appearing in every state. Readers are drawn into a ranking of the states, which is nothing but a ranking of costs of living. (We don't know, but presume, that the cost of living computation is appropriate for retirees, and not averaged.) This order obscures any spatial correlation. There are a few production errors in the first row in which the year and month numbers are misstated slightly; the numbers should be monotonically decreasing. In terms of years and months, the difference between many states is immaterial. The pictogram format is more popular than it deserves: only highly motivated readers will count individual dots. If readers are merely reading the printed text, which contains all the data encoded in the dots, then the graphic has failed the self-sufficiency principle - the visual elements are not doing any work.

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In my version, I surface the spatial correlation using maps. The states are classified into sensible groups that allow a story to be told around the analysis. Three groups of states are identified and separately portrayed. The finer variations between states within each state group appear as shades.

Data visualization should make the underlying data easier to comprehend. It's a problem when the graphic is harder to decipher than the underlying dataset.

## Diverging paths for rich and poor, infographically

##### Nov 21, 2017

Ray Vella (link) asked me to comment on a chart about regional wealth distribution, which I wrote about here. He also asked students in his NYU infographics class to create their own versions.

This effort caught my eye:

This work is creative, and I like the concept of using two staircases to illustrate the diverging fortunes of the two groups. This is worlds away from the original Economist chart.

The infographic does have a serious problem. In one of my dataviz talks, I talk about three qualifications of work called "data visualization." The first qualification is that the data visualization has to display the data. This is an example of an infographic that is invariant to the data.

Is it possible to salvage the concept? I tried. Here is an idea:

I abandoned the time axis so the data plotted are only for 2015, and the countries are shown horizontally from most to least equal. I'm sure there are ways to do it even better.

Infographics can be done while respecting the data. Ray is one of the designers who appreciate this. And thanks Ray for letting me blog about this.

## Choosing the right metric reveals the story behind the subway mess in NYC

##### Nov 08, 2017

I forgot who sent this chart to me - it may have been a Twitter follower. The person complained that the following chart exaggerated how much trouble the New York mass transit system (MTA) has been facing in 2017, because of the choice of the vertical axis limits.

This chart is vintage Excel, using Excel defaults. I find this style ugly and uninviting. But the chart does contain some good analysis. The analyst made two smart moves: the chart controls for month-to-month seasonality by plotting the data for the same month over successive years; and the designation "12 month averages" really means moving averages with a window size of 12 months - this has the effect of smoothing out the short-term fluctuations to reveal the longer-term trend.

The red line is very alarming as it depicts a sustained negative trend over the entire year of 2017, even though the actual decline is a small percentage.

If this chart showed up on a business dashboard, the CEO would have been extremely unhappy. Slow but steady declines are the most difficult trends to deal with because it cannot be explained by one-time impacts. Until the analytics department figures out what the underlying cause is, it's very difficult to curtail, and with each monthly report, the sense of despair grows.

Because the base number of passengers in the New York transit system is so high, using percentages to think about the shift in volume underplays the message. It's better to use actual millions of passengers lost. That's what I did in my version of this chart:

The quantity depicted is the unexpected loss of revenue passengers, measured against a forecast. The forecast I used is the average of the past two years' passenger counts. Above the zero line means out-performing the forecast but of course, in this case, since October 2016, the performance has dipped ever farther below the forecast. By April, 2017, the gap has widened to over 5 million passengers. That's a lot of lost customers and lost revenues, regardless of percent!

The biggest headache is to investigate what is the cause of this decline. Most likely, it is a combination of factors.

## Upcoming talks here and there

##### Nov 07, 2017

I'm giving a dataviz talk in San Ramon, CA on Thursday Nov 9. Go here to register.

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Then next Monday (Nov 13, 11 am), I will be in Boston at Harvard Business Review, giving a "live whiteboard session" on A/B Testing. This talk will be streamed live on Facebook Live.

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Finally, my letter to the editor of New York Times Magazine was published this past Sunday. This letter is a response to Susan Dominus's article about the "power pose" research, and the replication crisis in social science. Fundamentally, it is a debate over how data is used and analyzed in experiments, and therefore relevant to my readers. I added a list of resources in this blog post about the letter.

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Those are some of my favorite topics: dataviz, A/B testing, and data-driven decision-making.