Tag Archives: graphs

Graphic options for illustrating where Americans moved during COVID-19

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I appreciate the effort at CityLab to take all of the data regarding where Americans moved during the COVID-19 pandemic and put it into graphs and charts. Good graphs and charts should help illustrate relationships between variables and help readers see patterns. Here are several choices that I thought succeeded.

First, start with patterns in metro areas across the United States.

The two colors plus the size of the circle show the percentage change in population. The percentage is a nice touch yet the comparison to the previous year might slip past some viewers.

Second, another way to look at metro areas on the whole regarding population changes.

The side-by-side of central cities and suburbs quickly shows several differences: lower ratios for cities, more variability among suburban counties, more losses for cities during COVID. The patterns among suburban counties are a little hard to pick up; there are a number of counties that lost people even as the general trend might have been up.

Third, where did all those people moving from New York City, specifically Manhattan go?

In absolute numbers, there are patterns this map displays nicely: a lot of moves in New York City and in the region plus moves to other metro areas (including Miami, Los Angeles, Chicago, and more). The inset of the Southwest at the bottom left is a nice touch…presumably New Yorkers did not move in large numbers to anywhere roughly between Nashville and Seattle.

Fourth, which New Yorkers moved?

Looking at zip codes, neighborhoods with higher incomes had more people moving while the numerous neighborhoods with lower incomes had smaller changes in inflow.

All together, this is more than just a series of pretty graphics. These choices – first about what data to use and second about how to present one variable in light of another – help clarify what happened in the last year. Each choice could have been a little different; emphasize a different part of the data or another variable, choose another graphic option. Yet, while there is certainly more to untangle about mobility, cities and suburbs, and COVID-19, these images help us start making sense of complex phenomena.

Searching for well-presented weather forecasts; example of Weather Underground

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Finding good and well-presented weather forecasts can be difficult. What provider supplies accurate information? Use a widget, app, or website? And who puts it together best on the screen? I have settled on Weather Underground because they present data in this format when you look at the ten day forecast:

This is a lot of information in one graphic. Here is what I think it does well:

  1. The top still provides the basic information many people might seek: conditions and high and low temperatures. A quick scan of the top quickly reveals all of this information.
  2. The amount of information available each day is helpfully shown in four sets of graphs below the header information. I do not just get a high and low temperature; I can see this over an hourly chart (no need to click on hourly information). I do not just get a notice about precipitation; I can see when rain or snow will fall. I do not just get a summary of wind speed; I can see if that wind speed is consistent, when it is rising or falling, and the direction.
  3. Connected to #2, it is easy to see patterns across days. Will that rain continue into the next day? Is the temperature spike or drop going to last? The longitudinal predictions are easy to see and I can see more details than just the summary info at the top.
  4. Also connected to #2, I can see how these four different paths of data line up with each other at the same days and times.

In sum, I think Weather Underground does a great job of showing a lot of information in an easy-to-decipher format. This may be too much information for many people, especially if you want quick information for now or the next few hours. But, if you want to think about the next few days and upcoming patterns, this one graphic offers a lot.

A Patrick Mahomes word cloud, strengths and weaknesses

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The season-opening NFL broadcast included a word cloud of descriptions of Chief’s quarterback Patrick Mahomes from his teammates:

On the broadcast, they noted that “leader” was mentioned the most times and several people mentioned “smart” and “competitive.” And, since this came right after a conversation of Mahomes’ record contract, it was noted that no teammate said “rich.”

A few thoughts on this graphic:

  1. It highlights the popularity and/or spread of word clouds. If it makes it to a football broadcast, it is all throughout the United States.
  2. It remains a way to highlight words or themes across a series of interviews or texts. It can take time to relay thoughts from multiple interactions; the word cloud tries to summarize the concepts. But…
  3. The size of the words do not easily convey their frequency in this particular graphic. Leader is clearly the biggest, competitive and smart are somewhere in the middle, and then there are a lot of other words. Yet, the length of certain words – “courageous” or “extraordinary” – take up a lot of space even if they were just mentioned once.
  4. The colors of the word cloud are tied to the Chiefs’ colors. But with the background changing a bit behind the words (“add a dynamic background to that boring word cloud!”), it can be hard to read some of the words in red (see “smart” above).
  5. Without knowing the number of interviews or how many total descriptors were given, it is hard to know how many words stand out.

An interesting choice of graphic and still some work to do to make this even a better presentation of data.

Reminder: do not get carried away making fancy charts and graphs

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The Brewster Rockit: Space Guy! comic strip from last Sunday makes an important point about designing charts and graphs: don’t get carried away.

https://www.gocomics.com/brewsterrockit/2020/05/03

Brewster Rockit May 3, 2020

The goal of using a chart or graph is to distill the information behind it into an easy-to-read format for making a quick point. A reader’s eye is drawn to a chart or graph and it should be easy to figure out the point the graphic is making.

If the graph or chart is too complicated, it loses its potency. If it looks great or clever but cannot help the reader interpret the data correctly, it is not very useful. If the researcher spends a lot of time tweaking the graphic to really make it eye-popping, it may not be worth it compared to simply getting the point across.

In sum: graphs and charts can be fun. They can break up long text and data tables. They can focus attention on an important data point or relationship. At the same time, they can get too complicated and become a time suck both for the producer of the graphic and those trying to figure them out.

Changing the Y-axis scale across graphs – to good effect

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In a look at COVID-19 cases across countries, the New York Times changed the Y-axis on the different graphs:

COVID19CurvesAcrossCountries

Typically, readers of graphs should beware when someone changes the scale on the Y-axis; this leads to issues when interpreting the data and can make it look like trends are present when they are not. See two earlier posts – misleading charts of 2015, State of the Union data presented in 2013 – for examples.

But, in this case, adjusting the scale makes some sense. The goal is to show exponential curves, the type of change when a disease spreads throughout a population, and then hopefully a peak and decline on the right side. Some countries have very few cases – such as toward the bottom like in Morocco or Hungary or Mexico – and some have many more – like Italy or South Korea – but the general shape can be similar. Once the rise starts, it is expected to continue until something stops it. And the pattern can look similar across countries.

Also, it is helpful that the creators of this point out at the top that “Scales are adjusted in each country to make the curve more readable.” It is not always reported when Y-axes are altered – and this lack of communication could be intentional – and then readers might not pick up on the issue.

A (real) pie chart to effectively illustrate wealth inequality

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Pie graphs can be great at showing relative differences between a small number of categories. A recent example of this comes from CBS:

CBS This Morning co-host Tony Dokoupil set up a table at a mall in West Nyack, New York, with a pie that represented $98 trillion of household wealth in the United States. The pie was sliced into 10 pieces and Dokoupil asked people to divide up those pieces onto five plates representing the poorest, the lower middle class, middle class, upper middle class, and wealthiest Americans. No one got it right. And, in fact, no one was even kind of close to estimating the real ratio, which involves giving nine pieces to the top 20 percent of Americans while the upper middle class and the middle class share one piece between the two of them. The lower middle class would effectively get crumbs considering they only have 0.3 percent of the pie. What about the poorest Americans? They wouldn’t get any pie at all, and in fact would get a bill, considering they are, on average, around $6,000 in debt…

To illustrate just how concentrated wealth is in the country, Dokoupil went on to note that if just the top 1 percent are taken into account, they would get four of the nine pieces of pie that go to the wealthiest Americans.

A pie chart sounds like a great device for this situation because of several features of the data and the presentation:

1. There are five categories of social class. Not too many for a pie chart.

2. One of those categories, the top 20 of Americans, clearly has a bigger portion of the pie than the other groups. A pie chart is well-suited to show one dominant category compared to the others.

3. Visitors to a shopping mall can easily understand a pie chart. They understand how it works and what it says (particularly with #1 and #2 above).

Together, a pie chart works in ways that other graphs and charts would not.

(Side note: it is hard to know whether the use of food in the pie chart helped or hurt the presentation. Do people work better with data when feeling hungry?)

Graphing changing household arrangements from 1960 to 2017

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An article discussing changes in American household arrangements includes this graph:

HouseholdArrangements1960to2017

A summary of the data:

It all represents an increasing distance from the nuclear-family structure considered traditional for decades. The changes solidify shifts that have been mounting since then, erasing the notion of one dominant family type. In the early 1960s, two-thirds of children were raised in male-breadwinner, married-couple families. By contrast, today there is no one family-and-work arrangement that encompasses the majority of children, demographers say.

“That dominant model declined, but it’s not like it was replaced by one thing,” says Philip Cohen, professor of sociology at the University of Maryland. “It was replaced by a peacock’s tail, a plethora of different arrangements.”

The graph is most effective at showing the biggest change: the decline of the “mother-father married, father only earner” group over nearly six decades. Two other categories have significant increases – married and dual earners, single mother – while the five categories at the bottom involve relatively fewer households.

The graph is unusually skinny from left to right and this helps emphasize the straight lines up or down over time. Would a wider x-axis show some more variation over time or are the trends always pretty consistent?

The colors are a little hard to distinguish. I am not usually in favor of dotted lines and so on but this might be an opportunity to differentiate between trend lines.

Just thinking about other graph options, a pie chart for each time period might also communicate the big change well (though the smaller categories might not show up as well) or a clustered bar graph with the two years side to side could show the relative changes for each group.

In sum, graphing significant social change is not necessarily easy and this format clearly communicates a big change.

 

Summarizing data visualization errors

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Check out this good quick overview of visualization errors – here are a few good moments:

Everything is relative. You can’t say a town is more dangerous than another because the first one had two robberies and the other only had one. What if the first town has 1,000 times the population that of the first? It is often more useful to think in terms of percentages and rates rather than absolutes and totals…

It’s easy to cherrypick dates and timeframes to fit a specific narrative. So consider history, what usually happens, and proper baselines to compare against…

When you see a three-dimensional chart that is three dimensions for no good reason, question the data, the chart, the maker, and everything based on the chart.

In summary: data visualizations can be very useful for highlighting a particular pattern but they can also be altered to advance an incorrect point. I always wonder with these examples of misleading visualizations whether the maker intentionally made the change to advance their point or whether there was a lack of knowledge about how to do good data analysis. Of course, this issue could arise with any data analysis as there are right and wrong ways to interpret and present data.

“The most misleading charts of 2015, fixed”

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Here are some improved charts first put forward by politicians, advocacy groups, and the media in 2015.

I’m not sure exactly how they picked “the most misleading charts” (is there bias in this selection?) but it is interesting that several involve a misleading y-axis. I’m not sure that I would count the last example as a misleading chart since it involves a definition issue before getting to the chart.

And what is the purpose of the original, poorly done graphics? Changing the presentation of the data provides evidence for a particular viewpoint. Change the graphic depiction of the data and another story could be told. Unfortunately, it is actions like these that tend to cast doubt on the use of data for making public arguments – the data is simply too easy to manipulate so why rely on data at all? Of course, that assumes people look closely at the chart and the data source and know what questions to ask…

“New Apps Instantly Convert Spreadsheets Into Something Actually Readable”

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Several new apps transform spreadsheet data into a chart or graph without having to spend much or any time with the raw data:

It’s called Project Elastic, and he unveiled the thing this fall at a conference run by his company, Tableau. The Seattle-based company has been massively successful selling software that helps big businesses “visualize” the massive amount of online data they generate—transform all those words and numbers into charts and graphics their data scientists can more readily digest—but Project Elastic is something different. It’s not meant for big businesses. It’s meant for everyone.

The idea is that, when someone emails a spreadsheet to your iPad, the app will open it up—but not as a series of rows and columns. It will open the thing as chart or graph, and with a swipe of the finger, you can reformat the data into a new chart or graph. The hope is that this will make is easier for anyone to read a digital spreadsheet—an age-old computer creation that’s still looks like Greek to so many people. “We think that seeing and understanding your data is a human right,” says Story, the Tableau vice president in charge of the project.

And Story isn’t the only one. A startup called ChartCube has developed a similar tool that can turn raw data into easy-to-understand charts and graphs, and just this week, the new-age publishing outfit Medium released a tool called Charted that can visualize data in similar ways. So many companies aim to democratize access to online data, but for all the different data analysis tool out on the market, this is still the domain of experts—people schooled in the art of data analysis. These projects aim to put the democracy in democratize.

Two quick thoughts:

1. I understand the impulse to create charts and graphs that communicate patterns. Yet, such devices are not infallible in themselves. I would suggest we need more education in interpreting and using the information from infographics. Additionally, this might be a temporary solution but wouldn’t it be better in the long run if more people know how to read and use a spreadsheet?

2. Interesting quote: “We think that seeing and understanding your data is a human right.” I have a right to data or to the graphing and charting of my data? This adds to a collection of voices arguing for a human right to information and data.