Monday I examined a chart from the BBC that in my mind needlessly added confusing visual components to what could have been a straight table. So here we take a look at some other options that could have been used to tell the same story. The first is the straight forward table approach. Here I emphasised the important number, that of those killed. I opted to de-emphasise the years and the injured in the table. Also, since the bulk of my audience is from the United States, I used the two-letter states codes.
But let us presume we want a graphic because everyone wants everything to be visual and graphic. Here are two different options. The first takes the table/graphic from the BBC and converts it into a straight stacked bar chart, again with emphasis on the dead. I consolidated the list into a single column so one need not split their reading across both the horizontal and vertical.
And then if you examine the dates, one can find an interesting component of the data. Of the top-eight shootings, all but two occurred within the last ten years. So the second version takes the graphic component of the stacked bars from the first and places them on a timeline.
For those that wonder about the additional effort needed to create three different options from one data set, I limited myself to an hour’s worth of time. A little bit of thought after examining the data set can save a lot of time when trying to design the data display.
Yesterday I opined about how simple tables can convey meaningful information without the aid of unnecessary chart elements. And while we will get back to that post, I did want to take a moment to share an older piece from the New York Times I recalled and that has been updated since Orlando.
The piece uses a table to compare the gun homicide rates for various countries and compares it to other causes of death. Being killed by a gun in the Netherlands is as likely as dying by accidental gas poisoning in the United States. It puts the absurdly high gun homicide rates in the United States in a new light.
Credit for the piece goes to Kevin Quealy and Margot Sanger-Katz.
I will be trying to do a longer piece on the data visualisations surrounding the shootings in Orlando later this week. But for starters, a simple point through this piece from the BBC—not that they are the only culprits of this. Not all data-driven stories need visualisations. Sometimes a nicely typeset table will do the job better and faster.
An actual table with typographic emphasis on the tables would have been better and clearer than this. Or with a little more time and effort—not that those always exist in a journalism organisation—something more appropriate to the type of data could have been designed.
Credit for the piece goes the BBC graphics department.
Not every graphic information graphic is a sexy chart or map. Sometimes tables communicate the story just as well. Maybe even better. Today’s post comes from FiveThirtyEight, which examined a claim about what places represent “Normal America”. Turns out that when one looks at the data, here age, race, ethnicity, and education, Normal America is found in the eastern half of the country. And it includes some big cities, notably both Philadelphia and Chicago. The whole article is worth a read, as it goes on exploring states representing Normal America and then places that represent 1950s America.
So where is Normal America? New Haven, Connecticut.
Today we have a really interesting piece from the New York Times. In terms of visualisations, we see nothing special nor revolutionary—that is not to say it is not well done. The screenshot below is from the selection of my hometown county, Chester County in Pennsylvania. Where the piece really shines is when you begin looking at different counties. The text of the article appears to be tailored to fit different counties. But with so many counties in the country, clearly it is being done programmatically. You can begin to see where it falls apart when you select rather remote counties out west.
But it does not stop simply with location. Try using the controls in the upper right to compare genders or income quartiles. The text changes for those as well.
Credit for the piece goes to Gregor Aisch, Eric Buth, Matthew Bloch, Amanda Cox, and Kevin Quealy.
As Massachusetts and Maine celebrate Patriots’ Day, the Boston Red Sox are set to play their earliest game of the year with an 11.00 start time. (Yes, there is also a marathon today.) So after two weeks or twelve games, the question people want answered is what Red Sox do we get this year? FiveThirtyEight looked at what they called roller-coaster seasons of late, primarily using a box plot graphic to show just how much whiplash Boston fans have endured of late.
So who are the Red Sox this year? The cellar dwellers of 2012 and 2014? Or world champions like in 2013? Who knows?
Or so says Adweek. I would heartily disagree about their inclusion of Yuengling in their group of crappy. Though the other nineteen, yeah, I would tend to agree. Regardless, the infographic that sparked the Adweek post is quite blah. I do enjoy the illustrations of the bottles and labels, but the data visualisation below is weak.
So because of Yuengling, I decided to take a quick stab at ways to improve it. My first finding in the data was that the different brands were assigned a Beer Advocate rating, and Yuengling rated the highest—though not terribly high overall. Still, unless you are looking to get drunk, it does offer a good taste/cost value among the consideration set.
In my office, Chipotle is a popular fast-casual lunch choice. I am not sure, however, whether people would want to see today’s piece, an article from the New York Times about the nutritional value of a Chipotle meal. The piece makes good use of a few bar charts and nice photographs and table to explain how calorific a burrito there can be. Maybe I should be having a salad for lunch today…
Credit for the piece goes to Kevin Quealy, Amanda Cox, and Josh Katz.