Last month Vox published an article about the trend in America wherein people are drinking less alcohol. They cited a Gallup poll conducted since 1939 and which reported only 54% of Americans reported partaking in America’s national tipple—except for that brief dalliance with Prohibition—making this the least-drinking society since, well, at least 1939.
Vox charted the data in the following graphic.
Overall, the graphic is good. Here the use of individual dots makes clear the years wherein Gallup conducted the survey. In some of the earlier periods it was not an annual question. The line weight is just thick enough to be distinct and the axis lines are lighter and in a light grey to create contrast.
The line uses the colour black. You really do not need colour in a line chart—or a bar chart or anything really charting only a single variable—unless it stems from your branding. Admittedly, Vox is now membership-supported and I am not a member so I only can read a few articles and am unsure if their chart brand standards now use black as their primary colour.
I can quibble with the axis label, “Drink alcoholic beverages”, because that should or could be included in the graphic header or sub-header. But putting in the graphic space is fine. And I like it next to the line instead of in a legend above the chart.
But the thing that irks me is the use of data labels at specific years. You could argue for the inclusion of the label at the current year, the 54%. You could argue for the maximum value, the 71%, but I would not.
Labels distract the reader’s eyes from the line itself and the line is the story. Calling out the 54% and maybe the 71%, fine, but the random 67%? A second random 67%? Those are unnecessary distractions that take away from the chart’s communicative value.
I have mentioned it before and I will mention it again, the use of excessive data labels plagues data visualisation these days. I need to write it up in a longer piece and someday I will. Here is a crude mockup of the graphic without the data labels.
The line’s pattern is easier to spot—fewer distractions for the eyes. The pattern is clear that since the late 1970s roughly 65% of Americans drank alcohol, with the occasional dip, including one in the late 1980s and early 1990s and a briefer one in the mid-90s. I wonder if the 1989–90 dip relates to the recession. The sudden dip in the mid-90s confounds me. But the point is these things are easier to spot without labels the sparkling distraction of the labelling.
Again, overall, the graphic is good. And these days the state of information design and data visualisation is…not great, Bob. So I do not want to critique this graphic too heavily. But a tweak or two would make it even better.
For my longtime readers, you know that despite living in both Chicago and now Philadelphia, I am and have been since way back in 1999, a Boston Red Sox fan. And this week, the Carmine Hose make their biennial visit down I-95 to South Philadelphia.
And I will be there in person to watch.
This is the second series after the All-Star break and as much as I wish it were otherwise, the Red Sox are just not as good as the Phillies. The team my hometown supports is just better than the one for whom I root. The Sox are 54-47 with a .535 winning percentage and the Phillies are 56-43 with a .566 winning percentage. The Phillies have the better rotation, by far. And the Red Sox’ two best pitchers just threw out in Chicago whereas the Phillies’ best toe the rubber over the next three nights.
But…the Boston baseball bats are a bit better and the Bank is a bandbox. Consequently I do not want to say the Phillies sweep the Sox, but my prediction is it will be tough for the Sox.
How does this connect to information design and data visualisation? Last week as the “second half” began, my local rag, the Philadelphia Inquirer, published an article examining the Phillies’ season to date and their road up ahead. It included a couple of graphics I wanted to share, because I found them a nice addition to the type of article usually devoid of such visual pieces.
The first piece looked at the Phillies’ performance relative to recent teams.
You can see the 2025 club is out performing the 2022 and 2023 editions of the team. I have a few critiques, but overall I enjoyed the graphic. I think the heavier stroke and the colour change for 2025 works…but are both necessary? Or at least to the extent the designer chose? And which line is which year?
The chart is too visually busy with too many bits and bobs clamouring for attention. The heaviness of the blue stroke works because the chart needs the loudness. But move the year labels to a consistent location—which, once established helps the user find similar information—and remove the data label annotations—the precise number of games over .500 should be clear through the axis labelling. If I make a couple quick edits in Photoshop to the image, you end up with something like this.
Again, an overall good graphic, but one with just a few tweaks to quiet the overall piece allows the user to more clearly identify the visual pattern—that the Phillies are good and better than two of their three most recent iterations.
The second piece was even better. It looked at the Phillies’ forthcoming opponents, which at the time of publication first included the Los Angeles Angels before the Sox. (For what it may be worth, the Angels won two of three.)
A different graphic, the same critique: overall good, but visually cluttered. Here I revisit the chart, but move some elements around to clear the chart’s visual space of clutter to emphasise the visual pattern in the chart.
I left the annotated point about the Phillies’ winning percentage, because I do think annotations work. But when a chart is full of annotations, the annotations become the story, not the graphic. And if that is the story, then a table or factettes become a better visual solution to the problem.
I will add I do not love how low the line for the opponents falls below the chart’s minimum axis. I probably would have extended the chart to something like .750 and .250, but it is far from the worst sin I see these days. (I keep thinking of writing something about the decline of the quality of data visualisation and information design in recent years, but that feels more akin to a polemical essay than a short blog post.)
Big takeaway, I like seeing my baseball articles with nice data visualisation. It heralds back to a couple of years ago when outlets routinely published such pieces. Baseball especially benefits from data visualisation because the game generates massive amounts of data both within each game and the collective 162-game season.
Good on the Inquirer for this article. I do not usually read the Sports section, because I am not a Philadelphia sports fan, but maybe I will read a bit more of the Phillies coverage if they include visual content like this.
Credit for the original pieces goes to Chris A. Williams. The edits are mine.
Monday and Tuesday, Major League Baseball conducted its amateur player draft, wherein teams select American university and high school players. They have two weeks to sign them and assign them. (Though many will not actually play this year.)
Two years ago the Red Sox installed Craig Breslow as their new chief baseball organisation. He has cut a number of front office personnel and reorganised the Red Sox front office, leading to a number of departures. Crucially for this context, a number of the scouts who identified key Red Sox players like Roman Anthony were either let go or left. The team then focused on analysts and models.
My questions have thus been focused on how this might change the Red Sox’ approach to the draft. A running joke in Sox circles has been how every year the Red Sox draft a high school shortstop from California. But this year, the Red Sox’ first pick was Kyson Witherspoon, a starting pitcher from Oklahoma.
The graphic above shows how Witherspoon was ranked by the media who covers this niche area of baseball: a consensus top-10 pick. And yet the Sox selected Witherspoon at no. 15 overall. This has been another trend of the Sox over the last several years, where other teams select lower-ranked players and leave higher-ranked players available to the Sox and other mid-round selectors. Similarly, fourth-round pick Anthony Eyanson, ranked roughly 40–65, remained on the board and so the Sox took him at no. 87.
As someone who follows the Sox system, they need quality pitching prospects as they have very few of proven track records in the minors. Witherspoon and Eyanson provide them that, at least the quality, the track records have yet to develop. Marcus Phillips, seemingly, presents more of a lottery ticket. His ranking spread so far, from 13 to 98, it is clear there is no consensus on the type of talent the Sox took in him.
Godbout is a middle-infielder with a good hit tool, but light on the power. Clearly the Sox believe they can work with him to develop the power in the next few years. But all in all, three pitchers in the first four rounds.
Now, the additional context for the non-baseball fans amongst you who are still reading is this. Baseball’s draft does not work in the same way as those of, say the NFL or the NBA. One, the draft is much deeper at 20 rounds. (In my lifetime it used to be as deep as 50.) Two, teams (usually) do not draft for need. I.e., unlike the NFL where a team , say the Patriots, who needs a wide receiver might draft a wide receiver with their first pick, a team like the Red Sox who need, say, a catcher will not draft a catcher. A key reason why, it takes years for an MLB draftee to reach the majors if he does so at all. Whereas an NFL draftee likely plays for the Patriots the following year. In short, there is often a lag between the draft and the debut—unless you are the Los Angeles Angels. Thus you address your current positional needs via free agency or trades, not the draft. (Unless you are the Angels.) For the purposes of the draft, you therefore draft the “best player available” (BPA).
Some systems, however, are just better at doing different things. Some teams do a better job of developing pitchers, others of developing hitters. Some of developing certain traits of pitching or hitting. Some teams are just bad at it overall. The Sox have, of late, been very good at developing position players/hitters. They have been pretty not-so-great at developing pitching. Hence, when Breslow said he could improve their pitching pipeline, the Sox jumped at the chance to hire him. (It also helps everyone else they interviewed said no, and a number of candidates declined to even be interviewed.)
In part, the failure to develop pitching could be a failure to identify the correct player traits or characteristics. It could be the wrong methods and strategies, improper techniques and technologies. But, if we look at the recent history of Red Sox drafts, it could be, in part, also a consistent lack of drafting pitching. After all, the 26-man MLB team roster comprises 14 pitchers and 12 position players. (Technically it is a limit of 14 pitchers, but teams seem to generally max out their pitcher limit.)
You can see in my graphic above, since the late 2000s, the Red Sox, with few exceptions, ever drafted more than 50% pitchers. This period of time coincides with the ascendance of the vaunted Sox position player development factory and the decline of the homegrown starter. (Again, the obligatory reminder correlation is not causation.)
Nevertheless, in the last few years, we have seen the drafting of pitchers spike. In the first two years of the new Breslow regime, pitchers represent more than 70% of the amateur draft. (There is also the international signing period where players from around the world can be signed within limits. This is how the Sox have drafted very talented players like Rafael Devers and Xander Bogaerts. I omitted this talent acquisition channel from the graphics.)
Consequently, when a team states its strategy is to draft the BPA, but over 70% of all players selected are pitchers, I wonder how one defines “best”. Are the Red Sox weighing pitching more heavily than hitting? Is this an attempt to address a long-standing asymmetry in talent? In the models teams like the Red Sox use, are pitchers worth, say, 1.5× more than hitters? I doubt we will ever know the answer, though the team maintains they draft the best player available.
Ultimately, it may matter very little for the Red Sox in the near-term. The sport’s best prospect, Roman Anthony, is just starting to man the outfield for the Sox. A consensus top-10 prospect, Marcelo Mayer, has also just debuted. A top-25 prospect, Kristian Campbell, debuted on Opening Day. Two second-year players round out the outfield in Ceddanne Rafaela and Wilyer Abreu. A rookie catcher is behind the plate. The Sox may not need serious high-end positional player talent in the next 3–5 years. (Though it certainly helps when trying to trade for other pieces.)
But a two-year lull in drafting high-end positional player talent, on top of the previous two years’ first-round draft picks, catcher Kyle Teal and outfielder Braden Montgomery, being traded for ace Garrett Crochet, means the Sox may well have a several-year gap in positional player matriculation to the majors. That might matter.
Baseball, unlike the NFL and the NBA, is a marathon, however. So perhaps this is all a tempest in a teapot. Let us check back in five years’ time and we can see whether this new draft strategy, if it is indeed a strategy, has cost the Red Sox anything.
Last weekend, the United States’ 4th of July holiday weekend, the remnants of a tropical system inundated a central Texas river valley with months’ worth of rain in just a few short hours. The result? The tragic loss of over 100 lives (and authorities are still searching for missing people).
Debate rages about why the casualties ranked so high—the gutting of the National Weather Service by the administration shines brightly—but the natural causes of the disaster are easier to identify. And the BBC did a great job covering those in a lengthy article with a number of helpful graphics.
I will start with this precipitation map, created with National Oceanic and Atmospheric Administration (NOAA) data.
I remain less than fully enthusiastic about continual gradients for map colouration schemes, however the extreme volume of rainfall during the weather event makes the location of the flooding obvious to all. Nonetheless the designers annotated the map, pointing out river, the camp at the centre of the tragedy and the county wherein most of the deaths occurred.
In short, more than 12 inches of rain fell in less than 24 hours. The article also uses a time lapse video to show the river’s flash flooding when it rose a number of feet in less than half an hour.
The article uses the captivating footage of the flash flooding as the lead graphic component. And I get it. The footage is shocking. And you want to get those sweet, sweet engagement clicks and views. But from the standpoint of the overall narrative structure of the piece, I wonder if starting with the result works best.
Rather, the extreme rainfall and geographic features of the river valley contributed at the most fundamental level and showcasing that information and data, such as in the above map, would be a better place to start. The endpoint or culmination of the contributing factors is the flash flooding and the annotated photo of flood water heights inside the cabins of the camp.
Overall I enjoyed the piece tremendously and walked away better informed. I had visited an area 80 miles east of the floods several years ago for a wedding. Coincidentally on the 4th I remarked to a different friend from the area now living in Philadelphia about the flatness and barrenness of the landscape between Austin and San Antonio. I had no idea that just to the west rivers cut through the elevated terrain that would together cause over a hundred deaths a few hours later.
Credit for the piece goes to the BBC, but the article listed a healthy number of contributors whom I shall paste here: Writing by Gary O’Donoghue in Kerr County, Texas, Matt Taylor of BBC Weather and Malu Cursino. Edited by Tom Geoghegan. Images: Reuters/Evan Garcia, Brandon Bell, Dustin Safranek/EPA/Shutterstock, Camp Mystic, Jim Vondruska, Ronaldo Schemidt/AFP and Getty.
Last week was baseball’s opening day. And so on the socials I released my predictions for the season and then a look at the revolving door that has been the Red Sox and second base since 2017.
Back in 2017 we were in the 11th year of Dustin Pedroia being the Sox’ star second baseman. That summer, Manny Machado slid spikes up into second and ruined Pedroia’s knee. Pedroia had surgery and missed Opening Day 2018 then struggled to return. He played 105 games in 2017 then only three in 2018 and then six in 2019. And thus began the instability. Here’s a list of the Opening Day second baseman since 2017.
2018 Eduardo Nuñez
2019 Eduardo Nuñez
2020 José Peraza
2021 Kiké Hernández
2022 Trevor Story
2023 Christian Arroyo
2024 Enmanuel Valdez
2025 Kristian Campbell
And, again, by comparison…
2007 Dustin Pedroia
2008 Dustin Pedroia
2009 Dustin Pedroia
2010 Dustin Pedroia
2011 Dustin Pedroia
2012 Dustin Pedroia
2013 Dustin Pedroia
2014 Dustin Pedroia
2015 Dustin Pedroia
2016 Dustin Pedroia
2017 Dustin Pedroia
But not only is it a lack of stability, it is a lack of production. Wins Above Replacement (WAR) is a statistic that attempts to capture a player’s value relative to an “average” player or substitute. A below replacement level person is less than 0 WAR. A substitute is 0–2, a regular everyday players is 2–5, an All Star is 5–8, and an elite MVP level performance is 8+ WAR. And, spoiler, the Sox have not had a 5+ WAR second baseman since Pedroia’s final full season in 2016.
Suffice it to say, the Sox have long had a need for a long-term second baseman. The graphics I created were meant to be two Instagram images in the same post, and so the the axis labels and lines stretch across the artboards.
The graphic shows pretty clearly the turmoil at the keystone. The two outliers are Kiké Hernández in 2021 and Trevor Story in 2022. The latter is easily explained. Story was signed to be the backup plan in case shortstop Xander Bogaerts left after 2022. (Back in 2013 I made a graphic after a similar revolving door of shortstops in the eight years after the Red Sox traded Nomar Garciaparra. Then the question was, would a young rookie named Xander Bogaerts be the replacement for the beloved Nomah. Xander played 10 years for the Sox.)
Kiké, however, is a bit trickier to explain. WAR weights value by position. A second baseman is worth more than a leftfielder. But shortstops and centrefielders are worth more than second baseman. And Kiké played a lot more shortstop and centre than he did second base, which likely explains his 4.9 WAR that season.
And so now in 2025 we had yet another guy starting at second. His name? Kristian Campbell. I saw him a few times last year as he rocketed from A to AAA, the lowest to highest levels of minor league player development below the major league. I thought he looked good and so did the professionals, because he’s a consensus top-10 prospect in the sport.
Going into Monday’s matchup between Boston and Baltimore, Campbell is hitting 6 for 14 with one homer and two doubles, an on-base percentage of .500 and an OPS (on-base plus slugging, which weights extra base hits more heavily than singles) of 1.286. Spoiler: that’s very good.
Boston beat writers are reporting the Sox and Campbell’s agent are in talks for a long-term extension.
It looks like the Sox may have found their new long-term second baseman.
Apologies, all, for the lengthy delay in posting. I decided to take some time away from work-related things for a few months around the holidays and try to enjoy, well, the holidays. Moving forward, I intend to at least start posting about once per week. After all, the state of information design these days provides me a lot of potential critiques.
Let us start with the news du jour , the application of tariffs on China and the delayed imposition on both Canada and Mexico. Firstly, let us be very clear what a tariff is. A tariff is a tax paid by importers or consumers on goods sourced from outside the country. In this case, we are talking about Canadian, Mexican, and Chinese imports and the United States slapping tariffs on goods from those countries. Foreign governments do not pay money to the United States, neither Canada, nor Mexico, nor China will pay money to the United States.
You will.
You should expect your shopping costs to increase, whether that is on the price of gasoline (imported from Canada), fast fashion apparel (from China), or avocados (from Mexico). On the more durable goods side, homes are built with Canadian lumber and your automobiles with parts sourced from across North America—the reason why we negotiated NAFTA back in the 1990s.
Now that we have established what tariffs are, why is the Trump administration imposing them? Ostensibly because border security and fentanyl. What those two issues have to do with trade policy and economics…I have no idea. But a few news outlets created graphics showing US imports from our top-five trading partners.
First I saw this graphic from the New York Times. It is a variation of a streamgraph and it needs some work.
A streamgraph type chart from the New York Times
To start, at any point along the timeline, can you roughly get a sense of what the value for any country is? No. Because there is no y-axis to provide a sense of scale. Perhaps these are the top import sources and these are their share of the total imports? Read the fine print and…no. These are the countries with a minimum of 2% share in 2024, which is approximately 75% of US imports.
This graphic fails at clearly communicating the share of imports. You need to somehow extrapolate from the y-height in 2024 given the three direct labels for Canada, Mexico, and China what the values are at any other point in time or for any other country.
Nevertheless, the chart does a few things nicely. It does highlight the three countries of importance to the story, using colours instead of greys. That focuses your attention on the story, whilst leaving other countries of importance still available for your review. Secondly, the nature of this chart ranks the greatest share as opposed to a straight stacked area chart.
Overall, for me the chart fails on a number of fronts. You could argue it looks pretty, though.
The aforementioned stacked area charts—also not a favourite of mine for this sort of comparison—forces the designer to choose a starting country in this case and then stack other countries atop it.
A stacked area chart from the BBC
What this chart does really well, especially well compared to the previous New York Times example is provide content for all countries across all time periods by the inclusion of the y-axis. Like the Times graphic it focuses attention on Canada, Mexico, and China with colour and uses grey to de-emphasise the other countries. You can see here how the Times’ decision to exclude all countries below 2% can skew the visual impact of the chart, though here all countries below Japan (everything but the top-five) are grouped as other.
Personally, the inclusion of the specific data labels for Canada, Mexico, and China distract from the visualisation and are redundant. The y-axis provides the necessary framework to visually estimate the share. If the reader needs a value to the precision level of tenths, a table may be a better option.
I could not find one of the charts I thought I had bookmarked and so in an image search I found a chart from one of my former employers on the same topic (though it uses value instead of share) and it is worth a quick critique.
A stacked area chart from Euromonitor International
Towards the end of my time there, I was creating templates for more wide-screen content. My fear from an information design and data visualisation standpoint, however, was the increased stretch in simple, low data-intensity graphics. This chart incorporates just 42 data points and yet it stretches across 1200 pixels on my screen with a height of 500.
Compare that to the previous BBC graphic, which is also 1200 pixels, but has a greater height of 825 pixels. Those two dimensions give ratios of 2.4 for Euromonitor International and 1.455 for the BBC. Neither is the naturally aesthetically pleasing golden ratio of 1.618, but at least the BBC version is close to Tufte’s recommended 1.5–1.6. The idea behind this is that the greater the ratio, the softer the slope of the line. This can make it more difficult to compare lines. A steeper slope can emphasise changes over time, especially in a line chart. You can roughly compare this by looking at the last few years of the longer time span in the BBC graphic to the entirety of this graphic. You can more easily see the change in the y-axis because you have more pixels in which to show the change.
Finally we get to another New York Times graphic. This one, however, is a more traditional line chart.
A line chart from the New York Times
And for my money, this is the best. The data is presented most clearly and the chart is the most legible and digestible. The colours clearly focus your attention on Canada, Mexico, and China. The use of lines instead of stacked area allow the top importer to “rise” to the top. You can track the rapid rise of Chinese imports from the late 1990s through to the first Trump administration and the imposition of tariffs in 2018—note the significant drop in the line. In fact you can see the impact in Mexico becoming the United States’ top trading partner in recent years.
Over the years, if I had a dollar for every time I was told someone wanted a graphic made “sexier” or with more “sizzle” or made “flashier”, I would have…a bigger bank account. The issue is that “cooler” graphics do not always lead to clearer graphics. Graphics that communicate the data better. And the guiding principle of information design and data visualisation should be to make your graphics clear rather than cool.
Credit for the New York Times streamgraph goes to Karl Russell.
Credit for the BBC graphic goes to the BBC graphics department.
Credit for the Euromonitor International graphic goes to Justinas Liuima.
Credit for the New York Times line chart goes to the New York Times.
This past weekend saw some flooding along the East Coast due to the Moon pulling on Earth’s water. In Boston that meant downtown flooding, including Long Wharf. The Boston Globe’sarticle about the flooding dwelt with more impact, causes, and long-term forecasts—none of which really warranted data visualisation or information graphics. Nonetheless, the article included a long time series examining the change in Boston’s sea level relative to the mean.
For me, the graphic works really well. The data strips out the seasonal fluctuations and presents the reader with a clear view of rising sea levels in Boston. If the noisiness of the red line distracts the reader—one wonders if an annual average could have been used—the blue trend line makes it clear.
And that blue trend line has a nice graphic trick to help itself. Note the designer added a thin white stroke on the outside of the line, providing visual separation from the red line below.
My only real critique with the graphic is the baseline and the axis lines. The chart uses solid black lines for the axes, with grey lines running horizontally depicting the deviation from the mean sea level. But the black lines draw the attention of the eye and thus diminish the importance of the 0 inch line, which actually serves as the baseline of the chart.
If I quickly edit the screenshot in Photoshop, you can see how shifting the emphasis subtly changes the chart’s message.
Today I have a little post about something I noticed over the weekend: labelling line charts.
It begins with a BBC article I read about the ongoing return to office mandates some companies have been rolling out over the last few years. When I look for work these days, one important factor is the office work situation and so seeing an article about the tension in that issue, I had to read it.
The article includes this graphic of Office of National Statistics (ONS) data and BBC analysis.
Overall, the chart does a few things I like, most notably including the demarcation for the methodology change. The red–green here also works. Additionally the thesis expressed by the title, “Hybrid has overtaken WFH”, clearly evidences itself by the green line crossing the blue. (I would quibble and perhaps change the hybrid line to red as it is visually more impactful.)
I also like on the y-axis how we do not have a line connecting all the intervals. Such lines are often unnecessary and can often add visual clutter, see yesterday’s post for something similar. I quibble here with dropping the % symbol for the zero-line. Since the rest of the graphic uses it, I would have put the baseline as 0%. And that baseline is indeed represented by a darker, black line instead of the grey used for the other intervals.
Then we get to the labels on the right of the graphic. Firstly, I do not subscribe to the view charts and graphs need to label individual datapoints. If the designer created the chart correctly, the graph should be legible. Furthermore, charts show relationships, if one needs a specific value, I would opt for a table or a factette instead. These are not the most egregious labels, mind you, but here they label the datapoint, but not the line. Instead, for the line the reader needs to go back to the chart’s data definition and retrieve the information associated with the colour.
Now compare that to a chart representing Major League Baseball’s playoff odds from Fangraphs.
Here too we have mostly good things going on, but I want to highlight the labelling at the right. This chart also includes the precise value, which is fine, but here we also have the actual label for the lines. The user does not need to leave the experience of the chart to find the relevant information, although a secondary/redundant display or legend can be found at the bottom of the chart.
If you can take the time to label the end value, you may as well label the series.
Credit for the BBC graphic goes to the BBC’s graphics department.
Credit for the Fangraphs piece goes to Fangraphs’ design team.
As it happens, the Latino culture largely remains x’ed out on using the term Latinx, according to a new survey from Pew Research.
The issue of supplanting Latino/Latina with Latinx as a gender neutral replacement—or as a complementary alternative—emerged in the general discourse in that oh-so-fun year of 2020 when everything went well.
One common argument I have heard is the inherent gender within the Spanish language. Broadly you use -o for singular masculine endings and -a for singular feminine forms and -os for plural masculine and mixed gender forms and -as for plural feminine forms.
Perhaps my biggest issue is that -x does not linguistically make sense. X is typically pronounced like a j or sometimes an s. Consider how Mexicans pronounce Mexico, May-hie-co. Latinx becomes La-teen-h, an almost silent ending that does not fit, at least to my ears. Pero, hablo solo un pocito Español. Aprendí a hablar por cuatro años en la escuela, dos años de niño, y trabaja en una cocina del restaurante. Thus Latinx, pronounced Lat-in-ecks, always seemed, daresay, a gringo solution to a problem that earlier polling of Latino communities did not indicate was a problem. With the potential exception of the young, but even then not terribly so.
Four years later, however, and not much has changed according to Pew. Their graphic shows as much.
Significantly more people are aware of Latinx as a term. Fewer people use the term, though not significantly. Although a shift from four to three percent can be seen as significant given its low adoption. Moreover, as a second graphic shows, more people who are aware of the term think it should not be used.
The article continues with a discussion of a new new alternative, Latine, which to my ears makes more sense. But is largely yet unheard of in the community—20%—and of those who have heard it, almost nobody uses it.
As far as the graphics go, I am not a huge fan.
For the first, we have two lines showing the movement between two datapoints. At the most basic level, the use of a line chart makes sense to depict two series moving between two points in time. But without any axis labelling one can only trust the lines begin and end at the correct position. Furthermore people need to read the specific labels to get a sense of the line charts’ magnitude. More of a tell, don’t show approach. If the chart had even a simple 0% line and 50% line, one need not label all four datapoints to convey the scale of the graphic.
Ultimately, though, does a chart with four datapoints even need to be graphed? Some would argue in most instances a dataset with fewer than five or six numbers need not be visualised; a table should suffice. Broadly I agree. This chart does show a particularly striking trend of increasing awareness of the term, but largely static to declining usage.
The second graphic, however, falls more squarely into that argument’s camp of “Why bother?” It shows simply two numbers. Numbers placed atop purple rectangles. Without any axis labelling, we presume these bars represent columns encoding the percent—at least the lines in the first chart were clearer to their meaning. Then we still have the issue of telling and not showing. Perhaps labelling to the left from 0% to 75% or 80% would help. Then you need not even add additional “ink” with the four digits sitting atop the bars and sparkling for unnecessary reader attention.
This falls into a broader trends I have witnessed over the last few years in the information design and data visualisation field of labelling individual datapoints within a chart. It is a trend with which I strongly disagree, but perhaps is best left for another post another day. Suffice it to say, if knowing the precise measurement is important, a chart is not the best form. For that use case I would opt for a table, best used to organise and find specific datapoints.
Overall, Pew shows that within the Latino community, very few use the term Latinx. Consequently, perhaps this entire post is, to use a Spanish-language expression, a tempest in a teapot.
For the last few weeks I have been working on my portfolio site as I update things. (Note to self, do not wait another 15 years before embarking upon such an update.)
At the University of the Arts (requiescat in pace), I took an information design class wherein I spent a semester learning about the electricity generation market in the Philadelphia region. This became a key part of my portfolio when I applied for 99 jobs at the beginning of the Great Recession, had 3 interviews, and only 1 job offer.
That job offer lead me to Chicago and Euromonitor International where one of the first projects I worked on was a datagraphic about throat share, i.e. what drinks products/brands people in different countries drank. Essentially, I took what I learned about visualising the share of electricity generation in Pennsylvania to the share of drinks consumption across the world. Thus a career was born. Fast forward 15 years and I wanted to see how that electricity generation had changed. And I can do that because I used a public source in the US Energy Information Administration.
Anecdotally, Pennsylvanians know fracking for natural gas has been a boon to the former coal and steel parts of the Commonwealth, which really is a lifeline. But overall, Pennsylvania has long been known as a nuclear power state. More on that from a personal standpoint in a later post. Back in the uphill both ways to university day, I did not look at the United States overall. But now I can.
Largely this fits with the narratives I know. Coal has plummeted both in the Commonwealth and more broadly as natural gas has largely taken its place. No, that’s not great from a climate change perspective, but natural gas is definitely better than coal.
Renewables, nationally speaking, are now about 20% or 1/5th our net electricity generation. But in Pennsylvania, whilst this Monday morning might be a bright and blue sky day great for solar power, the nights are getting longer and we get a lot of clouds. We do have some hydroelectric dams—it helps to be a partially mountainous state. And, yes, we do have the wind farms along the Allegheny Ridge, one of the windiest spots along the East Coast, but for context one of the two nuclear reactors near to which I grew up is equal to almost the entire wind power electricity generation in the entire Commonwealth.
But for all the supposed growth in renewables, we just are not seeing it in Pennsylvania, at least not at a scale to supplant fossil fuels. And unfortunately, it is not as if demand is falling. And that might be why we are seeing quiet talks about reactivating some of Pennsylvania’s shuttered nuclear reactors. If you could bump that nuclear share of electric throat back up to 40% or even 50%, you could cut down that natural gas usage significantly.
