Yesterday in the early hours of the morning was technically the latest full moon. And so since today is Friday and we all made it to the end of the week, it seems like a good time to let xkcd educate us all on lunar periodicity.
Yesterday we looked at a nice piece from the BBC showing how big cities across the world will warm from the impact of climate change. It did a really nice job of showcasing the numbers. But it was admittedly number heavy. (And for the Americans in my audience, you probably were left out in the…cold…because the rest of the world uses Celsius to talk temperature.)
But this piece from the University of Maryland is something I have been raving about for weeks now. Generally speaking, people are able to better internalise data and information when they can compare it to something tangible or familiar. And degrees of Celsius, whilst accurate, fail to do that. So this piece takes their 2080 forecast and compares it to today, but in terms of place.
The above map is for Philadelphia. It shows how by 2080, according to a current emissions model, the city’s climate will best resemble that of Memphis, Tennessee and the lower Mississippi River Valley. Or, similar to the tidal regions of North Carolina. Having been to Memphis in the summer once, none of those are pleasant comparisons.
And for those of you in Chicago, it does not get a whole lot better.
So while these might not be as bad, it still is a swath of the plains and the lower Ohio River Valley. And…yes, a little like today’s climate here in Philadelphia.
From a design standpoint, I probably would have used a light or greyed out map. The colours used to represent the topography are too similar to those used to define the similarity. And that can make it tricky to read.
But the true strength of this piece is the designers’ ability to link tomorrow’s climate to today’s by use of space. And as I said at the beginning, I have been talking about this piece offline for weeks. And I likely will for weeks to come.
Credit for the piece goes to Matthew C. Fitzpatrick and Robert R. Dunn .
In Philadelphia, this summer has been warmer than average. But with most recent years being warmer than average, that might not mean much. However, a valid question is that with climate change, how much warmer will the city get on average? The BBC recently published an article that explored the temperature changes in cities around the world according to several different models for best to worst case scenarios.
It does a nice job via scrolling of showing how the averages work as a rolling average and the increase over time. It runs through each scenario, from best case to worst case, as a dotted line and then plots each in comparison to each other to show the range of possible outcomes.
I know that dark or black background is in style for big pieces. But I still do not love them. Thankfully the choice of these two colours work here. The dotted lines also work for showing the projections. And in the intermediate steps, not screencaptured, the previous projections go dark and only the current one is highlighted.
Thankfully the text boxes to the right capture the critical numbers: the actual projection numbers for the monthly average. And they tie them to the lines via the colours used.
Not shown here are a few other elements of the piece. The top of the article starts with a spinning globe that shows how the average temperature across the globe has already changed. Spoiler: not well. While the spinning globe adds some interactivity to the article, it by definition cannot display the entire world all at once, like flat, two-dimensional projections do. This makes it difficult to see impacts across the globe simultaneously. A more standard projection map could have worked really well.
Lastly, the article closes with a few stories about specific locations and how these temperature increases will impact them. These use more illustrations and text. The exception, however, is a graphic of the Arctic that shows how summer sea ice coverage has collapsed over the last few decades.
Overall this is a strong piece that shows some global impacts while allowing the user to dive down into the more granular data and see the impact on some of the world’s largest cities.
Credit for the piece goes to BBC Visual and Data Journalism team.
Today we look at a piece from the Guardian about the blossoming of some cities from, essentially, out of nowhere. Think similar to how there is really no reason for Las Vegas or Phoenix to exist—cities of hundreds of thousands situated smack in the middle of the desert. But most of these new growth cities, cities from scratch as the Guardian calls them, are sprouting in Africa and Asia.
The piece uses two pretty straight-forward graphics to show the scale of the growth problem.
I don’t love the area chart, but even for all its flaws, it it still massively obvious just how much Africa will contribute to population growth in the coming decades. And the line chart, which I find far more effective despite its borderline spaghetti-ness, shows just how much of that growth will likely be urban in nature.
But the star of the piece, for which you will need to click over to the original article to enjoy, are the motion graphics. They capture year-by-year the satellite views showing how the cities have grown from almost nothing. This is a screencapture of Ordos, China. But go back a couple of years and it’s almost an empty desert.
Credit for the piece goes to Antonio Voce and Nick Van Mead.
A few weeks ago here in the United States, we had the mass shootings in El Paso, Texas and Dayton, Ohio. The Washington Post put together a piece looking at how mass shootings have changed since 1966. And unfortunately one of the key takeaways is that since 1999 they are far too common.
The biggest graphic from the article is its timeline.
It captures the total number of people killed per event. But, it also breaks down the shootings by admittedly arbitrary time periods. Here it looks at three distinct ones. The first begins at the beginning of the dataset: 1966. The second begins with Columbine High School in 1999, when two high school teenagers killed 13 fellow students. Then the third begins with the killing of 9 worshippers in a African Episcopal Methodist church in Charlestown, South Carolina.
Within each time period, the peaks become more extreme, and they occur more frequently. The beige boxes do a good job of calling out just how frequently they occur. And then the annotations call out the unfortunate historic events where record numbers of people were killed.
The above is a screenshot of a digital presentation. However, I hope the print piece did a full-page printing of the timeline and showed the entire timeline in sequence. Here, the timeline is chopped up into two separate lines. I like how the thin grey rule breaks the second from the third segment. But the reader loses the vertical comparison of the bars in the first segment to those in the second and third.
Later on in the graphic, the article uses a dot plot to examine the age of the mass shooters. There it could have perhaps used smaller dots that did not feature as much overlap. Or a histogram could have been useful as infrequently used type of chart.
Lastly it uses small multiples of line charts to show the change in frequency of particular types of locations.
Overall it’s a solid piece. But the timeline is its jewel. Unfortunately, I will end up talking about similar graphics about mass shootings far too soon in the future.
Credit for the piece goes to Bonnie Berkowitz, Adrian Blanco, Brittany Renee Mayes, Klara Auerbach, and Danielle Rindler.
This is not exactly data visualisation or graphic design. But it made me laugh the other day. And since we all made it to Friday, we could all do for a good laugh. Classify this under my interest in branding and visual identities.
Two weeks ago President Trump gave a speech at a conference for young conservatives. Uncontroversially, the organisation hosting the event projected on the screen an image of the seal of the President of the United States.
Or did they?
According to the report from the New York Times, it turns out some careless audiovisual guy lifted the wrong image from the internet. Instead of the presidential seal, he took an anti-Trump merchandise image.
He was fired.
So remember, properly source your images. A Google search isn’t the solution.
Happy Friday, all.
Credit for the imitation piece goes to Charles Leazott. I have no idea who designed the original seal.
Yesterday we looked at the New York Times coverage of some water stress climate data and how some US cities fit within the context of the world’s largest cities. Well today we look at how the Washington Post covered the same data set. This time, however, they took a more domestic-centred approach and focused on the US, but at the state level.
Both pieces start with a map to anchor the piece. However, whereas the Times began with a world map, the Post uses a map of the United States. And instead of highlighting particular cities, it labels states mentioned in the following article.
Interestingly, whereas the Times piece showed areas of No Data, including sections of the desert southwest, here the Post appears to be labelling those areas as “arid area”. We also see two different approaches to handling the data display and the bin ranges. Whereas the Times used a continuous gradient the Post opts for a discrete gradient, with sharply defined edges from one bin to the next. Of course, a close examination of the Times map shows how they used a continuous gradient in the legend, but a discrete application. The discrete application makes it far easier to compare areas directly. Gradients are, by definition, harder to distinguish between relatively close areas.
The next biggest distinguishing characteristic is that the Post’s approach is not interactive. Instead, we have only static graphics. But more importantly, the Post opts for a state-level approach. The second graphic looks at the water stress level, but then plots it against daily per capita water use.
My question is from the data side. Whence does the water use data come? It is not exactly specified. Nor does the graphic provide any axis limits for either the x- or the y-axis. What this graphic did make me curious about, however, was the cause of the high water consumption. How much consumption is due to water-intensive agricultural purposes? That might be a better use of the colour dimension of the graphic than tying it to the water stress levels.
The third graphic looks at the international dimension of the dataset, which is where the Times started.
Here we have an interesting use of area to size population. In the second graphic, each state is sized by population. Here, we have countries sized by population as well. Except, the note at the bottom of the graphic notes that neither China nor India are sized to scale. And that make sense since both countries have over a billion people. But, if the graphic is trying to use size in the one dimension, it should be consistent and make China and India enormous. If anything, it would show the scale of the problem of being high stress countries with enormous populations.
I also like how in this graphic, while it is static in nature, breaks each country into a regional classification based upon the continent where the country is located.
Overall this, like the Times piece, is a solid graphic with a few little flaws. But the fascinating bit is how the same dataset can create two stories with two different foci. One with an international flavour like that of the Times, and one of a domestic flavour like this of the Post.
Credit for the piece goes to Bonnie Berkowitz and Adrian Blanco.
Most of Earth’s surface is covered by water. But, as any of you who have swallowed seawater can attest, it is not exactly drinkable. Instead, mankind evolved to drink freshwater. And as some new data suggests, that might not be as plentiful in the future because some areas are already under extreme stress. Yesterday the New York Times published an article looking at the findings.
The piece leads with a large map showing the degree of water stress across the globe. It uses a fairly standard yellow to red spectrum, but note the division of the labels. The High range dwarfs that of the Low, but instead of continuing on, the Extremely High range then shrinks. Unfortunately, the article does not go into the methodology behind that decision and it makes me wonder why the difference in bin sizes.
Of course, any big map makes one wonder about their own local condition. How stressed is Philadelphia, for example? Thankfully, the designers kept that in mind and created an interactive dot plot that marks where each large city falls according to the established bins.
At this scale, it is difficult to find a particular city. I would have liked a quick text search ability to find Philadelphia. Instead, I had to open the source code and search the text there for Philadelphia. But more curiously, I am not certain the graphic shows what the subheading says.
To understand what a third of major urban areas is, we would need to know the total number of said cities. If we knew that, a small number adjacent to the categorisation could be used to create a quick sum. Or a separate graphic showing the breakdown strictly by number of cities could also work. Because seeing where each city falls is both interesting and valuable, especially given how the shown cities are mentioned in the text—it just doesn’t fit the subheading.
But, for those of you from Chicago, I included my former home as a different screenshot. Though I didn’t need to search the source code, because I just happened across it scrolling through the article.
Credit for the piece goes to Somini Sengupta and Weiyi Cai.
Today’s piece is nothing more than a line chart. But in the aftermath of this past weekend’s gun violence—and the inability of this country to enact gun control legislation to try and reduce instances like them—the Economist published a piece looking at public polling on gun control legislation. Perhaps surprisingly, the data shows people are broadly in favour of more restrictive gun laws, including the outlawing of military-style, semi-automatic weapons.
In this graphic, we have a line chart. However the import parts to note are the dots, which is when the survey was conducted. The lines, in this sense, can be seen as a bit misleading. For example, consider that from late 2013 through late 2015 the AP–NORC Centre conducted no surveys. It is entirely possible that support for stricter laws fell, or spiked, but then fell back to the near 60% register it held in 2015.
On the other hand, given the gaps in the dataset, lines would be useful to guide the reader across the graphic. So I can see the need for some visual aid.
Regardless, support for stricter gun laws is higher than your author believed it to be.
Credit for the piece goes to the Economist graphics department.
The weather here in Philadelphia has been fairly intense this summer. But, as August begins and summer begins to wane, even the meteorologists will need a holiday. Thankfully, xkcd has us covered on meteorology’s plan to provide coverage on their holiday.