Dorian now speeds away from Newfoundland and into the North Atlantic. We looked at its historic intensity last week. But during that week, with all the talk of maps and Alabama, I noted to myself a map from the BBC that showed the forecast path.
But note the state borders. New Jersey and Delaware have merged. Is it Delawarsey? And what about Maryland, Virginia, and the District of Columbia? Compare that to this map from the Guardian.
What we have are intact states. But, and it might be difficult to see at this scale, the problem may be that it appears the BBC map is using sea borders. I wonder if the Delaware Bay, which isn’t a land border, is a reason for the lack of a boundary between the two states. Similarly, is the Potomac River and its estuary the reason for a lack of a border between Virginia, Maryland, and DC?
I appreciate that land shape boundary files are easy, but they sometimes can mislead users as to actual land borders.
Credit for these pieces go the BBC graphics department and the Guardian graphics department.
There is a lot going on in the world—here’s looking at you Brexit vote today—but I did not want to miss this frightening article from the BBC on the melting of Greenland’s ice. It’s happening. And it’s happening faster than thought.
There are several insightful graphics, including the standard photo slider of before and after, a line chart showing the forecast rise of sea levels within the possible range. But this one caught my eye.
The colour palette here works fairly well. The darkest reds are not matched by a dark blue, but that is because the ice gain does not match the ice loss. Usually we might see a dark blue just to pair with a dark red, but again, we don’t because the designers recognised that, as another chart shows, the ice loss is outweighing the gains, though there are some to be found most notably at the centre of the ice sheets. This is a small detail, but something that struck me as impressive.
My only nitpick is that the legend does not quantify the amounts of gain or loss. That could show the extremes and reinforce the point that the loss is dwarfing the gain.
Credit for the piece goes to the BBC graphics department.
One of the things I have been following closely the last few months has been the protests in Hong Kong. The city is one of China’s few Special Administrative Regions—basically the former British colony of Hong Kong and the former Portuguese colony of Macau, two cities bordering mainland China and separated by the Pearl River estuary.
Long story short, but since 1997 Hong Kong should enjoy 50 years of a legal system that is more aligned to that of its former status of a British colony than that of mainland China. But increasingly since Xi Jinping took power, he has been eroding those rights and the youth of Hong Kong have taken to the streets to protest, a right they enjoy but not the rest of mainland Chinese.
And so we have a survey looking at the identity by which those people living in Hong Kong choose to identify.
And it’s not Chinese.
From a news perspective, this poses problems for a Beijing-based Chinese government that is making pains to promote a greater Chinese identity throughout the world, least of all by pushing for a reunification with Taiwan by force if necessary. A generation of several million Hong Kongers and the way they raise their children, in addition to their friends and supporters abroad, weakens the authority of Beijing.
Hence the threat of a Tiananmen Square style crackdown on Hong Konger protestors.
Alas, the United States has been far more concerned with its trade dispute than it has been the democratic and human rights of several million people. At least, that is the impression given by the White House.
But, as to the design, I do not love the spaghettification of the line charts. Though I do appreciate that the Hong Kong identity has been separated by the maroon-coloured line. I wonder if labelling the lines in the small multiples is necessary given the decision to include the legend at the top of the chart.
The other tricky thing with this type of chart is that the data series is a population cohort. And yet the data is based on a time series. And so the cohorts vary over time. It might not be entirely clear to the audience that this (appears to be)/is a sample of people of an age at a particular date. How do those people change over the years? It’s hard to see that trend by separating out the data.
Overall, it’s a solid piece. And it’s important given the gravity of the protests in Hong Kong.
Credit for the piece goes to the Economist Data team.
As all my readers probably know, I love London. And in loving London, I love the Tube and the Oyster Card and all that goes along with Transport for London. But, I have noticed that sometimes when I take the Underground, there are segments where it gets a bit loud, especially with the windows open. The Economist covered this in a recent article where they looked at some data from a London-based design firm that makes noise protective gear. (For purposes of bias, that seems important to mention here.)
The data looks at decibels in a few Underground lines and when the levels reach potentially harmful levels. I took a screenshot of the Bakerloo line, with which I am familiar. (At least from Paddington to Lambeth.) Not surprisingly, there are a few segments that are quite loud.
I like this graphic—but like I said about bias, I’m biased. The graphic does a good job of using the above the 85-decibel line area fill to show the regions where it gets loud. And in general it works. However, if you look at the beginning of the Bakerloo line noise levels the jumps up in down in noise levels, because they happen so quickly in succession, begin to appear as a solid fill. It masks the importance of those periods where the noise levels are, in fact, potentially dangerous.
I have had to deal with this problem often in my work at the Fed, where some data over decades is available on a weekly basis. One trick that works, besides averaging the data, is thinning out the stroke of the line so the overlaps do not appear so thick. It could make it difficult to read, but it avoids the density issues at the beginning of that chart.
All in all, though, I would love a London-like transport system here in Philly. I’d rather some loud noises than polluting cars on the road.
Credit for the piece goes to the Economist Data Team.
Moving away from climate change now, we turn to the lovely land of Afghanistan. While the Trump administration continues to negotiate with the Taliban in hopes of ending the war, the war continues to go worse for Afghanistan, its government, and its allies, including the United States.
It is true that US and NATO ally deaths are down since the withdraw of combat troops in 2014. But, violence and sheer deaths are significantly up. And as this article from the Economist points out, the deaths in Afghanistan are now worse than they are in Syria.
The beginning of the article uses a timeline to chart the history of Afghan conflicts as well as the GDP and number of deaths. And it is a fascinating chart in its own right. But I wanted to share this, a small multiples featuring graphic looking at the geographic spread of deaths throughout the country.
It does a nice job by chunking Afghanistan into discrete areas shaped as hexagons and bins deaths into those areas. All the while, the shape remains roughly that of Afghanistan with the Hindu Kush mountain range in particular overlaid. (Though, I am not sure why it is made darker in the 2003–04 map.)
To highlight particular cities or areas, hexagons are outlined to draw attention to the population centres of interest. But overall, the rise in violence and deaths is clear and unmistakable. And it has spread from what was once pockets in the south to the whole of the country that isn’t mountains or deserts.
Tamerlane would be proud.
Credit for the piece goes to the Economist graphics department.
Last week we looked at a few posts that showed the future impact of climate change at both a global and US-level scale. In the midst of last week and those articles, the Washington Post looked backwards at the past century or so to identify how quickly the US has changed. Spoiler: some places are already significantly warmer than they have been. Spoiler two: the Northeast is one such place.
The piece is a larger and more narrative article using examples and anecdotes to make its point. But it does contain several key graphics. The first is a big map that shows how temperature has changed since 1895.
The map does what it has to and is nothing particularly fancy or groundbreaking—see what I did there?—in design. But it is clear and communicates effectively the dramatic shifts in particular regions.
The more interesting part, along with what we looked at last week, is the ability to choose a particular county and see how it has trended since 1895 and compare that to the baseline, US-level average. Naturally, some counties have been warming faster, others slower. Philadelphia County, the entirety of the city, has warmed more than the US average, but thankfully less than the Northeast average as the article points out.
But, not to leave out Chicago as I did last week, Cook County, Illinois is right on line with the US average.
But the big cities on the West Coast look very unattractive.
The interactive piece does a nice job clearly focusing the user’s attention on the long run average through the coloured lines instead of focusing attention on the yearly deviations, which can vary significantly from year to year.
And for those Americans who are not familiar with Celsius, one degree Celsius equals approximately 1.8º Fahrenheit.
Overall this is a solid piece that continues to show just what future generations are going to have to fix.
Credit for the piece goes to Steven Mufson, Chris Mooney, Juliet Eilperin, John Muyskens, and Salwan Georges.
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.
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.
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.