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.
Did New Jersey eat Delaware?
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.
Here the states are intact
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.
Alarming rates along the coast.
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.
The G7 conference in France wrapped up yesterday and they announced an aid package for Brazil. Why? Because satellite data from both Brazil and the United States points to a rash of fires devastating the Amazon rainforest, the world’s largest carbon sink, or sometimes known as the lungs of the Earth. I have not had time to check this statistic, but I read that 1/5 the world’s oxygen comes from the Amazon ecosystem. I imagine it is a large percentage given the area and the number of trees, but 20% seems high.
Regardless, it is on fire. Some is certainly caused by drier conditions and lightning strikes. But most is manmade. And so after the Brazilian president Jair Bolsonaro said his country did not have the resources to fight the fires, the G7 offered aid.
This morning, Bolsonaro refused it.
And so we have this map from InfoAmazonia that takes NASA data on observed fires for all of South America. I cropped my screencapture to Brazil.
You should also see the smoke maps
A key feature to note here, in addition to that black background approach, is that you will see three distinct features: yellow hotspots fading to cold black areas, yellow dots with red outlines, and red dots. Each means something different. The yellow to red to black gradient simply means frequency of fires, the yellow dots with red outlines represent significantly hot fires from 2002 through 2014. The red dots are what concern us. Those are fires within the last month.
Sure enough, we see lots of fires breaking out across the Amazon. And Bolsonaro not only rejected the aid, but a few weeks ago he rejected similar data. He fired the head of a government agency tasked with tracking the deforestation of the Amazon after he released the agency’s monthly report detailing the deforestation. It had risen by 39%.
From a design standpoint, it is a solid piece. I do wonder, however, if some kind of toggle for the three datasets could have been added. Given the focus on the new fires breaking out, isolating those compared to the historic fires would be useful.
But before wrapping up, I also want to point out that there are a significant number of red dots appearing outside Brazil. The Amazon exists beyond borders, and there are a significant number of fires in neighbouring Bolivia and Paraguay. Let alone around the world…
If you haven’t heard, President Trump wants to buy Greenland from Denmark. So is Greenland going to beat Puerto Rico to joining the Union as the 51st state?
No.
Not even close.
It would be the smallest state in terms of population, but also one of the smallest US territories. But in terms of area, Greenland dwarfs every state but Alaska. Though it still beats Alaska by almost 50% of its land area.
It’s like a super-charged Seward’s Folly
I had hoped to include some more economic data, but that will have to wait for a different post. Acquiring the population data was actually the most difficult—the US Census Bureau does not actually have easy to access data on the populations of US territories not called Puerto Rico.
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.
Getting hotter (because red obviously means heat)
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.
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.
Ew. Just eeww.
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.
Not as ew-y. But still ew.
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.
The raw data so to speak
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.
Ew. Just ew.
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.
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.
Still no reason to move to the Southwest
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.
California is pretty outlying
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.
China and India are really big
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.
More reasons for me not to move to the desert southwest
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.
Not so great, Philly
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.
It helps having Lake Michigan right there
Credit for the piece goes to Somini Sengupta and Weiyi Cai.
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.
