How Warm Will It Get? Part II

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.
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.
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 .

How Warm Will It Get?

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
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.
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.

Water, Water Everywhere Nor Any Drop to Drink Part II

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
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
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
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.

The Rise of the Tropic(al Plant)s

Last week I had three different discussions with people about some of the impact of climate change upon the United States. However, what did not really come up in those conversations was the environmental changes set to befall the United States. And by environment, I explicitly mean how the flora of the US will change.

Why? Well, as warmer climates spread north, that means tropical and subtropical plants can follow warmer temperatures northward into lands previously too cold. And they could replace the species native to those lands, who evolved adaptations for their particular climate.

Thankfully, last week the New York Times published a piece that explored how those impacts could be felt. Hardiness zones are a concept designed to tell gardeners when and where to plant certain crops. And while the US Department of Agriculture has a detailed version useful to horticulturists, the National Oceanic and Atmospheric Administration produces a very similar version for the purpose of climate studies. And when you group those hardiness levels by the forecast lowest temperatures in an area, you get this.

More palm trees?
More palm trees?

There you have it, the forecast change to plant zones.

From a design standpoint, I like the idea of the colour shift here. However, where it breaks seems odd. Though it could be more influenced by the underlying classifications than I understand. The split occurs at 0ºF, which is well below freezing. I wonder if the freezing point, 32ºF could have been used instead. I also wonder if adding Celsius units above the same legend could be done to make the piece more accessible to a broader audience.

Otherwise, it’s a nice use of small multiples. And from the editorial design standpoint, I like how the article’s text above the graphic makes use of a six-column layout to add some dynamic contrast to what is essentially a three-column layout for the graphics.

They're living on a grid
They’re living on a grid

Credit for the piece goes to Nadja Popovich.

Natural Disasters

Today’s piece is another piece set against a black background. Today we look at one on natural disasters, created by both weather and geography/geology alike.

The Washington Post mapped a number of different disaster types: flooding, temperature, fire, lightning, earthquakes, &c. and plotted them geographically. Pretty clear patterns emerge pretty quickly. I was torn between which screenshots to share, but ultimately I decided on this one of temperature. (The earthquake and volcano graphic was a very near second.)

Pretty clear where I'd prefer to be…
Pretty clear where I’d prefer to be…

It isn’t complicated. Colder temperatures are in a cool blue and warmer temperatures in a warm red. The brighter the respective colour, the more intense the extreme temperatures. As you all know, I am averse to warm weather and so I will naturally default to living somewhere in the upper Midwest or maybe Maine. It is pretty clear that I will not really countenance moving to the desert southwest or Texas. But places such as Philadelphia, New York, and Washington are squarely in the blacked out or at least very dark grey range of, not super bad.

Credit for the piece goes to Tim Meko.

Dry Heat Is Only Part of California’s Problem

Wildfires continue to burn across in California. One, the Camp Fire in northern California near Chico, has already claimed 77 lives. But why has this fire been so deadly?

FiveThirtyEight explained some of the causes in an article that features a number of charts and graphics. The screenshot below features a scatter plot looking at the temperature and precipitation recorded from winter through autumn every year since 1895.

The evolving California climate
The evolving California climate

The designers did a good job of highlighting the most recent data, separating out 2000 through 2017 with the 2018 data highlighted in a third separate colour. But the really nice part of the chart is the benchmarking done to call out the historic average. Those dotted lines show how over the last nearly two decades, California’s climate has warmed. However, precipitation amounts vary. (Although they have more often tended to be below the long-term average.)

I may have included some annotation in the four quadrants to indicate things like “hotter and drier” or “cooler and wetter”, but I am not convinced they are necessary here. With more esoteric variables on the x- and y-axis they would more likely be helpful than not.

The rest of the piece makes use of a standard fare line chart and then a few maps. Overall, a solid piece to start the week.

Credit for the piece goes to Christie Aschwanden, Anna Maria Barry-Jester, Maggie Koerth-Baker and Ella Koeze.

A Wetter Midwest

Here in Philadelphia, I think yesterday was the first day it had not rained in over a week. Not that everyday was a drenching storm, but at least showers passed through along with some downpours and definitely grey skies. But what about my old home, Chicago?

Well, FiveThirtyEight turned to a longer-term look and examined how over the century the amount of rainfall in the upper Midwest has been increasing. We are actually looking at the same places the Post looked at a few days ago. But instead of political maps, we have rainfall maps.

This one in particular is weird.

Water water everywhere
Water water everywhere

I get why they have the map, to show the geographic distribution of the rain gauges that collect the data. And those are site specific, not statewide. But did the designer have to choose area?

We know that area is a less than ideal way of allowing users to compare data points. And as I just noted, a choropleth, even at say the county level, is out of the question. But what about little squares? Or circles? Could colour have been used to encode the same data instead of size? And then we would likely have fewer overlapping triangles.

I suppose the argument is that the big triangles make a bigger visual impact. But they do so at the cost of comparable data points across the Midwest. Maybe the designer chose the area of triangles because there were too few gauges across the country. I am not sure, but for me the triangles are not quite on point.

That said, the graphics throughout the rest of the article are quite good, especially the opening scatterplots. They are not the sexiest of charts, but they clearly show a trends towards a wetter climate.

Credit for the piece goes to Ella Koeze.

Warmer Winters

Philadelphia is expecting a little bit of snow today, 20 March. We should not be seeing too much accumulate if anything, but still, flakes will likely be in the air this evening. That made me think of this piece from just last week where the New York Times looked at the change in winter temperatures across the United States for the last almost 120 years.

Of course, I would be remiss if I failed to mention that climate change does not mean that temperatures always rise. Instead, while the general average trends upward, the curve flattens out meaning more extreme events on both the hot and the cold parts of the spectrum. (Actually, the New York Times covered this very subject well back in August.)

As a cold weather person, yeah, this isn't great…
As a cold weather person, yeah, this isn’t great…

Anyway, the map from the Times shows how the biggest changes have been recorded in the north of the Plains states. But the same general shift is subject to local conditions, most notably in the southeast where temperatures are actually a lit bit lower.

Credit for the piece goes to Nadja Popovich and Blacki Migliozzi.

Rising Tides, Rising Disasters?

One more day of Harvey-related content. At least I hope. (Who knows? Maybe someone will design a fantastic retrospective graphic?) Today, however, we look at a piece from the Economist about the rising number of weather-related disasters, but thankfully falling numbers of deaths. The piece has all the full suite of graphics: choropleths, line charts, and bar charts (oh my!). But I want to look at the bar chart.

A timeline of disaster causes around the world
A timeline of disaster causes around the world

I cannot tell from this chart whether there has been any change in the individual elements, the meteorological, hydrological, or climatological disasters. And unfortunately stacked bar charts do not let us see that kind of detail. They only really allow us to see total magnitude and the changes in the element at the bottom of the stack, i.e. aligned with the baseline. So I took their chart and drew the shapes as lines and realigned everything to get this.

My take
My take

You can begin to see that meteorological might be overtaking hydrological, but it is too early to tell. And that right now, climatological causes are still far behind the other two.

Credit for the piece goes to the Economist Data Team.

Credit for mine goes to me.

Hot Summer Days

We made it to Friday, folks. So here in Philly it is, of course,  hot and disgusting. (Please refer to Tuesday’s post about the increasingly hot weather in summers.) Thankfully we have ThisIsIndexed to explain what happens in hot weather.

To be fair, I'm not sure misanthropy couldn't be in any other overlap…
To be fair, I’m not sure misanthropy couldn’t be in any other overlap…

Credit for the piece goes to Jessica Hagy.