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.

How Mass Shootings Have Changed

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.

Getting worse over time
Getting worse over time

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.

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.

Water, Water Everywhere Nor Any Drop to Drink

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
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
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
It helps having Lake Michigan right there

Credit for the piece goes to Somini Sengupta and Weiyi Cai.

United in Gun Control

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.

These trendlines are heading in the right direction
These trendlines are heading in the right direction

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.

Quantifying Part of the Opioid Crisis

Two weeks ago the Washington Post published a fascinating article detailing the prescription painkiller market in the United States. The Drug Enforcement Administration made the database available to the public and the Post created graphics to explore the top-line data. But the Post then went further and provided a tool allowing users to explore the data for their own home counties.

The top line data visualisation is what you would expect: choropleth maps showing the prescription and death rates. This article is a great example of when maps tell stories. Here you can clearly see that the heaviest hit areas of the crisis were Appalachia. Though that is not to say other states were not ravaged by the crisis.

There are some clear geographic patterns to see here
There are some clear geographic patterns to see here

For me, however, the true gem in this piece is the tool allowing you the user to find information on your county. Because the data is granular down to county-level information on things like pill shipments from manufacturer to distributor, we can see which pharmacies were receiving the most pills. And, crucially, which manufacturers were flooding the markets. For this screenshot I looked at Philadelphia, though I only moved here in 2016, well after the date range for this data set.

It could be worse
It could be worse

You can clearly see, however, the designers chose simple bar charts to show the top-five. I don’t know if the exact numbers are helpful next to the bars. Visually, it becomes a quick mess of greys, blacks, and burgundies. A quieter approach may have allowed the bars to really shine while leaving the numbers, seemingly down to the tens, for tables. I also cannot figure out why, typographically, the pharmacies are listed in all capitals.

But the because I lived in Chicago for most of the crisis, here is the screenshot for Cook County. Of course, for those not from Chicago, it should be pointed out that Chicago is only a portion of Cook County, there are other small towns there. And some of Chicago is within DuPage County. But, still, this is pretty close.

Better numbers than Philly
Better numbers than Philly

In an unrelated note, the bar charts here do a nice job of showing the market concentration or market power of particular companies. Compare the dominance of Walgreens as a distributor in Cook County compared to McKesson in Philadelphia. Though that same chart also shows how corporate structures can obscure information. I was never far from a big Walgreens sign in Chicago, but I have never seen a McKesson Corporation logo flying outside a pharmacy here in Philadelphia.

Lastly, the neat thing about this tool is that the user can opt to download an image of the top-five chart. I am not sure how useful that bit is. But as a designer, I do like having that functionality available. This is for Pennsylvania as a whole.

For Pennsylvania, state-wide
For Pennsylvania, state-wide

Credit for the piece goes to Armand Emamdjomeh, Kevin Schaul, Jake Crump and Chris Alcantara.

Baby, It’s Hot Outside

Those of you living on the East Coast, specifically the Mid-Atlantic, know that presently the weather is quite warm outside. As in levels of dangerous heat and humidity. Personally, your author has not left his flat in a few days now because it is so bad.

Alas, not everyone has access to air conditioning in his or her abode. Consequently, they need to look to public spaces with air conditioning. Usually that means libraries or public buildings. But here in Philadelphia, have people considered the subway?

Billy Penn investigated the temperatures in Philadelphia’s subsurface stations along the Broad Street and Market–Frankford Lines—Philadelphia’s third and oft-forgot line, the Patco, was untested. What they found is that temperatures in the stations were significantly below the temperatures above ground. The Market–Frankford stations, for example, were less than 100ºF.

Just explore the rails…
Just explore the rails…

Of course that misses the 2nd Street station in Old City, but otherwise picks up all the Market–Frankford stations situated underground.

Then there is the Broad Street Line.

More rail riding…
More rail riding…

Here, I do have a question about why the line wasn’t investigated from north to south. It ran only as far north as Girard, stopping well short of north Philadelphia neighbourhoods, and then as far south as Snyder, missing both Oregon and Pattison (sorry, corporately branded AT&T) stations. The robustness of the dataset is a bit worrying.

The colours here too mean nothing. Instead blue is used for the blue-coloured Market–Frankford line and orange for the orange-coloured Broad Street line. (The Patco line would have been red.) Here was a missed opportunity to encode temperature data along the route.

Finally, if the sidewalk temperatures were measured at each station, I would want to see that data alongside and perhaps run some comparisons.

This is an interesting story, but some more exploration and visualisation of the data could have taken it to the next level.

Credit for the piece goes to Danya Henninger.

The Rent Is Too Bloody High

This is a graphic from the Guardian that sort of mystified me at first. The article it supports details how the rising rents across England are hurting the rural youth so much so they elect to stay in their small towns instead of moving to the big city.

But all those segments?
But all those segments?

The first thing I noticed is that there really is no description of the data. We have a chart looking at something from 1997 and comparing it to 2018. The title is more of a sentence describing the first pair of bars. And from that title we can infer that these bars are income changes for the specified move, e.g. Sunderland to York, for the specified year. But a casual reader might not pick up on that casual description.

Then we have the issue of the bars themselves. What sort of range are we looking at? What is the min? The max? That too is implied by the data presented in the bars. Well, technically not the bars, but in the numbers at the end of each bar. I will spare you the usual rant about numbers in graphics defeating the purpose of graphics and organisation vs. visual relationship. Instead, the numbers here are essential because we can use them to suss out the scale of the grey bars. After looking at a few bars, we can tell that the white lines separating the grey boxes are most likely 10% increments. And from that we can gather the minimum is about -40% and the maximum 100%. But instead of making the reader work to figure this out, would not some min/max labels at the bottom of the chart be far clearer?

And then there is the issue of the grey boxes/bars themselves. Why are they there in the first place? If the dataset were more about an unmet value, say reservoirs in towns were only at x% of capacity, the grey bars could relate the overall capacity and the coloured bars the actual values. But here, income is not a capacity or similar type of value. It could expand well beyond the 100% or decline beyond the -40%. These bars imply the values are trapped within these ranges. I would instead drop the grey bars entirely and let the coloured bars exist on their own.

Overall this is a confusing graphic for a fascinating article. I wish the graphic had been a little bit clearer.

Credit for the piece goes to the Guardian’s graphics department.

The Ebola Outbreak in the Congo

Ebola, which killed 11,000 people in West Africa in 2014 (which I covered in a couple of different posts), is back and this time ravaging the Congo region, specifically the Democratic Republic of the Congo (DRC). The BBC published an article looking at the outbreak, which at 1,400 deaths is still far short of the West Africa outbreak, but is still very significant.

That's looking like a tenuous border right now…
That’s looking like a tenuous border right now…

The piece uses a small multiples of choropleths for western Congo. The map is effective, using white as the background for the no case districts. However, I wonder, would be more telling if it were cases per month? That would allow the user to see to where the outbreak is spreading as well as getting a sense of if the outbreak is accelerating or decelerating.

The rest of the article features four other graphics. One is a line chart that also looks at cumulative cases and deaths. And again, that makes it more difficult to see if the outbreak is slowing or speeding up. Another is how the virus works and then two are about dealing with the virus in terms of suits and the containment camps. But those are graphics the BBC has previously produced, one of which is in the above links.

Credit for the piece goes to the BBC graphics department.