Americans Can’t Kick the Auto Habit

After looking this week at the growth of the physical size of cities due to improvements in transport technologies, the increasing density of cities, and then the contribution of automobile (especially personal cars) to carbon dioxide emissions, today we look at a piece from the Transport Politic comparing US and French mass transit ridership to see whether the recent decline in US ridership is inevitable or a choice made by consumers and policymakers. Spoiler: it’s not inevitable.

The article makes use of a few graphics and an interactive component. The lead-in graphic is a nice line chart that runs with the spaghetti nature of the graphic: lots of line but only two are really highlighted.

The French are definitely better than the US here.
The French are definitely better than the US here.

Light grey lines and light blue lines encode the US and French cities under study. But only the lines representing the averages of both the US and France are darkly coloured and in a thicker stroke to stand out from the rest. Normally I would not prefer the minimum of the y-axis being 50%, but here the baseline is actually 100% so the chart really works well. And interestingly it shows that prior to the Great Recession, the United States was doing better than France in adoption of mass transit, relative to 2010 numbers.

But then when you directly compare 2010 to 2018 for various US and French cities, you get an even better chart. Also you see that French cities reclaim the lead in transit growth.

A lot of declines on this side of the pond.
A lot of declines on this side of the pond.

These two static graphics, which can each be clicked to view larger, do a really great job of cutting through what some might call noise of the intervening years. I do like, much like yesterday’s post, the comparison of total or aggregate ridership to per capita numbers. It shows how even though New York’s total ridership has increased, the population has increased faster than the ridership numbers and so per capita ridership has declined. And of course as yesterday’s post examined, in the States the key to fighting climate change is reducing the number of people driving.

What I cannot quite figure out from the graphic is what the colouration of the lines mean. I thought that perhaps the black vs. grey lines meant the largest cities, but then LA would be black. Maybe for the steepest declines, but no, because both LA and Boston are grey. I also thought the grey lines might be used when black lines overlap to aid clarity, but then why is Boston in grey? Regardless, I like the choice of the overall form.

But where things go really downhill are the interactive charts.

Just what?
Just what?

Talk about unintelligible spaghetti charts. So the good. The designer kept the baseline at 100% and set the min and max around that. After that it’s a mess. Even if the colours all default to the rainbow, the ability to select and isolate a particular city would be incredibly valuable to the user. Unfortunately selecting a city does no such thing. All the other cities remain coloured, and sometimes layered atop the selected city.

I would have thrown the unselected cities into the greyscale and let the selected city rise to the top layer and remain in its colour. Let it be the focus of the user’s attention.

Or the designer could have kept to the idea in the first graphic and coloured American cities grey and French cities light blue and then let the user select one from among the set and compare that to the overall greyed/blued masses and the US and French averages.

Overall, it wasn’t a bad piece. But that final interactive bit was questionable. Unfortunately the piece started strong and ended weak, when the reverse would have been preferable.

Credit for the piece goes to Yonah Freemark.

Auto Emissions Stuck in High Gear

The last two days we looked at densification in cities and how the physical size of cities grew in response to the development of transport technologies, most notably the automobile. Today we look at a New York Times article showing the growth of automobile emissions and the problem they pose for combating the greenhouse gas side of climate change.

The article is well worth a read. It shows just how problematic the auto-centric American culture is to the goal of combating climate change. The key paragraph for me occurs towards the end of the article:

Meaningfully lowering emissions from driving requires both technological and behavioral change, said Deb Niemeier, a professor of civil and environmental engineering at the University of Maryland. Fundamentally, you need to make vehicles pollute less, make people drive less, or both, she said.

Of course the key to that is probably in the range of both.

The star of the piece is the map showing the carbon dioxide emissions on the roads from passenger and freight traffic. Spoiler: not good.

From this I blame the Schuylkill, Rte 202, the Blue Route, I-95, and just all the highways
From this I blame the Schuylkill, Rte 202, the Blue Route, I-95, and just all the highways

Each MSA is outlined in black and is selectable. The designers chose well by setting the state borders in a light grey to differentiate them from when the MSA crosses state lines, as the Philadelphia one does, encompassing parts of Pennsylvania, New Jersey, Delaware, and Maryland. A slight opacity appears when the user mouses over the MSA. Additionally a little box remains up once the MSA is selected to show the region’s key datapoints: the aggregate increase and the per capita increase. Again, for Philly, not good. But it could be worse. Phoenix, which surpassed Philadelphia proper in population, has seen its total emissions grow 291%, its per capita growth at 86%. My only gripe is that I wish I could see the entire US map in one view.

The piece also includes some nice charts showing how automobile emissions compare to other sources. Yet another spoiler: not good.

I've got it: wind-powered cars with solar panels on the bonnet.
I’ve got it: wind-powered cars with solar panels on the bonnet.

Since 1990, automobile emissions have surpassed both industry emissions and more recently electrical generation emissions (think shuttered coal plants). Here what I would have really enjoyed is for the share of auto emissions to be treated like that share of total emissions. That is, the line chart does a great job showing how auto emissions have surpassed all other sources. But the stacked chart does not do as great a job. The user can sort of see how passenger vehicles have plateaued, but have yet to decline whereas lorries have increased in recent years. (I would suspect due to increased deliveries of online-ordered goods, but that is pure speculation.) But a line chart would show that a little bit more clearly.

Finally, we have a larger line chart that plots each city’s emissions. As with the map, the key thing here is the aggregate vs. per capita numbers. When one continues to scroll through, the lines all change.

Lots of people means lots of emissions.
Lots of people means lots of emissions.
There's driving in the Philadelphia area, but it's not as bad as it could be.
There’s driving in the Philadelphia area, but it’s not as bad as it could be.

Very quickly one can see how large cities like New York have large aggregate emissions because millions of people live there. But then at a per capita level, the less dense, more sprawl-y cities tend to shoot up the list as they are generally more car dependent.

Credit for the piece goes to Nadja Popovich and Denise Lu.

The Amazon Burns

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
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…

Credit for the piece goes to InfoAmazonia.org.

Hotter Muggier Faster

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 Southeast is an anomaly, but its warming has accelerated since the 1960s
The Southeast is an anomaly, but its warming has accelerated since the 1960s

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.

This ain't so good
This ain’t so good

But, not to leave out Chicago as I did last week, Cook County, Illinois is right on line with the US average.

Nor is this, but it's average
Nor is this, but it’s average

But the big cities on the West Coast look very unattractive.

Tinseltown is out of the question
Tinseltown is out of the question

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.

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

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.

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.

British English vs. Irish English

The United Kingdom is known for having a large number of accents in a—compared to the United States—relatively small space. But then you add in Ireland and you have an entirely new level of linguistic diversity. Josh Katz, who several years ago made waves for his work on the differences in the States, completed some work for the New York Times on those differences between the UK and Ireland.

You might know this as tag. At least I do.
You might know this as tag. At least I do.

Why do I bring it up? Well, your author is going on holiday again, this time back to London. I will be maybe taking some day trips to places outside the capital and maybe I will confirm some of these findings. But if you want, you can take the quiz and see where you fall compared to Katz’s findings.

And it does pretty well. It identified me as being clearly not from the British Isles.

Maybe I'm secretly Cornish?
Maybe I’m secretly Cornish?

But depending upon how you answer a particular question, the article will show you how your answer compares.  Let’s take my answer for scone. In that, I am more Irish.

Or you can just call them fantastic and delicious.
Or you can just call them fantastic and delicious.

Credit for the piece goes to Josh Katz.