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.

Different Paths to Density

Yesterday we looked at the expansion of city footprints by sprawl, in modern years largely thanks to the automobile. Today, I want to go back to another article I’ve been saving for a wee bit. This one comes from the Economist, though it dates only back to the beginning of October.

This article looks at the different ways a city can achieve density. Usually one things of soaring skyscrapers, but there are other paths. For those interested, the article is a short read and I won’t cover it here. But for the sake of the graphic below, there are three basic paths: coverage, height, and crowding. Or to put in other terms, how much of the city is covered by homes, how tall those homes go, and how many people fit into each home.

Reticulating splines
Reticulating splines

I really like this graphic. It does a great job of using small multiples to compare and contrast three cities that exemplify the different paths. Notably, it keeps each city footprint at the same scale, making it easier to see things such as why Hong Kong builds skyward. Because it has little land. (It is, after all, an island and the tip of a peninsula.)

One area where I wish the graphic had kept to the small multiples is its display of Minneapolis. There, the scale shifts (note the lines for 5 km below vs. Minneapolis’ 10 km). I think I understand why, because the sprawling city would not have fit within the confines of the graphic, but that would have also hammered home the point of sprawl.

I should also point out that the article begins with a graphic I chose not to screenshot, but that I also really enjoy. It uses small multiples to compare cities density over time, running population on the x-axis and people per hectare on the y-. It is not a perfect graphic (it uses I think unnecessary arrowheads at the end of the line), but scatter plots over time are, I think, an underused graphic to show how two variables (ideally related) have moved in tandem over time.

Overall, this is a strong piece from the Economist.

Credit for the piece goes to the Economist graphics department.

Mapping the Growth of Cities

This is an older piece from back in August, but I was waiting for a time when I would have some related articles to post alongside it. To start off the series of posts, we start with this piece from CityLab. As my titles implies, it looks at the growth of cities, but not in terms of people or technology but in terms of area/land.

The basic premise is that people look for a 30-minute commute and have done so throughout history. To make that point, the authors look at how transport technology evolved to enable people to live and work at further distances from each other, expanding the urban core.

The designer then chose to overlay the city limits of several cities largely defined by these technologies atop each other.

From small, compact, and dense to large, sprawling, and fluffy.
From small, compact, and dense to large, sprawling, and fluffy.

Conceptually the graphic works really well. The screenshot is of an animated. gif leading into the article that step-by-step reveals each city. However, throughout the article, each de facto section is introduced by a city outline graphic.

The graphic does a really nice job of showing how as technology allowed us to move faster, people chose to be further removed from the city core. Of course there are often multiple factors in why people may move out of the core, but transport certainly facilitates it.

Credit for the piece goes to David Montgomery.

Baby You Can Drive My Non-automobile Personal Mode of Transportation

Last week was the climate summit in New York, and the science continues to get worse. Any real substantive progress in fighting climate change will require sacrifices and changes to the way our societies function and are organised, including spatially. Because one area that needs to be addressed is the use of personal automobiles that consume oil and emit, among other things, carbon dioxide. But living without cars is not easy in a society largely designed where they are a necessity.

But over at CityLab, Richard Florida and Charlotta Mellander created an index trying to capture the ability to live without a car. The overall piece is worth a read, but as usual I want to focus on the graphic.

The Northeast is where it's at with its dense cities designed for a pre-automobile era
The Northeast is where it’s at with its dense cities designed for a pre-automobile era

It’s nothing crazy, but it really does shine as a good example of when to use a map. First, I enjoy seeing metro maps of the United States used as choropleths, which is why I’ve made them as part of job at the Philly Fed. CityLab’s map does a good job showing there is a geographic pattern to the location of cities best situated for those trying to live a car-free life. Perhaps not surprisingly, one of the big clusters is the Northeast Corridor, including Philadelphia, which ranks as the 17th best (out of 398) and the 7th best of large metro areas (defined as more than one million people), beating out Chicago, ranked 23rd and 8th, respectively.

Design wise I have two small issues. First, I might quibble with the colour scheme. I’m not sure there is enough differentiation between the pink and light orange. A very light orange could have perhaps been a better choice. Though I am sympathetic to the need to keep that lowest bin separate from the grey.

Secondly, with the legend, because the index is a construct, I might have included some secondary labelling to help the reader understand what the numbers mean. Perhaps an arrow and some text saying something like “Easier car-free living”. Once you have read the text, it makes sense. However, viewing the graphic in isolation might not be as clear as it could be with that labelling.

Credit for the piece goes to David Montgomery.

Congressional District Population Density

Tomorrow is Election Day here in the United States and this morning I wanted to look at a piece I’ve had in mind on doing from City Lab. I held off because it looks at the election and what better time to do it than right before the election.

Specifically, the article looks at the density of the different congressional districts across the United States. Whilst education level appears to be the most predictive attribute of today’s political climate—broadly speaking those with higher levels of formal education support the Democrats and those with lower or without tend to support President Trump—the growing urban–rural divide also works. But what about the in-between? The suburbs? The exurbs? And how do we then classify the congressional districts that include those lands.

For that purpose City Lab created its City Lab Congressional Density Index. Very simplistically it scores districts based on their mixture of low- to medium- to high-density neighbourhoods. But visually, which is where this blog is concerned, we get maps with six bins from pure urban to pure rural and all the mixtures in-between. This cartogram will show you.

All the urban and rural seats
All the urban and rural seats

Now, there are a couple of things I probably would have done differently in terms of the visualisation. But the more I look at this, one of those things would not be to design the hexagons to all fit together nicely. Instead, you get this giant gap right where the plains states begin west of the Mississippi River stretching through the Rockies over to California. If you think about it, however, that is a fairly accurate description of the population distribution of the United States. With a few exceptions, e.g. Denver, there are not many people living in that space. Four geographically enormous states—North Dakota, South Dakota, Montana, and Wyoming—have only one congressional district. Idaho has two. Nebraska three. And then Iowa and Kansas four. So why shouldn’t a map of the United States display the plains and Rocky Mountain interior as a giant people hole?

Like I said, initially I took umbrage at that design decision, but the more I thought about it, the more it made sense. But there are a few others with which I quibble. The labelling here is a big one. First, we have the district labels. They are small, because they have to be to fit within the five hexagons that define the districts’ shapes. But every label is black. Unfortunately, that makes it difficult to read the labels on the darker colours, most notably the dark purple. I probably would have switched out the black labels in those instances for white ones.

But then the state labels are white with black outlines, which makes it difficult to read on either dark or light backgrounds. The designer made the right decision in making the labels larger than the districts, but they need to be legible. For example, the labels of Alaska and Hawaii need not be white with black outlines. They could just be set in black type to be legible. Conversely, Florida’s, sitting atop darker purple districts, could be made white.

The piece makes use of more standard geographic map divided into congressional districts—the type you will see a lot tomorrow night. And it makes use of bar charts to describe the demographics of the various density types. I like the decision there to use a new colour to fill in the bars. They use a dark green because it can cut across each of the six types.

Credit for the piece goes to David H. Montgomery.

Mapping All the Buildings

I wish I had more for this post. Saturday morning’s New York Times was delivered with this on the front page, above the fold. It promised a special section including graphics that showed every building in the United States with a pullout poster of a large major city.

I just wanted to see more…
I just wanted to see more…

I have been through my Sunday paper twice now and cannot find the maps. So while I would love to see the full work, and then probably share a bit of it with all of you, I cannot. Instead, we can only look at the above. Even there though, you can begin to get a sense of the different types of spatial arrangements our cities exhibit.

Credit for the piece goes to the New York Times graphics department.

Most Liveable Cities Ranking

There is nothing super sophisticated in these charts, but I love them all the same. The Economist Intelligence Unit (EIU) published its rankings of the world’s most liveable cities and this year Vienna knocked off Melbourne for top spot. But what about the rest of the list?

Thankfully the Economist, a related company, put together a graphic highlighting important or noteworthy cities among the entire dataset. It is a wonderful tangle of light grey lines that have select cities highlighted in thicker strokes and brighter colours. Labelling each city would be too tricky at this scale.

I'm okay with the occasional rainbow spaghetti

I’m okay with the occasional rainbow spaghettiThat said about labelling each city, a few years back I worked on a similar top cities in a category datagraphic for Euromonitor International. We took a similar approach and coloured lines by region, but we presented the entire dataset and then complemented it by some additional charts to the side.

These were always fun pieces on which to work
These were always fun pieces on which to work

What is really nice about the Economist piece, however, is that they opted not to show the whole dataset. This could be a business decision, if people want to find where a particular city they could be persuaded to either outright subscribe or otherwise provide contact information in exchange for access to the data. Either way, the result is a piece that has space to provide textual context about why cities rose or fell over the years.

I think I like these types of pieces because there is so much to glean from getting lost in the chart. And this one from the Economist does not disappoint.

Credit for the liveability piece goes to the Economist Data Team.

Credit for the destinations piece goes to me.

Chinese Urban Clusters

Yesterday the Economist posted a graphic about Chinese urban clusters, of which the Chinese government is planning to create 19 as part of a development strategy. In terms of design, though, I saw it and said, “I remember doing something like that several years ago”.

The Economist piece looks at just the geography of the Chinese clusters. It highlights three in particular it discusses within the article while providing population numbers for those clusters. Spoiler: they are large.

The Economist graphic does little else beyond labelling the cities and the highlighting of the three features clusters. But that is perfectly okay, because that was probably all the graphic was required to do. I am actually impressed that they were able to label every city on the map. As you will see, we quickly abandoned that design idea.

The Chinese government's new urban cluster plan
The Chinese government’s new urban cluster plan

So back in 2015, using 2014 data, my team worked on a series of graphics for a Euromonitor International white paper on Chinese cities. The clusters that the analysts identified, however, were just that, ones identified by researchers. Since the Chinese government had not yet created this new plan.

We added some context to our cluster map
We added some context to our cluster map

We also looked at more cities and added some vital context to the cluster map by working to identify the prospects of the various Chinese provinces. Don’t ask me what went into that metric, though, since I forget. The challenge, however, was identifying the four different tiers of Chinese city and then differentiating between the three different cluster types while overlaying that on a choropleth. Then we added a series of small multiples to show how now all provinces are alike despite having similar numbers of cities.

Credit for the Economist piece goes to the Economist Data Team.

Credit for the Euromonitor piece is mine. I would gladly give a shoutout to those that worked with me on that project…but it’s been so long I forget. But I’m almost certain both Lindsey Tom and Ciana Frenze helped out, if not on that graphic, on other parts of the project.

The Disappearing Urban Middle Class

Today we look at income in American cities and in particular the middle class disappearance. The Guardian published the graphics, but they originate with Metrocosm, LTDB at Brown, and IPUMS National Historical Geographic Information System. So what are we looking at? Well, the big one is a set of small multiples of cities and their income breakdowns as percentages of city census tracts. This screenshot is static, but the original is an animated .gif.

The flattening of the curve
The flattening of the curve

I have a few issues with the design of the graphic, the most important of which is the colour palette. If the goal is to focus on the decline of the middle class—and I admit that may be the point of the Guardian’s authors and not the original authors—why are the most visually striking colours at the top of the income distribution. Instead, you would want to draw attention to the middle of each chart, not the right. And if the idea was that the darker colours represent the higher income groups, well the positioning of each bar on the chart and the axis labelling does that already. After all, if anything, the story is that in a number of cities the middle class has shrunk while the lower income groups have grown. And you can barely see that with the lower income groups coloured yellow.

My other issues are more minor design things such as the city labelling. I kept reading the label as being below the bars, not above as it actually is.

And then I wonder if a different chart form would be more effective at showing the decline in the middle class. Perhaps a line chart plotting the beginning and end points for each cohort?

Then the piece gets into some three-dimensional maps that you can spin and rotate.

Just stop
Just stop

Yeah. Shall I count the ways? A more conventional choropleth would have served the purpose far more effectively. The dimensionality hides lower income tracts behind higher ones. The solution? Allow the user to rotate and spin the map? No, get rid of the dimensionality. It offers little to the understanding of the underlying data. Not to mention, are the areas of shadows shadows? Or are they another bin or cohort of income?

And then you have to read the piece to get a fuller understanding of my criticism.

But don’t worry, I can quote it.

Chicago was largely successful transitioning away from manufacturing to a service-based economy. This shift is evident in the bifurcated pattern present in 2015 – a heavy concentration of wealth in the business/financial district and marked decline in the surrounding area.

Those of you who read this blog from Chicago or who have lived in Chicago will pick up on it. The rest of you not so much. The concentration of wealth is not located in the business/financial district. Those dark red skyscrapers are not actual skyscrapers, they are census tracts located not in the financial district, but the areas of River North, Old Town, Gold Coast, &c. Thinking of the issue more logically, yes incomes are up in cities that are doing well. But how many of those very wealthy live on the same block as their office? Not many. Your higher income is going to be concentrated in residential or mixed-residential neighbourhoods near, but not in the business/financial district.

The data behind this work fascinates me. I just wish the final graphics had been designed with a bit more consideration for the data and the stories therein. And a little bit of proper understanding of the cities and their geography would help the text.

Credit for the piece goes to Metrocosm, LTDB at Brown University, and IPUMS National Historical Geographic Information System.