Many of us have pent up travel demand. Covid-19 remains with us, lingering in the background, but it’s largely from our front-of-mind. For those of my readers in Europe, or just curious how superior European rail infrastructure is over American, this piece from Benjamin Td provides some useful information.
It uses isochrones to map out how much a traveller could travel if he would travel five hours. For this screenshot I chose London’s King’s Cross station. In red we see distances within a one-hour rail ride from said station. In the lightest yellow are those places within the five-hour distance.
The interactive map allows users to investigate stations throughout Europe. Mousing over various parts brings up different stations. Clicking on the station freezes the station on the map allowing the user to zoom in or out and investigate different areas of Europe.
Colour-wise, things work well. The desaturated map allows the yellow-to-red palette to shine. And to the right a closable legend, which unfortunately cannot be reopened once closed for the only real blemish on the piece. Even typographically, the labels appear in grey whereas selected stations appear in black.
I took two weeks off as work was pretty crazy, but we’re back to covering data visualisation and design with a graphic about trains. And anybody who knows me knows how I love trains. One of the early acts of the Biden administration was funding a proper expansion of rail service in the United States.
Last week the Washington Post published an article that explored some of the difficulties Amtrak, the national rail company, faces in that expansion. Most of it has to deal with the fact that outside the Northeast Amtrak largely uses rail lines owned by freight companies.
The article uses a map to show Amtrak routes and, in particular, where Amtrak wants to increase service or create new service.
As far as the map goes, it does a nice job needing not to reinvent the wheel. When an existing route will have expanded service, e.g. the Northeast Corridor, the blue line sits next to the dotted white line. What remains a bit unclear to me is the use of black text for Chicago, Atlanta, Dallas, and Los Angeles. The bold type for New Orleans and Mobile makes sense because of the story’s focus on that particular route. Chicago is mentioned once, but Dallas is not. So that is unclear.
But what really stood out to me was what happened when I re-read the story on my mobile. The graphic split from a full map to three narrow graphics, each featuring 1/3 of the United States. The designers moved the text labels so that they are fully visible in each graphic.
Overall, the piece does a great job at showing the map, but in particular it shines when it swaps out the large map for the smaller graphics on small screens. And the attention to detail in moving the text labels makes it all the better.
Happy Friday, everyone. I prefer to travel via Amtrak and intercity rail, but from my flat I can see two routes of the US interstate highway system: I-676 and I-76. And when I drive to my hometown outside Philadelphia, I use those two routes. Plus, I live not far from I-95, the main highway corridor running through the East Coast of the United States.
But what a lot of people do not know is that the numbering system for US interstate highways—by and large—has an underlying principle. (I say by and large because I frequently drive on one of the most infamous exceptions.) Thankfully, the YouTuber CCP Grey just released a video that details the numbering system.
And if you’re curious about that exception, which runs through Altoona, Pennsylvania, here’s a screenshot. But you should watch the full video.
The alliteration failed at that last word, but it gets the point across. No mater how you may want to define infrastructure, the term always includes transit. In the Boston Globe, an opinion piece proposed how the city and region of Boston could improve upon the city’s mass transit options.
And they made a map.
The map is an interesting one. It uses thick purple lines to indicate the commuter rail branches—not the metro/subway lines. The problem is that the outside of those lines then encodes the suggested improvements. An orange outline indicates where tracks should be electrified—Boston still uses diesel engines for some of its commuter rail transit. But the problem is that the dark purple dominates the graphic. If, however, the purple were entirely replaced by an orange line, it would be clearer that the Providence needs electrification. (It’s actually already electrified, as that’s the same line Amtrak uses, but Boston’s transit service still uses diesel engines on the line.)
Similarly, the key to indicate upgraded tracks and signals is a blue line of similar “colour” to the purple. That makes it hard to distinguish between the two, especially when next to the green inline option, representing increased speeds.
The key flaw? A long-time wish for Boston transit lovers (or haters). Note how the system is divided into two, the two main hubs, South Station and North Station, do not connect. Connecting the two will require billions of dollars. But the benefits can be tremendous.
Philadelphia, for example, for decades had two rail hubs: Broad Street Station across from City Hall and Reading Terminal several blocks east along Market Street. Reading Terminal was the terminus for the Reading Railroad and Broad Street Station for the Pennsy, or Pennsylvania Railroad. In 1930, Broad Street Station was replaced by an underground station, today’s Suburban Station. But it would not be until 1984 when rail tunnels would finally be opened linking the western/southern Pennsylvania Railroad lines to the northern lines of Reading. But today you can take a train from a southwest suburb to the far northern suburbs without changing trains because of that connection.
For those of my readers who live in a city where the subway or underground is a great means of getting around the city, you know you really miss that late Saturday night/early Sunday morning bouquet in the air. Though as this New York Times piece explains, sure it smells bad, but that air is probably safer than you dining indoors at a restaurant or even a child attending class in person.
The piece focuses on New York City subway cars, but they are very similar to the rest of the stock used in the United States. It uses a scrolling reveal to show how the air circulation and filtration systems work. Then it concludes with a model of how a person sneezing appears, both with and without a mask. (Spoiler, wear a mask.)
It’s a really nicely done and informative piece. It compares the rate of air recycled in a subway car to that of several other locations, and the results were a bit surprising to me. Of course, early on in the pandemic before we began to fully understand it, the threat was thought to be from contaminated surfaces—and let’s be honest, there are a lot of contaminated surfaces in a New York City subway car—but we now know the real risk is particles breathed/coughed/sneezed out from one’s mouth and nose. And we can now see just how efficient subways are at cycling and filtering that air.
Credit for the piece goes to Mika Gröndahl, Christina Goldbaum, and Jeremy White.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This afternoon I am off on a flight to Austin, Texas for a friend’s wedding in nearby Kyle, Texas. Two years in a row I’ve been to Texas in October. And so that felt like a good enough reason to update my counties visited map that, according to my files, I haven’t updated since 2015.
In those four years, before I moved from Chicago to Philadelphia, I explored Wisconsin for genealogy purposes. Then after said move, I have visited Las Vegas for bachelor party—now the furthest west in the United States I have ever visited. And work trips have sent me to St. Louis and Dallas, the former of which allowed me a nice train ride from St. Louis to Chicago across central Illinois. I have also done some genealogy research up in western New York bookending a bachelor party to the Finger Lakes.
At a state level that makes 23 states visited plus two through which I’ve travelled (Connecticut and Rhode Island). Plus I’ve visited DC. Almost halfway there to visiting half the United States.
With the wedding Saturday, I am on holiday Friday. Plus, Monday is a bank holiday and so I will be posting again from Tuesday.
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.
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.