Those who know me know one of my pet peeves are when maps of the United States do not display Alaska and Hawaii. I even noted yesterday that those two states were so late of additions to the United States and it made sense as to why they were not included.
So when I was going through some old photos yesterday, I stumbled across this of a poster on the Philadelphia subway system. I had flagged it for posting, but I guess I never did.
I understand this is an advert and so for creative purposes, creative liberty. And it could be that this service does not exist in either Alaska or Hawaii.
But, the statement here is that Metro covers 99% of the United States. Geographically, to do so Metro must cover Alaska because in terms of land area, Alaska comprises nearly 18% of the entire United States. Yeah, Alaska is big. Now, if you’re talking covering 99% of the people of the United States, Metro has some wiggle room. Combined, both Alaska and Hawaii comprise 0.6% of the United States population. That would still leave 0.4% of the American population not covered, and by definition that must be some part of the contiguous lower 48. But above we can see the whole map is purple.
In other words, this is not an accurate map. They should have found some way of incorporating Alaska and Hawaii.
Credit for the piece goes to Metro’s designer or design agency.
I spent the better part of the last two weeks travelling and hanging out in the Berkshires and Connecticut River Valley in western Massachusetts. One of the coolest experiences was driving up the automobile route for Mt Greylock, the tallest point in Massachusetts.
Most of the drive itself was just regularly spectacular as the mid-morning sunlight hit the trees above the road, creating a warm yellow-orange light that bathed the route. But maybe about halfway or two-thirds of the way up, I rounded a bend in the road and came upon a clearing—and convenient pullover. The scene elicited an audible swear and not surprisingly I stopped the car to enjoy the scenery and take some photos.
Whilst there, I also noticed a small sign that, among other things diagrammed the cross section of Mt Greylock and points to the east and west. And I figured that would be a good way to start the week.
The sign uses an old map to illustrate the different rock layers that define the mountain. Marble, which is a soft rock, erodes during glaciation whereas schist, a hard rock, does not. And during the recent ice ages, when glaciers covered the area, most of the marble areas of the mountain range were eroded away, leaving just the sharp stony peaks of schist.
Credit for the piece goes to the US Geological Survey designers, ca. 1894.
Yesterday I mentioned more about revolutions, well today we’re talking about Mars, a planet that revolves around the Sun. Late last week scientists working with the InSight lander on the Red Planet published their findings. Turns out we need to rethink what we know about Mars.
First, the planet is probably much older than Earth. Mars’ composition also differs from Earth in some significant ways. InSight mapped the interior of Mars by studying the seismic waves (think like sound waves but through the inside of planets) of marsquakes.
The Wall Street Journal published a great article spelling out the findings in detail that is well worth the read. It also included some nice graphics helping to support the piece. The one I wanted to highlight, however, was a brilliant comparison of Mars to Earth.
Conceptually this is important, because many diagrams and graphics I’ve seen about these findings only detail the interior of Mars. But what makes Mars important is how it differs from Earth, and let’s be honest, how many of us remember our Earth science classes at school and can diagram out the interior of Earth?
And right here the designer compares the smaller—and now older—brother of Earth. Again, read the article for the details, but in short, some of the key findings are that the core is larger, but also lighter, than we thought. Our planet’s core differs because Earth has two parts: a solid and heavy ball of iron and nickel surrounded by a liquid core that spins. That spinning core creates the magnetic fields that protect our planet from the Sun and have kept our atmosphere intact. Mars doesn’t have that. And that’s in part because, given the core is larger than we thought, the mantle is smaller.
A smaller mantle means that certain materials couldn’t form that insulate the Earth’s core. So while Earth’s core has been prevented from cooling and slowing down, Mars was not. And so while it did have a magnetic field at one point, that slowing, cooling core slowly dissipated the magnetic field. That may be why the planet, once rich in water, now is a barren rock exposed to the Sun.
Again, this is a big deal in terms of planetary science. Consider that Earth and Mars are broadly made of the same materials that orbited the Sun billions of years ago. But Mars took those same ingredients and made itself into a very different planet. And now we know quite a good deal more about the Red Planet.
One last point to make about the graphic above. Again, many illustrations will increase the size of the crust to make it more visible. Here the designer chose to keep it more in proportion to the scale of the planets’ interiors. (Even though Mars’ crust is quite a bit thicker than Earth’s.) I think that’s important because it puts us into perspective. We can build monuments like the Pyramids that last thousands of years and dig bore holes miles deep and tunnel out connections through mountain ranges, but that also just scratches the surface of the crust. But that crust is the thinnest of shells over the mantle and cores of these planets.
That life began and took hold on Earth, on that thinnest of shells protected by a magnetic field because of a spinning molten core buried at the centre of the planet…something to think about sometimes.
No two rivers are the same, though they certainly can be similar. Rivers have their own ecosystems and when I was at school, I learned of the different classifications of rivers by the colour of their water: black, white, and clear. Broadly speaking, that just means the amount of sediment dissolved in the river’s water. Black colours appear when slow moving water has absorbed lots from its environment, think swamps. White waters resemble tea or coffee with added milk or cream. This happens when sediments enter and dissolved into the water. Clear water is that, relatively clear and free of sediment.
But a team of scientists at University of North Carolina at Chapel Hill (UNC Chapel Hill) recently released some work where they used shifts in blue to yellow and green to help classify rivers. Their classification differs, but broadly can point to a change from healthy (blue) to unhealthy (yellow and green). The novelty in their work, however, focuses on using satellite imagery to capture the colour of rivers and their evolution since the mid 1980s.
They published their findings as an interactive application driven primarily by a clickable map. Clearly not all rivers are available, but a large number are, and you can see some obvious patterns at a national scale—their work excludes Alaska and Hawaii. If blue represents healthy rivers, we see healthy rivers in New England and the Pacific Northwest with a host of green rivers in the Mid-Atlantic and Upper Midwest with yellow in the Mississippi basin and southeast.
I wanted to look at Pennsylvania a bit more specifically given my familiarity with the Commonwealth and zoomed in a bit on the map.
You can see that using that above scale, Pennsylvania’s rivers are in okay, not great state. Some of the upper stretches of the Delaware and Susquehanna Rivers are coloured blue, but we mostly see a lot of green.
To the right of the map, the designers placed three smaller charts driven by the user’s selection of river. Let’s take a look at the Juniata River as an example—my grandfather grew up living alongside a tributary that emptied into the Frankstown Branch just a short walk from his house.
We can see that the chart on the upper right shows the colour shift over the decades for that observed section of the river. The legend provides the information that the section of the river has shifting blue—gotten healthier—and then below it looks for any seasonal changes. Here the chart is grey, indicating the system lacks enough data for a clear trend. This examines the short changes we might see in a river based on seasonal effects like rainy season, dry season, and human-driven effects—perhaps we pollute more in the spring and then use rivers recreationally in the summer.
Finally a distribution of the river section’s colour, all in wavelengths of light.
My biggest critique here would be the wavelengths. Users likely will not the colour spectrum by wavelength, and adding some labels like blue, yellow, and green could go a long ways to help users understand at what they are looking.
Overall, though, this is a really fascinating project.
Last Friday I shared an xkcd post about the relative smoothness of the Earth. This week he posted an illustration but a slightly different scale. You can see more of Earth’s jagged edges.
Gotta love the Star Trek reference. I’m betting he used the length of the Kelvin timeline Enterprise, which I personally dislike, as it’s significantly larger than the prime timeline Enterprise of Shatner and Nimoy.
At scale. Not quite as smooth as a billiards ball, as is often claimed. But still, with the majority of the Earth’s surface covered by water, the highest mountains of Everest and K2 make for mere fractions of differences in height relative to the Earth’s size.
But that did not stop xkcd from making a scale model of Earth.
I’ve largely been busy creating and posting content on the Covid pandemic and its impact on the Pennsylvania, New Jersey, and Delaware tristate area along with, by request, both Virginia, and Illinois, my former home. It leaves me very little time for blogging, and I really do not want this site to become a blog of my personal work. That’s why I have a portfolio or my data project sites, after all.
But in posting my Covid datagraphics, I’ve come across variations of this map with all sorts of meme-y, witty captions saying why Canada is doing so much better than the US, why Americans shouldn’t be allowed to travel to Canada, and now why the Blue Jays shouldn’t be allowed to host Major League Baseball games.
Well, that map isn’t necessarily wrong, but it’s incredibly misleading.
You can see the map there in the centre and some tables to the left, some tables to the right, and even a micro table beneath thundering away at the map’s position. I could get into the overall design—maybe I will one of these days—but again, let’s look at that map.
The crux of the argument is that there are a lot of red dots in the United States and very few in Canada. But look at the table in the dashboard on the left. At the very bottom you see three small tabs, Admin 0, Admin 1, and Admin 2. Admin 0 contains all entities at the sovereign state level, e.g. US, Canada, Sweden, Brazil, &c. Admin 1 is the provincial/state level, e.g. Pennsylvania, Illinois, Ontario, Quebec, &c. Admin 2 is the sub-provincial/sub-state level, e.g. Philadelphia County, Cook County, Chester County, Lake County, &c.
Notice anything about my examples? Not all countries have provinces/states, but Canada certainly does. And then at Admin 2, the examples and indeed the data only have US counties and US data. Everything in Canada has been aggregated up to Admin 1. And that is the problem.
The second part to point out is the dot-ness of the map. And to be fair, this is part of a broader problem I have been seeing in data visualisation the last few months. Dots, circles, or markers imply specificity in location. The centre of that object, after all, has to fall on a specific geographic place, a latitude and longitude coordinate. It utterly fails to capture the dimensions and physical size of the geographic unit, which can be critical.
Because not all geographic units are of the same size. We all know Rhode Island as one of the smallest US states. Let’s compare that to Nunavut or Yukon in Canada, massive provinces that spread across the Canadian Arctic. Rhode Island, according to Google, 1212 square kilometres. Nunavut? 808,200.
So now show both states/provinces on a map with one dot and Rhode Island’s will practically cover the state. And it will also be surrounded by and in close proximity to the states or Massachusetts and Connecticut. Nunavut, on the other hand will be a small dot in a massive empty space on a map. But those dots are equal.
Now, combine that with the fact that the Hopkins map is showing data on the US county level. Every single county in the United States gets a red dot. By default, that means the US is covered with red dots. But there is no county-level equivalent data for Canada. Or for Mexico (also seen in the above graphic). And so given we’re only using dots to relate the data, we see wide swaths of empty space, untouched by red dots. And that’s just not true.
Yes, large parts of the Canadian Arctic are devoid of people, but not southern Ontario and Quebec, not the southwestern coast of British Columbia, not the Maritimes.
The Hopkins map should be showing geographic units at the same admin level. By that I mean that when on Admin 0, the map should reflect geographic units of sovereign state level, allowing us to compare the US to Canada directly. But, and for this argument I’m assuming we’re keeping the dots despite their flaws, we only see Admin 0 level data.
Admin 1 shows only provincial level data. Some countries will begin to disappear, because Hopkins does not have the data at that level. But in North America, we still can compare Pennsylvania and Illinois to Ontario and Quebec.
But then at Admin 2, we only see the numerous dots of the United States counties. It’s neither an accurate nor a helpful comparison to contrast Chester County or Will County to the entire province of Ontario and so the map should not allow it. Instead, as the above graphic shows, it creates misconceptions of the true state of the pandemic in the US and Canada.
Credit for the Hopkins dashboard goes to, well, Hopkins.
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.
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.
Though the temperatures might not always feel it, at least in Philadelphia, summer is ending and autumn beginning. Consequently I wanted to share this neat little work that explores urban heat islands. Specifically, this post’s author looks at Massachusetts and starts with a screenshot of the Boston area.
The author points out that the Boston Common and Public Garden are two areas of cool in an otherwise hot Boston. He also points out the Charles River and the divide between Boston and Brookline. I would like to add to it and point out the Fens and the Emerald Necklace.
I wonder if a scale of sorts would help, though the shift from warm yellows and reds to cooler greens and blues certainly helps differentiate between the cooler and warmer areas.
Dorian now speeds away from Newfoundland and into the North Atlantic. We looked at its historic intensity last week. But during that week, with all the talk of maps and Alabama, I noted to myself a map from the BBC that showed the forecast path.
But note the state borders. New Jersey and Delaware have merged. Is it Delawarsey? And what about Maryland, Virginia, and the District of Columbia? Compare that to this map from the Guardian.
What we have are intact states. But, and it might be difficult to see at this scale, the problem may be that it appears the BBC map is using sea borders. I wonder if the Delaware Bay, which isn’t a land border, is a reason for the lack of a boundary between the two states. Similarly, is the Potomac River and its estuary the reason for a lack of a border between Virginia, Maryland, and DC?
I appreciate that land shape boundary files are easy, but they sometimes can mislead users as to actual land borders.
Credit for these pieces go the BBC graphics department and the Guardian graphics department.