After a rainy weekend in Philadelphia thanks to Hurricane Henri, we are bracing for another heat wave during the middle of this week. Of course when you swelter in the summer, you seek out shade. But as a recent article in the Philadelphia Inquirer pointed out, not all neighbourhoods have the same levels of tree cover, or canopy.
From a graphics standpoint, the article includes a really nice scatter plot that explores the relationship between coverage and median household income. It shows that income correlates best with lack of shade rather than race. But I want to focus on a screenshot of another set of graphics earlier on in the article.
I enjoyed this graphic in particular. It starts with a “simple” map of tree coverage in Philadelphia and then overlays city zip codes atop that. Two zip codes in particular receive highlights with bolder and larger type.
Those two zip codes, presumably the minimum and maximum or otherwise broadly representative, then receive call outs directly below. Each includes an enlarged map and then the data points for tree cover, median income, and then Black/Latino percentage of the population.
I don’t think the median income needs to be in bar chart form here, especially given the bars do not line up so that you can easily compare the zip codes. The numbers would work well enough as factettes or perhaps a small dot plot with the zip codes highlighted could work instead.
Additionally, the data labels would be particularly redundant if a small scale were used instead. That would work especially well if the median income were moved to the lowest place in the table and the share charts were consolidated in one graphic. Conceptually, though, I enjoy the deep dive into those two zip codes.
Then I wanted to highlight some great design work on the maps. Note how in particular for Chestnut Hill, 19118, the outline of the zip code is largely in a thicker, black stroke than the rest of the map. At the upper right, however, you have two important roads that define the area and the black stroke breaks at those points so the roads can be clearly and well labelled. The other map does the same thing for two roads, but their breaks are shorter as the roads run perpendicular to the border.
Overall this was just a great piece to read and I thoroughly enjoyed the graphics.
Yesterday I mentioned how I spent Monday researching some old family properties in Philadelphia. In some cases the homes my family owned still stand. But, in many others the homes have long since been replaced. But that’s the nature of city development.
That got me thinking about an article published earlier this month at Philadelphia YIMBY where the author created an animated .gif detailing the Philadelphia skyline from 1905 through 2020. This screenshot captures the overlay of 2020 atop 1905 from the south of Philadelphia.
But the gem of the piece is the animation. Implicit in the graphic but unmentioned is the text, which is understandably centred on the architectural designs of the skyline, is the history of Philadelphia.
In the old days, well before 1905, the city was concentrated along the Delaware River because it was—and still is—a port city. But as those shipping businesses were replaced by banks and financial companies which were replaced by other offices and manufacturing headquarters that were themselves replaced by corporate highrises and so on and so forth, we can see the centre of gravity shift westward.
The mass of buildings by 1905 has shifted away from the Delaware River and is concentrated to the east of City Hall, the tallest building until the 1980s. But you can see the highest and largest buildings moving more to the left in every frame. Though in the latest you can see some new largely residential highrises built along the Delaware waterfront.
I took a holiday yesterday and headed down the street to the Philadelphia City Archives, which houses some of the oldest documents dating back to the founding of the colony. But I was there primarily to try and find deeds and property information for my ancestors as part of my genealogy work.
When I walked into the building—the archives moved a few years ago from an older building in University City into this new facility—an interactive exhibit confronted me immediately. Now I did not take the time to really investigate the exhibit, because I anticipated spending the entire day there and wanted to maximise my time.
But there was this one graphic that felt appropriate to share here on Coffeespoons.
Like a lot of statistical graphics from the mid-20th century we have a single-colour piece because colour printing costs money. It makes use of a stacked bar chart to highlight the share of housing in the city that can be classified as substandard, i.e. dilapidated or without access to a private bath.
The designer chose to separate the nonwhite from the white population on different sides of the date labels, though the scale remains the same. I wonder what would have happened if the nonwhite bars sat immediately below the white bars within each year. That would allow for a more direct comparison of the absolute numbers of housing units.
That would then free up space for a smaller chart dedicated to a comparison of the percentages that are otherwise written as small labels. Because both the absolutes and percentages are important parts of the story here.
The white housing stock increased and the number of substandard units decreased in an absolute sense, leading to a strong decline in percentages.
But with nonwhite housing, the number of substandard units slightly increased, but with larger growth in the sheer number of nonwhite housing units overall, that shrank the overall percentage.
Put it all together and you have significant improvements in white housing, though in an absolute sense there still remain more substandard units for whites than nonwhites. Conversely, we don’t see the same improvements in housing for nonwhites. Rather the improvement from 45% to 35% is due more from the increase in housing units overall. You could therefore argue that nonwhite housing did not improve nearly as much as white housing between 1940 and 1950. Though we need to underline that and say there was indeed improvement.
Anyway, I then went inside and spent several hours looking through deed abstracts. Not sure if those will make it into a post here, but I did have an idea for one over a pint at lunch afterwards.
Credit for the datagraphic goes to some graphics person for some government department.
Okay, technically not Spring Garden Street, but a strip mall along one of Philadelphia’s main arterial city streets. Luckily these aren’t some victims of a serial murderer, but rather the result of Philadelphia being an old city (for the United States). As this article from the Philadelphia Inquirer explains, the bodies were discovered during excavations for new construction at the site.
The reason I shared this today was that this past weekend, I had a pint with a colleague of mine at Yards Brewery located at 5th and Spring Garden. We sat at an outside table along Spring Garden and at some point I recall pointing out that Spring Garden hadn’t always been a street. Originally it only existed west of Broad.
Little did I know that the construction site across the street on that sultry Sunday afternoon was home to an archaeological excavation of an old, long since demolished city church cemetery.
You can use the slider in the article to compare the layout of the intersection in 2021 to that of 1860. I love these old timey maps, especially when working on genealogy. Because while we know the cities where we live today, they didn’t look like we know them 150 years ago. And in lieu of photography, it’s otherwise difficult to try and make sense of our ancestors’ world.
Just a few doors south of the Methodist church we had a bakery and a small alley called Brussels Place. And facing the alley we had what look like a number of small homes or perhaps stables. Larger presumably rowhomes lined the main streets of the intersection.
At the right of the screen, I also remember my colleague and I discussing some of the old-looking rowhomes. They may very well be the same ones depicted on this 1860 map. They are the few survivors as most of the area, as the article points out, was eventually turned into a petrol station that later became the strip mall today fenced off to be turned into flats.
This is a piece I’ve been sitting on for a little while now, okay half a year now. There isn’t too much to it as it’s an illustration overlay on a satellite photo. But the graphic supports an article about the construction of a new roundabout in Philadelphia, coincidentally where I used to live.
That intersection is…tricky to navigate at best as a pedestrian because there are six and a half streets converging at the junction—I give a half to Arizona St because, well, you’ll see shortly. When I lived in the neighbourhood I saw several near accidents between vehicles and pedestrians and vehicles and cyclists. Anything to help improve the safety will be welcome. And that improvement is what the Philadelphia Inquirercovered back in January.
This definitely fits in the category of well done, small graphics. Not everything needs to be large and interactive. This does a great job by using transparency over the satellite image and layering illustration atop the photo.
Now if we could only restore the old rails on Trenton Ave to be some kind of tram/trolley or light rain corridor. Regardless, there are some good restaurants and drinks options in the neighbourhood, so maybe I’ll have to go investigate in person now that going out is an option again. You know, to a do a proper follow-up.
Like I said yesterday, I wanted to compare cities, surprise, Philadelphia vs. Chicago. And so with some extra time I was able to finish this graphic that took the data from Climate Central to compare the two cities.
What you can see below is that Philadelphia has seen more significant temperature growth in both summer highs and summer lows. And, importantly, the increase in low temperatures, i.e. nighttime, has been greater than that of daytime highs. That means that we have less of an opportunity to cool down after a hot summer day, adding stress to the system.
Chicago on the other hand has seen less overall growth, though it’s still present. And there too we see the same pattern of greater increases in low, i.e. nighttime, temperatures than of daytime highs.
It’s remarkable to think that the flat where I lived seven of my eight years in Chicago had no air conditioning unit in the bedroom, only in the living room. It was, of course, an older concrete building from the 1960s/70s when, as the chart above shows, nighttime temperatures didn’t really require air conditioning.
But like I said yesterday, I’m just glad I’ve been able to crank the air conditioning the last several days.
First, I should say that I don’t have a lot to say about this graphic because I went back to the source because I was interested in another city and I wanted to compare the two. In other words, expect a small graphic follow up to this maybe tomorrow.
Anyways, over the last few years since returning to Philadelphia after eight years away in Chicago, I’ve had numerous conversations with different people about how “I don’t remember it always being this hot before”, which is particularly relevant as the Philadelphia region endures excessive heat. Thankfully, it’s not nearly as bad as the Pacific Northwest. Also I have air conditioning blasting next to me as I type this out, so, you know.
The common refrain in these conversations, however, tends to be less about how we have high temperatures and more about how it’s difficult to sleep at night. And there’s a reason for that as this article from the Philadelphia Inquirer explains, our average summer low temperatures are rising, and rising faster than our average summer high temperatures.
Of course you can probably already see where I was going with this. The Inquirer linked to their source and that’s where I’ve spent my time this morning, alas, I didn’t quite have enough to finish what I started and so this post will have to do.
Credit for the piece goes to the Philadelphia Inquirer graphics department.
It’s no secret that Americans—and likely at least Western communities more broadly—live in bubbles, one of which being our political bubbles. And so I want to thank one of my mates for sending me the link to this opinion piece about political bubbles from the New York Times.
The piece is fairly short, but begins with an interactive piece that allows you to plot your address and examine whether or not you live in a political bubble. Using my flat in Philadelphia, the map shows lots of little blue dots, representing Democratic voters, near the marker for my address and comparatively few red dots for Republicans.
If you then look a bit more broadly, you can see that by summing up the dots, my geographic bubble is largely a political bubble, as only 13% of my neighbours are Republicans. Not terribly surprising for a Democratic city.
And while the piece does then zoom back out a wee bit, it tries to show me that I don’t live too far from a politically integrated bubble. Except in this case, it’s across a decent sized river and getting there isn’t the easiest thing in the world. I’m not headed to Gloucester anytime soon.
These interactives serve the purpose of drawing the user into the article, which continues explaining some of the causes of this political segregation, by both policy, redlining, and personal choice, lifestyle. The approach works, because it gives us the most relatable story in a large dataset, ourselves. We’re now emotionally or intellectually invested in the idea, in this case political bubbles, and want to learn all about it. Because the more you know…
The piece uses the same type of map to showcase the bubbles more broadly from the Bay Area to the plains of Wyoming. (No surprises in the nature of those political bubbles.) It wraps up by showing how politicians can use the geography of our political bubbles to create political geographies via gerrymandering that shore up their political careers by creating safe districts. The authors use a gerrymandered northeastern Ohio district that encompasses two cities, Cleveland and Akron, to make that point.
That’s in part why I’m in favour of apolitical, independent boundary commissions to create more competitive congressional districts. Personally, I would have been fascinated to see how Pennsylvania’s congressional districts, redrawn in 2018 by the Pennsylvania Supreme Court, after the court found the gerrymandered districts of 2011 unconstitutional, created political competition between parties instead of within parties. But I digress.
And then for kicks, I looked at how my flat in Chicago compared.
Not surprisingly, my neighbourhood in Lakeview was another political bubble, though this one even more Democratic than my current one.
But if I had wanted to move to an integrated political bubble, instead of Philadelphia, I could have moved to…Jefferson Park.
Credit for the piece goes to Gus Wezerek, Ryan D. Enos and Jacob Brown.
In many cities through the United States, real estate represents a hot commodity. It’s not difficult to understand why, as have covered before, Americans are saving a bit more. Coupled with stay-at-home orders in a pandemic, spending that cash on a home down payment makes a lot of sense for a lot of people. But with little new construction, it’s a seller’s market.
The Philadelphia Inquirer covers that angle for the Philadelphia region and in the article, it includes a map looking at time to sell a house. And it’s that interactive map I want to look at briefly this morning.
Primarily I want to discuss the colours, as you can gather from this post’s title. We have six bins here, each indicating an amount of time in one-week intervals. So far so good. Now to the colours, we have red for homes that sell in one week or less and blue for homes that sell in five weeks or more.
Blue to red is a pretty standard choice. You will often see it in maps where you have positive growth to negative growth or something similar, I’ve used it myself on Coffeespoons a number of times, like in this map of population growth at the county level here in Pennsylvania.
In those scenarios, however, note how you have positive values and negative values. The change in colour (hue) encodes the change in numerical value, i.e. positive vs. negative. We then encode the values within that positive or negative range with lighter/darker blues and reds. Most often the darker the blue or red, the greater the value toward the end of the spectrum. For example, in Pennsylvania, the dark blue meant population growth greater than 8% and red meant population declines in excess of 8%.
As an aside you’ll note that there are no dark blue counties in that map and that’s by design. By keeping the legend symmetrical in terms of its minimum and maximum values, we can show how no counties experienced rapid population growth whilst several declined rapidly. If dark blue had meant greater than 4% growth, that angle of the story would have been absent from the map.
Back to our choropleth discussion, however. How does that fit with this map of selling times for homes in the Philadelphia region?
Note first that five weeks is a positive value. But so is one week or less. The use of the red-blue split here is not immediately intuitive. If this map were about the change or growth in how long homes sell, certainly you could see positive and negative rates and those would make sense in red and blue.
The second part to understand about a traditional red-blue choropleth is that at some point you have to switch from red to blue, a mid-point if you will. If you are talking positive/negative like in my Pennsylvania map, zero makes a whole lot of sense. Anything above zero, blue, anything below zero red.
Sometimes, you will see a third colour, maybe a grey or a purple, between that red and blue. That encodes a fuzzier split between positive and negative. Say you want to give a margin of 1%, i.e. any geographic area that has growth between +1% and -1%. That intrinsically means the bin is both positive and negative at the same time, so a neutral colour like grey or a blend of the two colours, a purple in the case of red and blue, makes a whole lot of sense.
Here we have nothing like that. Instead we jump from a light yellow two-to-three weeks to a light blue three-to-four weeks.
What about that yellow? In a spectrum of dark blue to light blue, you will see lighter blues than darker blues. But in a red spectrum, that light red becomes pinkish or salmonish depending on that exact type of red you use. (Conversation for another day.) Personal preferences will often push clients to asking a designer to “use less pink” in their maps. I can’t tell you the number of times I’ve heard that.
If that comes up, designers will often keep their blue side of the legend from the dark to light—no complaints there, or at least I’ve never heard any. But for the red side, they’ll switch to using hue or type of colour instead of dark to light red.
Not all colours are as dark as others. Blue and red can be pretty dark. Yellow, however, is a fairly light colour. Imagine if you converted the colours to greyscale, you’ll have very dark greys for blue and red, but yellow will be consistently far lighter than the other two.
The designer can use the light yellow as the light red. But to link the yellow to red, they need to move through the hues or colours between the two. There’s a whole conversation here about colour theory and pigment and light absorption vs. pixels and light emission, but let’s go back to your colours you learned in primary school (pigment and light absorption). Take your colour wheel and what sits between red and yellow? Orange.
And so if a client objects to a light pink, you’ll see a pseudo dark-to-light red spectrum that uses a dark red, a medium orange, and a light yellow. Just like we see here in this Inquirer map.
Back to the two-to-three week and three-to-four week switch, though. What’s the deal? This is my sticking point with the graphic. I am looking for the explanation of why the sudden break in colour here, but I don’t see any obvious one.
Why would you use this colour scheme where blue and red diverge around a non-zero value? Let’s say the average home in the region sells in three weeks, any of the zip codes in red are selling faster than average, hot markets, and those taking longer than average are in blue, cold markets. Maybe it’s the current average, however. What if it were the average last year? Or the national average? These all serve as benchmarks for the presented data and provide valuable context to understand the market.
Unfortunately it’s not clear what, if any, benchmarks the divergence point in this map reflects. And if there is no reason to change colours mid-legend, with only six bins, a designer could find a single colour, a blue or purple for example, and then provide five additional lighter/darker shades of that to indicate increasing/decreasing levels of speed at which homes sell.
Overall, I left this piece a wee bit confused. The general trend of regional differences in how quickly homes are selling? I get that. But because there’s a non-logical break between red and blue here—or at least one I fail to see in the graphic—this map would work almost as well if each bin were a separate colour entirely, using ROYGBIV as a base for example.
In 2020, baseball did not permit fans to attend regular season matches. (They changed this for the playoffs.) Instead, many stadiums opted for cardboard cutouts: fans often paid a fee and submitted a picture that the team printed on cardboard cutouts. Like so many things we will say about 2020, it was surreal.
But in Philadelphia at least, cardboard cutouts are out, and human fans are in. The state government in Harrisburg and the city government will allow 20% capacity at outdoor stadiums and 15% for indoor stadiums.
The Philadelphia Inquirer created a small graphic for its homepage to capture this news.
I intentionally included other site elements in the cropping to show how the graphic fits into the broader site. The extra white space around the image helps focus attention on the datagraphic over the numerous photographic elements for each article. Clicking on other tabs in the section brings up full-component-width graphics.
To the graphic itself.
My guess would be this was a quick turnaround piece. There are a few things going on here. The first and most obvious one, the squares as spectators. Now I confess this confused me at first. I was not entirely certain what the coloured squares meant; they mean in-person attendees. Was this supposed to be an overall stadium? Or was it a representative seating section?
The quick turnaround becomes important, because this is probably how I would have first conceptualised the graphic. But, with more time, I may have attempted to incorporate the shape of the playing field, be it a baseball diamond or basketball court, or hockey rink—I know all the sports terms!—and surrounded them with shapes representing a certain number of spectators. Squares might not work in that case because of the curves. Circles? Hexagons? Regardless of the shape, the filling of occupied seats would be the same as here, but it would perhaps be clearer to some readers, i.e. me.
Second, we get to the table below the graphics. Here we have a subtle design decision. Note that here the designer greyed out the normal capacity figures. The new figures at that 20% and 15% rates are what appear in black bold text. My usual instinct is to use typographic weight, regular vs. bold, in these situations. But the grey here works equally well.
Third, and this also involves the table, we have the first game data. We talked about the comparison of the capacity and permitted attendance. But I wonder, did the date of the first game with fans needed to be displayed in the same way as the permitted attendance? Because the news isn’t the dates of the first games—at least not as I read the news—but the numbers of attendees. And because of that, maybe I would have reduced the size of the type for the date of the first game. Or, conversely, set the type for the new attendance in a larger point size.
Overall, I enjoyed seeing this news presented visually, even if I was left confused.