Substandard Housing in Philadelphia

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.

Philadelphia’s population crested in the 1950 census, it would decline continually until the 2010 census.

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.

Credit for the exhibit goes to Talia Greene.

Choropleths and Colours

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.

Red vs. blue

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.

Credit for the piece goes to John Duchneskie.

Millennials Are the Worst

Happy Friday, everybody. We made it to week’s end. But wouldn’t you know it? Millennials are still terrible. Admittedly this piece is over a year old, but I could not remember ever seeing it before.

If you do not recall, last year there was a debate about the spending habits of millennials and why they are not out there buying homes and properties. The point was that we waste our money on experiences like expensive coffees and, most specifically, avocado toast. So amidst all this, the BBC decided to look at how many pieces of avocado toast would be needed to purchase an apartment in 10 global cities. Neither Philadelphia nor Chicago were on that list, but New York is.

Note they even have local prices for avocado toast to make the index more accurate.
Note they even have local prices for avocado toast to make the index more accurate.

Ultimately, I have never had avocado toast. But it sounds pretty good. But I find it a stretch to think the reason I do not own a home is because I am trying to eat 12,135 slices of avocado toast.

Credit for the piece goes to Piero Zagami.

Labour Marches On (into Tory Housing?)

We have a nice little piece from the Economist today, a look at the electoral majority for London-area constituencies and how their housing prices may begin to draw out priced-out Labour votes from London proper.

The political impact of scarce housing supply
The political impact of scarce housing supply

What I really like from the design side is the flip of the traditional choropleth density. In other words, we normally see the dark, rich colours representing high percentages. But here, those high majority constituencies are not the ones of focus, so they get the lighest of colours. Instead, the designers point attention to those slimmest of majorities and then offer the context of average home prices.

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

Detroit’s Housing Market

A few weeks ago the Wall Street Journal published a graphic that I thought could use some work. I like line charts, and I think line charts with two or three lines that overlap can be legible. But when I see five in five colours in a small space…well not so much.

So I spent 45 minutes attempting to rework the graphic. Admittedly, I did not have source data, so I simply traced the lines as they appeared in the graphic. I kept the copy and dimensions and tried to work within those limitations. Clearly I am biased, but I think the work is now a little bit clearer. I also added for context the Great Recession, during which credit tightened, ergo more properties would have been likely purchased with cash. It’s all about the context.

The original:

The original graphic
The original graphic

And my take:

My take on it all
My take on it all

Credit for the original work goes to the Wall Street Journal graphics department.