Just Keep Grinding it Out

There are certain journalism outlets that I read that consistently do a good job with information design or at least are known for it. Now I try to keep my media diet fairly large and ideologically broad, but in that there are also still some outlets that feature quality design than others. The New York Times, the Washington Post, and the Economist are usually probably top of my list, but you will also see the Wall Street Journal, Philadelphia Inquirer, Boston Globe, the Guardian, and the BBC. I also read more niche outlets for some of my interests, e.g. the Athletic for Red Sox and baseball. But these often don’t feature information design. Politico is one that I read for my political news fix. And when I was reading it whilst on holiday, I was surprised to find an article about the employment market with a really nice line chart.

The article examines the changing labour market where, for over a year now, bargaining power largely resided with employees. If employees wanted raises, benefits, perks, whatever, they could often leave their current employer if their requests weren’t met because another employer, desperate for staff, would likely meet their asks. However, as the economy cools, we would expect the labour market to tighten making few openings available. That begins to reduce the bargaining power of employees as now employers can say “take it or leave it”, knowing that the offers they make to staff aren’t likely to be met by other employers who don’t have open positions or aren’t otherwise hiring.

Four graphics punctuate the article, detailing just that changeover. The full article is worth a read, but I wanted to take a look at one graphic that I think best captures the design decisions made.

That looks like an inflection point to me.

My screenshot above doesn’t capture the interactivity, but we will return to that in a moment. We see three data series: job openings, quits, and layoffs and discharges. The designer represented each with a primary colour, making clear distinctions between the three, and since all three are represented by thousands of units, they can be plotted together. That allows one to make easy comparisons across the three series at particular moments in time, e.g. the Covid recession. My only real quibble is with that recession bar. I probably would have used a neutral colour like a light grey instead of red, because the red appears visually linked to layoffs and discharges when they really are not.

Normally when we see an interactive line chart, we have a small legend above, sometimes below, the graphic. Here, however, the labelling for the lines sit directly next to the line. This makes the display clearer for the reader who scans the data series and I’ve seen the approach often in print, but rarely for interactive work.

And when the reader mouses over the work, the highlight does a few nice things.

See what you want to see.

We can first see that the line with which the user is engaged becomes the focus: the remaining two lines recede into the background as they are greyed out. We also get a simple, but well designed text label above the cursor. Note how that behind the text there is a thin white stroke that creates visual separation between the letters and the data line. And that cursor is a small grey circle surrounding the data point, allowing you to see said data point.

Take it all together and you have a very clear and very effective interactive line chart. It’s a job well done.

When I see good work from unexpected places it’s important to call it out and highlight it because it means some design director somewhere cares enough to try and improve their publication’s quality of communication. And in an era when many outlets suffer from disinvestment and cost-cutting staff reductions that leave fewer designers, editors, and photographers on staff it is easy to imagine design quality decreasing.

So credit for this piece goes to Eleanor Mueller.

Facebook’s for the Old Folks

We start this work week with something that the young people use, but in a different way than older people do, including elder millennials like myself: social media. Of course, as an elder millennial, I remember Facebook when it was The Facebook when it expanded access to Penn State, which I attended for a single year.

Pew Research conducted a study of teenagers that revealed they use social media more than ever before, but that they use new (sort of) platforms more than the venerable paragon of the past: Facebook.

The Economist’s Data Team looked at the data and created this graphic showing the trends.

What do you use? How often?

We see stacked bar charts on the left and then a line chart on the right. The left-hand chart shows the frequency with which teenagers use various social media platforms. What I don’t understand is how someone uses a social media application “almost constantly”. But that’s probably why I’m an elder millennial.

Get off my lawn, you whippersnappers.

On the right we see the percentage of teenagers who have used an application at least once. The biggest winners? Applications primarily featuring image over text. The losers? Those that use words.

Now longtime readers know that I am not terribly fond of stacked bar charts, especially because they make comparisons between, in this case, social media platforms very difficult. And I feel like we have a story in the occasional use responses, but it’s tough teasing it out from this graphic.

On the right, well, this is one I enjoy. You can tell just how much the social media environment has evolved in the last 7–8 years because TikTok did not exist and YouTube was not thought of as a social media platform.

I wonder if different colours were truly needed for the line chart. The lines do not really overlap and there is sufficient separation that each line can be read cleanly. If the designers wanted to highlight the fall of Facebook or another story line, they could have used accent colours.

But overall a solid graphic.

Now to check my feeds.

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

Warming Towards Women Leaders

We are going to start this week off with a nice small multiple graphic that explores the reducing resistance to women in positions of leadership in Arab countries. The graphic comes from a BBC article published last week.

A lot of positive negative movement.

These kinds of graphics allow a reader to quickly compare the trajectory of a thing between a start and an endpoint. The drawback is it can obscure any curious or interesting trends in the midpoints. For example, with Libya, is its flat trajectory always been flat? You could imagine a steep fall off but then rapid climb back up. That would be a story worth telling, but a story obscured by this type of graphic.

I do think the graphic could use a few tweaks to help improve the data clarity. The biggest change? I would work to improve the vertical scale, i.e. stretch each chart taller. Since we care about the drop in opposition to women leaders, let’s emphasise that part of the graphic. There could be space constraints for the graphic, but that said, it looks like some of the spacing between chart header and chart could be reduced. And I think for most of the charts except for the first, the year range could be added as a data definition to the graphic and removed from each chart. Similar to how every row only once uses the vertical axis labels.

Another way this could be done is by reducing the horizontal width of each chart in an attempt to squeeze the nine from three rows down to two. That would mean two additional chart positions per row. Tight fit? Probably, but there is also some extraneous space to the right and left of each chart and a large gap between the charts themselves. This all appears to be due to those aforementioned x-axis labels. An additional benefit to reducing the horizontal dimensions of each chart is it increases the vertical depth of the chart as each line’s slope, its rise over run, sees its horizontal distance shrink.

Overall this is a really smart graphic that works well, but with a few extra tweaks could take it to the next level.

Credit for the piece goes to the BBC graphics department.

It’s a Little Steamy Out There

And by out there I mean 1150 light years away. One of the five amazing images out of the first day’s announcement by the James Webb Space Telescope (JWST) team was not a sexy photo of a nebula or a look back 13.5 billion years in time. Instead it was a plot of the amount of infrared light was blocked as exoplanet WASP-96b, a hot Jupiter, transited in front of its sun. A hot Jupiter is a gas giant roughly the size of Jupiter that orbits its sun so closely—often closer than Mercury does our Sun—its year takes mere days. WASP-96b is about half the mass of Jupiter and a year takes a little over three Earth days. Hot indeed.

The JWST means not just to take those images we saw, but to also capture data about the light passing through planetary atmospheres, just like WASP-96b. And showing the world Tuesday just how that works was a brilliant idea. What they shared was this graphic.

Everyone likes water.

The original post explains the science behind it, but in short we see telltale signs of water vapor in the atmosphere. Remember that the planet is far too hit for liquid water to exist. But because the peaks and troughs were not as pronounced as expected, scientists can conclude that there are clouds and haze in the atmosphere. It did not detect any significant signs of oxygen, carbon dioxide, or methane, all of which would be noticeable if present as we expect in future exoplanets to be studied.

But later that day, the BBC published an article summarising the releases, but included a different version of the above graphic. Though the other four photos were unchanged. The BBC presented us with this.

Also steamy.

The most notable difference is the background. What was a giant illustration of a planet and then a semi-transparent chart background atop that on which the graphic sat is here replaced by a simple white background. Off the bat this chart is easier to read.

But then here we also lose some data clarity. Note on the original how we have axis markers for the wavelengths of light and the parts per million of light blocked. All are absent here. Instead the BBC opted to only put “Shorter” and “Longer” on the wavelength axis. I would submit that there was no real need to remove those labels, but that they could have been added to with these new ones. The new labels certainly explain the numbers to an audience that may not be as scientifically literate as perhaps the JWST’s audience was or was thought to be. There is certainly a value to simplifying and distilling things to a level at which your audience can understand. But there’s also a value in presenting more complex data, issues, and ideas in an attempt to educate and elevate your audience. In other words, instead of always trying to play to the lowest common denominator, it sometimes is worth it to lose a few in the audience if you ultimately increase the level of said denominator overall.

The other notable difference is that the data is presented without what I assume to be plots of the range of observations with their respective medians. You can see this in the original by how every wavelength has a line and a dot sitting in the middle of that line. In other words, over the 6+ hours the planet was observed, at each wavelength a certain amount of light was blocked. The average middle point over that whole time period is the dot. Then a line of best fit “connects” the dots to show the composition of the light streaming though that steamy atmosphere.

Again, I can understand the desire to remove the ranges and keep the median, but I also think that there is little harm in showing both. Though, the first graphic could like have used an explanation of what was shown, as I’m only assuming what we have and I could be way off. You can show more things and raise the level of the denominator, but you can only do so if you explain what your audience is looking at.

Overall both graphics are nice and capture not just the particular makeup of this one exoplanet’s atmosphere, but more broadly the potential power of the JWST and its impact on astronomy.

Credit for the original goes to the NASA, ESA, CSA, and STScI graphics teams.

Credit for the BBC version goes to the BBC graphics department.

New Mexico Burns

Editor’s note: I was having some technical issues last week. This was supposed to post last week.

Editor’s note two: This was supposed to go up on Monday. Still didn’t. Third time’s the charm?

Yesterday I wrote about a piece from the New York Times that arrived on my doorstep Saturday morning. Well a few mornings earlier I opened the door and found this front page: a map of the western United States highlighting the state of New Mexico.

That doesn’t exactly look like a climate I’d enjoy.

Unlike the graphic we looked at yesterday, this graphic stretched down the page and below the fold, not by much, but still notably. The maps are good and the green–red spectrum passes the colour blind test. How the designer chose to highlight New Mexico is subtle, but well done. As the temperature and precipitation push towards the extreme, the colours intensify and call attention to those areas.

Also unlike the graphic we looked at yesterday, this piece contained some additional graphics on the inside pages.

Definitely not a place where I want to be.

These are also nicely done. Starting with the line chart at the bottom of the page, we can contrast this to some of the charts we looked at yesterday.

Burn, baby, burn.

Here the designer used axis lines and scales to clearly indicate the scale of New Mexico’s wildfire problem. Not only can you see that the number of fires detected has spiked far above than the number in the previous years back to 2003. And not only is the number greater, the speed at which they’ve occurred is noticeably faster than most years. The designer also chose to highlight the year in question and then add secondary importance to two other bad years, 2011 and 2012.

The other graphics are also maps like on the front page. The first was a locator map that pointed out where the fires in question occurred. Including one isn’t much of a surprise, but what this does really nicely is show the scale of these fires. They are not an insignificant amount of area in the state.

Pointing out where I really don’t want to be in New Mexico.

Finally we have the main graphic of the piece, which is a map of the spread of the Calf Canyon and Hermits Peak fire, which was two separate fires until they merged into one. The article does a good job explaining how part of the fire was actually intentionally set as part of a controlled burn. It just became a bit uncontrolled shortly thereafter.

Nope. Definitely not a place to be.

This reminded me of a piece I wrote about last autumn when the volcano erupted on La Palma. In that I looked at an article from the BBC covering the spread of the lava as it headed towards the coast. In that case darker colours indicated the earlier time periods. Here the Times reversed that and used the darker reds to indicate more recent fire activity.

Overall the article does a really nice job showing just what kind of problems New Mexico faces not just now from today’s environmental conditions, but also in the future from the effects of climate change.

Credit for the piece goes to Guilbert Gates, Nadja Popovich, and Tim Wallace.

May Jobs Report

Friday the Bureau of Labour Statistics published the data on the jobs facet of the American economy. Saturday morning I woke up and found the latest New York Times visualisation of said jobs report waiting for me at my door. The graphic sat\s above the fold and visually led the morning paper.

Almost out of the hole.

We have a fairly simple piece here, in a good way. Two sections comprise the graphic. The first uses a stacked bar chart to detail the months wherein the US economy lost jobs during the previous two and a half years. We can take a closer look in this second photo that I took.

But the recovery hasn’t been uniformly good for all.

Here we can see the stacked bars pile up with the most recent bars to the right. Some of the larger bars have labels stating the number of jobs either lost (top) or gained (bottom). I’m not normally a fan of stacked bar charts, because they don’t allow a reader to easily discern like-for-like changes. In this instance, the goal is to show how close all the little bits have come towards making up the three negative bars. Where I take issue is that I would prefer the designers used some sort of scale to indicate even a rough sense of how many jobs the various bars represent.

That issue crops up again to a slightly lesser degree with the bottom set of graphics. These compare the growth of hourly earnings and inflation both from February 2020. During the first few months of the pandemic and its recession, you can see earnings for those most directly impacted by shutdowns drop. But there is no negative scale accompanying the positive scale and that makes it difficult to determine just how far earnings fell for those in, say, leisure and hospitality.

The second part of the graphic works overall, however it’s just some of the finer design details that are missing and take away from the graphic’s overall effectiveness.

This all fits part of a larger trend in data visualisation that I’ve been noticing the last few months. Fewer charts seem to be using axes and scales. It’s not a good thing for the field. Maybe some other day I’ll write some things about it.

For this piece, though, we have an overall solid effort. Some different design decisions could have made the piece clearer and more effective, but it still does the job.

Credit for the piece goes to Ben Casselman, Ella Koeze, and Bill Marsh.

The Shrinking Colorado River

Last week the Washington Post published a nice long-form article about the troubles facing the Colorado River in the American and Mexican west. The Colorado is the river dammed by the Hoover and Glen Canyon Dams. It’s what flows through the Grand Canyon and provides water to the thirsty residents of the desert southwest.

But the river no longer reaches the ocean at the Gulf of California.

Why? Part drought, part population growth, and part economic activity. The article does a great job of exploring the issue and it does so through the occasional use of information graphics. This screenshot captures the storage capacity of the two main dams, Lake Mead and Lake Powell, created by the Hoover and Glen Canyon Dams, respectively. You may have heard of these recently because the water shortages presently affecting the region have brought reservoir levels to some of their lowest levels in years. And that means people have been finding all sorts of things.

But the graphic does a nice job of showing just how low things have gotten of late. Naturally I am curious what the data looks like on a longer timeline. Hoover Dam, of course, began during the administration of Herbert Hoover but was completed during the Franklin Roosevelt administration—who also renamed the dam as Boulder Dam though Congress reversed that change in 1947. Lake Powell came along three decades later and so the timelines would not be the exact same, but I am curious all the same.

Low and getting lower

The overall article makes sparse use of the graphics and they occupy much less space in the design than the numerous accompanying photographs. But the balance in terms of content works, I just would have preferred the charts and maps a bit larger.

Contrast this to what we explored last week in a New York Times piece, specifically the online version. There we saw graphics with no headers, data descriptors, axes labels, &c. Here we see the Washington Post was able to create a captivating piece but treat the data and information—and the reader—with respect. There are fewer graphics in this piece, but the way they were handled puts this leaps and bounds above the online version we looked at last week.

Credit for the piece goes to a lot of people, but the graphics specifically to John Muyskens. The rest of the credits go to the author Karin Brulliard and then just copying and pasting from the page: Editing by Amanda Erickson and Olivier Laurent. Photography by Matt McClain. Video by Erin Patrick O’Connor and Jesús Salazar. Video editing by Jesse Mesner-Hage and Zoeann Murphy. Graphics by John Muyskens. Graphics editing by Monica Ulmanu. Design and development by Leo Dominguez. Design editing by Matthew Callahan and Joe Moore. Copy editing by Susan Stanford. Additional editing by Ann Gerhart.

More on Those Million Covid-19 Deaths

Yesterday I focused on the big graphic from the New York Times that crossed the full spread of the front/back page. But the graphic was merely the lead graphic for a larger piece. I linked to the online version of the article, but for this post I’m going to stick with the print edition. The article consists of a full-page open then an entire interior spread, all in limited colour. The remainder of the extensive coverage consists of photo essays and interviews that understandably attempt to humanise the data points, after all, each dot from yesterday represented one individual, solitary, human being. That is an important element of a story like this and other national and international tragedies, but we also need to focus on the data and not let the emotion of the story overwhelm our rational and logical analysis.

Sometimes it’s hard to realise we’re in the third year of this pandemic.

From a data visualisation standpoint the first page begins simply enough with a long timeline of the Covid-19 pandemic charting the number of absolute deaths each day. As we looked at yesterday, the absolute deaths tell part of the story. But if we were to have looked at the number of absolute cases in conjunction with the deaths, we could also see how the virus has thus far evolved to be more transmissible but less lethal. Here the number of daily deaths from Omicron surpassed Delta, but fell short of the winter peak in early 2021. But the number of cases exploded with Omicron, making its mortality rate lower. In other words, far more people were getting sick, but as far fewer were dying.

An interesting note is that if you take a look at the online version, there the designers chose a more stylised approach to presenting the data.

All the dots

Here they kept the dot approach and simply stacked and reordered the dots. However, I presume for aesthetic reasons, they kept the stacking loose dots and dropped all the axis lines because it does make for a nice transition from the map to this chart. But they also dropped all headings and descriptors that tell the reader just what they are looking at. These decisions make the chart far less useful as a tool to tell the data-driven element of the story.

There are three annotations that label the number of deaths in New York, the Northeast, and the rest of the United States. But what does the chart say? When are the endpoints for those annotations? And then you can compare the scale of the y-axis of this chart and compare it to the printed version above. A more dramatic scale leads to a more dramatic narrative.

This sort of visual style of flash and fancy transitions over the clear communication of the data is why I find the print piece more compelling and more trustworthy. I find the online version, still useful, but far more lacking and wanting in terms of information design.

The interior spread is where this article shines.

Just a fantastic spread.

From an editorial design standpoint, the symmetry works very well here. It’s a clear presentation and the white space around the graphic blocks lets that content shine as it should in this type of story. Collectively these pieces do a great job telling the story of the pandemic thus far across the nation. The graphics do not need a lot of colour and make do with sparse flash. Annotations call the reader’s attention to salient points and outliers.

Very nice work here.

From a content standpoint, I would be particularly curious if we have robust data for deaths by education level. Earlier this year I recall reading news about a study that said education best correlated to Covid cases, and I would be curious to see if that held true for deaths. Of course these charts do a great job of showing just how effective the vaccines were and remain. They are the best preventative measure we have available to us.

More really nice graphics

Here I disagree with the design decision of how to break down the states into regions. The Census Bureau breaks down the United States into four regions using the same names as in the graphic above. However, if you look closely at the inset map, you will see that Delaware, Maryland, and West Virginia in particular are included as part of the Northeast. (I cannot tell if the District of Columbia is included as part of the Northeast or South.)

Now compare that to the Census Bureau’s definition:

How the government defines US geography

If you ask me to include Delaware and Maryland as part of the Northeast, well, if you’re selling it, I’ll buy it. After all, just because the Census Bureau defines the United States this way does not mean the New York Times has to. Both are connected to the Northeast Corridor via Amtrak and I-95 and are plugged into the Megalopolis economy. Maybe the Potomac should be the demarcation between Northeast and South. But I struggle to understand West Virginia. Before you go and connect it to the Northeast, I would argue that West Virginia has far more in common with the Midwest geographically, economically, and culturally.

More critically, given this issue, it strikes me as a serious problem when the online version of the chart—with the aforementioned issues—does not even include the little inset to highlight this at best unusual regional definition.

Where would you place West Virginia?

And so while I have reservations about the data—how would the data have looked if the states were realigned?—the design of the line charts overall is good.

Again, I am talking about the print version, not that online graphic. I would argue that the above screenshot is barely even a chart and more “data art” or an illustration of data. Consider here, for example, that for the South we have that muted slate blue for the dots, but the spacing and density of the dots leads to areas of lighter slate and darker slate. But a lighter slate means more space between stacked dots and darker slate means a more compact design. A lighter colour therefore pushes the “edge” of the line further up the y-axis and artificially inflates its value, not that we can understand what that value is as the “chart” lacks any sort of y-axis.

Finally the print piece has a set of small multiples breaking down deaths by income in the three largest American cities: New York, Los Angeles, and Chicago. These are just great little charts showing the correlation between income and death from Covid, organised by Zip code.

But this also serves as a stark reminder of just how much better the print piece is over the online version. Because if we take a look at a screenshot from the online article, we have a graphic that addresses all the issues I pointed out earlier.

Why couldn’t the online article kept to this style?

I am left to wonder why the reader of the online version does not have access to this clearer and more accurate representation of the data throughout the piece?

To me this article is a great example of when the print piece far exceeds that of the online version. Content-wise this is a great story that needed to be told this weekend, but design wise we see a significant gap in quality from print to online. Suffice it to say that on Sunday I was very glad I received the print version.

Credit for the piece goes to Sarah Almukhtar, Amy Harmon, Danielle Ivory, Lauren Leatherby, Albert Sun, and Jeremy White.

All the Colours, All the Space

Everyone knows inflation is a thing. If not, when was the last time you went shopping? Last week the Boston Globe looked specifically at children’s shoes. I don’t have kids, but I can imagine how a rapidly growing miniature human requires numerous pairs of shoes and frequently. The article explores some of the factors going into the high price of shoes and uses, not very surprisingly, some line charts to show prices for components and the final product over time. But the piece also contains a few bar charts and that’s what I’d like to briefly discuss today, starting with the screenshot below.

What is going on here?

What we see here are a list of countries and the share of production for select inputs—leather, rubber, and textiles—in 2020. At the top we have a button that allows the user to toggle between the two and a little movement of the bars provides the transition. The length of the bar encodes the country in question’s market share for the selected material.

We also have all this colour, but what is it doing? What data point does the colour encode? Initially I thought perhaps geographic regions, but then you have the US and Mexico, or Italy and Russia, or Argentina and Brazil, all pairs of countries in the same geographic regions and yet all coloured differently. Colour encodes nothing and thus becomes a visual distraction that adds confusion.

Then we have the white spaces between the bars. The gap between bars is there because the country labels attach to the top of the bars. But, especially for the top of the chart, the labels are small and the gap is at just the right height such that the white spaces become white bars competing with the coloured bars for visual attention.

The spaces and the colours muddy the picture of what the data is trying to show. How do we know this? Because later in the article we get this chart.

Ahh, much better. Much clearer.

This works much better. The focus is on the bars, the labelling is clear, almost nothing else competes visually with the data. I have a few quibbles with this design as well, but it’s certainly an improvement over the earlier screenshot we discussed. (I should note that this graphic, as it does here, also comes after the earlier graphic.)

My biggest issue is that when I first look at the piece, I want to see it sorted, say greatest to least. In other words, Furniture and bedding sits at the top with its 15.8% increase, year-on-year, and then Alcoholic beverages last at 3.7%. The issue here, however, is that we are not necessarily looking at goods at the same hierarchical level.

The top of the list is pretty easy to consider: food, new vehicles, alcoholic beverages, shelter, furniture and bedding, and appliances. We can look at all those together. But then we have All apparel. And then immediately after that we have Men’s, Women’s, Boys’ , Girls’, and Infants’ and toddlers’ apparel. In other words, we are now looking at a subset of All apparel. All apparel is at the same level of Food or Shelter, but Men’s apparel is not.

At that point we would need to differentiate between the two, whilst also grouping them together, because the range of values for those different sub-apparel groups comprise the aggregate value for All apparel. And showing them all next to Food is not an apples-to-apples comparison.

If I were to sort these, I would sort by from greatest to least by the parent group and then immediately beneath the parent I would display the children. To differentiate between parent-level and children-level, I would probably make the bars shorter in the vertical and then address the different levels typographically with the labels, maybe with smaller type or by putting the children in italic.

Finally, again, whilst this is a massive improvement over the earlier graphic, I’d make one more addition, an addition that would also help the first graphic. As we are talking about inflation year-on-year, we can see how much greater costs are from Furniture and bedding to Alcoholic beverages and that very much is part of the story. But what is the inflation rate overall?

According to the Bureau of Labour Statistics, inflation over that period was 8.5%. In other words, a number of the categories above actually saw price increases less than the average inflation rate—that’s good—even though they were probably higher than increases had been prior to the pandemic—that’s bad. But, more importantly for this story, with the addition of a benchmark line running vertically at 8.5%, we could see how almost all apparel and footwear child-level line items were below the inflation rate. But the children and infant level items far exceeded that benchmark line, hence the point of the article. I made a quick edit to the screenshot to show how that could work in theory.

To the right, not so good.

Overall, an interesting article worth reading, but it contained one graphic in need of some additional work and then a second that, with a few improvements, would have been a better fit for the article’s story.

Credit for the piece goes to Daigo Fujiwara.

America’s Crime Problem

During the pandemic, media reports of the rise of crime have inundated American households. Violent crimes, we are told, are at record highs. One wonders if society is on the verge of collapse.

But last night a few friends asked me to take a look at the data during the pandemic (2020–2021) and see what is actually going on out on the streets in a few big cities. Naturally I agreed and that’s why we have this post today. The first thing to understand, however, is that we do not have a federal-level database where we can cross compare crimes in cities using standardised definitions. The FBI used to produce such a thing, but in 2020 retired it in favour of a new system that, for reasons, local and state agencies have yet to fully embrace. Consequently, just when we need some real data, we have a notable lack of it.

At the very least we have national-level reporting on violent crimes and homicides, the latter of which is a subset of violent crimes. Though these reports are also dependent on local and state agencies self-reporting to the FBI. I also wanted to look at not just whether crime is up of late, but is crime up over the last several years. I chose to go back 30 years, or a generation.

We can see one important trend here, that at a national level violent crimes are largely stable at rate of 400 per 100,000 people. Homicides, however, have climbed by nearly a third. Violent crimes are not rising, but murders are.

My initial charge was to look at cities and violent crime. However, knowing that nationally violent crimes are largely stable, the issue of concern would be how the rise in murders is playing out on American city streets. With the caveat that we do not have a single database to review, I pulled data directly from the five cities of interest: Philadelphia, Chicago, New York, Washington, and Detroit.

I also considered that large cities will have more murders simply by dint of their larger populations. And so when I collected the data, I also tried to find the Census Bureau’s population estimates of the cities during the same time frame. Unfortunately the 2021 estimates are not yet available so I had to use the 2020 population estimates for my 2021 calculations.

First we can see that not all cities report data for the same time period. And for Detroit in particular that makes comparisons tricky. In fact only New York had data back to the beginning of the century. Regardless of the data set’s less than full robustness we can see that in all five cities homicides rose in 2020 and 2021.

Second, however, if squint through that lack of full data, we see a trend at the city level that aligns with the national level. Homicides, tragically, are indeed up. However, in New York and Washington homicides are still below the data from near 2000 and at that time homicides already appear on a downward trajectory. I would bet that homicides were even higher during the 1990s and that the 2000s represented a long-run decline. In other words, whilst homicides are up, they are still below their peaks. A worrying trend, but far from the sky is falling.

That cannot quite be said for other cities. Let’s start with Detroit. Sadly we have too few years of data to draw any conclusion other than that homicides rose compared to the years preceding the pandemic.

That leaves us with Philadelphia and Chicago. Philadelphia has less data available and it’s harder to make a determination of what is happening. But we can say that since 2007, homicides have not been higher. If you look closely though, you can see how there does appear to be a downward trend at the beginning of the line. We do not have enough data like we do with New York and Washington, but I would bet homicides are up in Philadelphia, but still far short of what they were in the 1990s.

Chicago is the oddball. Yes, it saw a peak in homicides during the pandemic. But in 2016 the city didn’t miss the pandemic peak by much. In other words, homicides were staggeringly high in Chicago before the pandemic. If anything, we see a failure to combat high crime rates. But even before that spike in 2016, we see more of a valley floor in homicides. True, at the beginning of the century homicides appear to have trended down. But unlike the other cities here, homicides bottomed out at around 450 per 100,000 people. I’m not so certain we had a persistent, long-run decline in Chicago with which to start.

And like I said above, larger populations we would expect to have more murders because more potential criminals and victims. When we equalise for population we see the same trends as we expect—the city populations have been relatively stable over the last 20 years. Instead what we see is that relative to each other murders are more common in some cities and less so in others.

New York is a great example with nearly 500 murders last year, a number on par with Philadelphia. But New York has over 8 million inhabitants. Philadelphia has just 1.6. Consequently New York’s homicide rate is a surprisingly low 5.9 per 100,000 people. Philadelphia’s on the other hand? 35.6.

Philadelphia is near the top of that list, with Washington and Chicago having similar, albeit lower, rates at 31.7 and 30.1, respectively. But sadly Detroit surpasses them all and is in league of its own: 47.5 in 2021.

Credit for the pieces is mine.