Transcript
Speaker: Welcome back to the Policy Biz Podcast. I'm your host, John Schwabisch. everybody. This is one of these intermediate episodes on one of my favorite graphs. And today we're going to talk about one of my favorite connected scatter plots from Hannah Fairfield at the New York Times. This is going back all the way to 2012. We're going to get into that in just a second. But just wanted to remind you of two things in case you hadn't seen it.
Speaker: First, I will be doing a live episode of this podcast at the Viz conference coming up in November in Boston. That will take place on Thursday of the conference, part of the cluster summit that Raquel Farkinson, Frank Alavsky and i have been pulling together. It's a very exciting event. We're gonna have a live podcast. We're gonna have some lightning talks and we're gonna have an info session for people who are interested in being a practitioner or a freelancer in the data visualization practice. So if you are interested, please sign up for the conference, register for the conference and join us. You can register for one day or two days or the entire week of the conference. So make sure you sign up for that. Of course, I'll be publishing the podcast later, but we will be doing a live podcast and it's going to be great. I'm super excited. First time I'll be doing a live podcast.
Speaker: Secondly, in case you don't know, you haven't seen, i have a new book coming out co-authored with my longtime collaborator, Alice Fang. Our new book, Thoughtful Data, comes out of the Do No Harm project that I've been working on for the last several years. That will be coming out at the end of 2026. through my publisher, Columbia University Press that I've published now. This will be the third book I've published with them. So be sure to be on the lookout for that. I'll put links to both the Cluster Summit at Viz and to the new book on Amazon in the show notes. So be sure you check those out.
Speaker: All right, let's take a look at one of my favorite graphs. Again, this is just a selection of graphs, not in any particular order, just going by ones I wanna talk about. So today we're gonna talk about Hannah Fairfields, Connected Scatter Plot, Driving Safety in Fits and Starts. You can see this on the Wayback Machine. I have not really been able to find it back on the new York Times website, so I'll put the link to this one as well in the show notes. If you're on YouTube, you can just see it. It is right in front of you. It's a little small because it's small, but you can go check it out. Okay, so this and Hannah's other connected scatter plot that I don't have in front of me, I like this one for a couple of reasons I'll talk about in a second.
Speaker: Probably the first connected scatter plots I'd ever seen, aside from the beverage curve, which is a connected scatter plot that shows the relationship between job job openings and the unemployment rate. That's sort of like a standard graph in the economics literature, but I had never really thought about it other than like, well, it's just a line chart.
Speaker: that shows time, but it's actually not a line. I mean, it is a line term, but it's connected scatterplot. And only until I think I saw Hannah's piece that I start to put these things together, that the connected scatterplot is a scatterplot, but instead of having each dot represent you know a country or a person or a region or a place,
Speaker: what a company or whatever it might be instead each dot is gonna show a period of time. So let's just explain this one right here and if you're just listening to this I'll do my best to describe it. So what this is showing is along the vertical axis is auto fatalities per 100,000 people. So that's a vertical axis and then the horizontal axis is vehicle miles driven per capita and this is all US data. So we've got vehicle miles driven per capita on the horizontal axis. So it's starting at around 3,500 or maybe 3,000 miles per capita, all the way up to 10,000. And then auto fatalities starting all the way at the bottom at 10 fatalities per 100,000, going all the way up to about 30 so. So each dot in the scatter plot represents a year. So it starts with 1950, that's the first point. And then as we move along, sort of like wobbles a little bit. And then by 1960, what we've seen is that the line has basically trended downward along the vertical axis, which means fatalities have fallen, but vehicle miles driven per capita has increased because we've gone from left to right. So that's where we end up between 1950, 1960. It sort of goes up and down a bit, but over the whole period, it is trending downward.
Speaker: Then between 1960 and 1970, it basically shoots straight up, right? um And so um we, again, are moving left to right. So still miles driven per capita have increased, but now fatalities are going way up.
Speaker: And now between 1970 and 1980, we have this downward trend. We're going to come back to some of the details within these decades in a moment because it's really cool. Between 1970 and 1980, we're still going down,
Speaker: ah We're now going down along the vertical axis, still going left to right. So now fatalities are dropping, but we're driving more 1980 to 1990. Similarly, we're driving more. Fatalities are going down 1990 to 2000, still going down in fatalities, but driving more. And then between 2000 and 2011, when this chart ends,
Speaker: Basically, we've dropped all the way down. We have about 10 fatalities per 100,000, but the vehicle miles driven per capita have basically leveled off. So the vertical positioning hasn't changed. So,
Speaker: I just think this is a great chart. Now, why do I think it's a great chart? Well, you've combined these two variables and a lot of people have done some, have done testing on these in particular, Robert Kisara and Drew Scow and some some some papers a few years ago, along with Steve Frank Canary showing that, you know, lots of people mistake what they're looking at. They don't quite understand it. They don't know how to read it. and and i And I do get that, right? I just had to explain to you sort of how to read this chart. But again,
Speaker: Like a lot of non-standard graphs, uncommon graphs, once you learn how to read it, it's it's in your library. So then you know how to read it. um so So I think there are some, not everybody loves connected scatter plots. And I do get that. um I find that generally when I try to make these, I end up, you know eight out of 10 times, I end up in one of two places. One is that the line is basically diagonal, right? I'm looking at,
Speaker: total participation in a program and total spending. That's not particularly interesting because it's just basically almost perfectly correlated. It's just going up or it's going down. or it's some huge hairball mess and it's really hard to disentangle.
Speaker: That's what makes Hannah's scatterplot so interesting is because the patterns are so clear and easy to see and also that the annotation is so good here. So I've already explained to you how this whole chart works, but if you're just listening to this, I want you to i want to sort of step back a bit and I want you to just sort of picture in your mind a big,
Speaker: tall rectangle. That's sort of the page of the New York Times. Okay. And so what you see on this page is in the bottom left is the actual text, the actual story. And it's one, two, three paragraphs in two columns. And that's in the bottom left part of the page. And then the graph takes up the rest of the page. So we start in 1950 again, wobbles a bit and it shoots up, ah wobbles a bit, and then it starts to go down. And it's sort of on this, you know, irregular, but, you know,
Speaker: top left to bottom right direction, which gives a lot of space to sort of integrate this whole page together, which I think is really what is why I like this piece so much in that it it integrates both the story and the graphic in a static way, right? So this is before scrolly telling, before, you know, interactivity sort of took over. In a static way, it it it it integrates the graphic and the text. It's all sitting together. And then along with, we have these great annotations. So for those who are watching, I'm going to zoom in here.
Speaker: And if we look at the first part of this graph, when we go from 1950 to 1960, the line basically goes down and then from 1961 to 1969 or so, it goes up, which gives this nice white space to the left of that line, that kind of V-shaped line. And what we see there is a little bit of annotation here and it says muscle cars in bold font and then in regular font it says American cars get bigger faster and with more v8 engines and mid-sized cars more deadly the interstate highway system grows and speeds rise with it and then there's a little downward pointing black triangle so you're kind of you know more or less standard annotation here but
Speaker: What I thought was also really great about this is within that annotation in the top left part of that paragraph is a very small version of the entire line with the period that is under discussion in red. So we see this kind of little V shape in this line chart in red in this first annotation. And then just on the other side of the line, we have another annotation in quotes, unsafe at any speed. And then it talks about Ralph Nader. And there's another little you know thumbnail image essentially of the entire graph with that top part of the line shaded in red. And so this I think is a really clever way to integrate
Speaker: the text of the graph and the chart and not just not just explaining what's happening right muscle cars and ralph nader's book and so on and so on but also explaining and i think in a subtle way through these little thumbnail images with the area under discussion highlighted how to read this chart right because it tells you in this region in this era of these two variables this is what explains it this is what's happening and I'm actually gonna show you I'm gonna highlight that little part in the annotation and of course the other part of this that is kind of the what I think makes this
Speaker: the best is what the data actually tell you when we get to the late 1970s, we get to the energy crisis. So we have a third annotation here, energy crises. After the 1973 Arab oil embargo, Nixon sets a 55 mile an hour speed limit as a national energy policy. A few years later, the Iranian revolution and the Iran-Iraq war curtail fuel supplies, people drive less and more slowly and fatalities fall. And then it's got this little loop because from 1977 to miles driven and depth both go up, but then between 1978 and 1980, driving goes down. So we go right to left and then fatality sort of flatline there between those years and then we sort of resume. So this little curl here, this little curl backwards is a really interesting part of the chart and it's easy to see, it's easy to read because it's not sort of a hairball with stuff going all over the place. So it's a really fascinating chart. It's a great chart and just well done. And what I really like about it the most is the integration of all of this together into a single chart space. It's still static. So you don't click on the line and that arrow that you're looking at highlights. It is highlighted for you in the static annotations. So I think this is a great chart. um I think it's probably one of the, it's certainly one of the first times I saw a connected scatterplot in
Speaker: popular media or popular production. And I think one of the, probably one of the earlier places that I saw a really effective way to integrate explanation and annotation into a chart, not just to explain the data, but also to explain how to read the chart. Because I think even at the time, Hannah probably thought like, not everybody knows how to read this chart. So I better explain it to people, which I think is something that we all need to keep in mind.
Speaker: All right, so this is one of my favorite charts. 2012, New York Times, Hannah Fairfield, Driving Safety in Fits and Starts, Connected Scatterplot, one of my favorites. I hope you'll check it out. I hope you enjoyed this episode.
Speaker: Come back next week for another interview with another great guest. And like always, until next time, thank you so much for listening.
