So Victoria Prieto kindly invited me to give a lecture via Skype (because the class is in Uruguay) on applications of the Lexis diagram. And I must say that Skype quality just stinks for this kind of thing. They were able to get clear audio of me IFF we made it so that I was just doing screen share and their audio connection was turned off. We made it work somehow, but got lost in a sea of white noise during the Q&A a couple times. Victoria asked me to try to make them love Lexis. So, aside from trying to make a real case on the utility of the Lexis diagram, I also found some atypical applications here and there, and made my own silly (but fun!) example of lifelines in the Lexis diagram, based on futbol (I have to write it like that cuz football means US football to me, no matter how long I live in the EU) players on the Uruguay national team in all of Uruguay's appearances in World Cup and Copa America. Uruguay, you ask? It's here:
Uruguay punches (or bites? mwahaha) above their weight class in the realm of futbol. It's a mostly urban country, and they have a demography masters program, and a PhD specialization in pop studies. And Victoria teaches there, ergo the Lexis lecture, and that's why the Skype hassel. OK, now back to the point of this post:
I just wanted to show that you can represent any population of durations on the Lexis diagram (or its analogues).
Here are the plots I showed them, without first telling them what it was. I asked them to guess the data:
1) guess what this is showing.
2) Maybe this would tip off? If I just say that the data in the above historgram came from the points in this Lexis diagram? (click to embiggen)
Same data, but the lifelines only connect the first and last cups played by each player. The life lines are of course still aligned to age 0 (birth), but you could certainly realign them to make first cup be age zero, or last cup age omega... Further you could augment these to make the points games rather than simply being a team member, add other cups, regular season, and ... the pre-data territory: youth teams. The Lexis diagram isn't the limiting factor here, but the more detail that is added, the less useful lifelines become, and the more likely you are to gain insight from some kind of aggregation (perhaps on a metric rather than on team membership, and aligned to one or another definition of age). Then patterns will emerge that are otherwise invisible. Woot.
I hopothesized that you could use the Lexis diagrams to guess at the heros of any given team. Whaaa? Take the lifelines of the most recent team, and follow the lines back in time (downward left) to sometime between ages 5 and 15, assuming that that's when futbol impressions are made in the most lasting way, then look up to which players appeared in those cups. That's the population of likely homebrew heros. Not entirely novel. Any player will just tell you their childhood references anyway.
But possibly more interesting that this subject matter was the mungery required to get these data. The data were scraped from wikipedia entries, like this one. And indeed, using the code annotated here
Feel free to use it, expand it, make cool plots, and share them.
and here's the full presentation:
Thanks again Vicky!
These historical data from Quebec look promising:
If we think age and period as lat long coordinates, then we can also borrow the analogy of map projections. There is a large but finite number of ways to project the Lexis surface as it is (rotations, reflections, isotropic or not, other kinds of time measures, etc), but there are an infinite number of ways to reproject any of the time measures used as axes, thereby reprojecting whatever data is being shown on the surface. I'll use the example of an age pattern. This is very much in line with the previous post, where age itself was reprojected, and it's a basic extention of the Sanderson-Scherbov method of standardizing age patterns by distorting age in one of them. Let's start with the following standard Lexis surface of fertility.
Behold 120+ years of Sweden: (HFD)
HMD), which tells us the proportion surviving at each age x. If youinvert this then it tells you the age at which a given proportion of the synthetic population remains alive. Ergo, the ages that correspond to quantiles. These change over time, as mortality jumps about and/or gradually improves. We want the quantiles themselves as the new y-axis (last post it was remaining life expectancy, but now using survivial quantiles, just because). So, 1) for a given quantile and year, find the exact age that it corresponds to, 2) find the fertility rate that corresponds to that exact age (I use splines for both steps) 3) wash, wrinse, repeat over the whole surface: voila:
Same data! OK, we know the story, reproductive ages are now almost completely survived through. But it wasn't always the case! But that's not the point of this post. The point is the idea of reprojecting one or more axes of the Lexis surface, and its variants. All you need are two time series of data that is structured on the same age-like index (lifespan, time-to-death, time since X, time until X, etc). Here I chose lifespan quantiles, but it could have easily been another (preferably monotonic, but not necessarily so) lifetable function (or a function of a lifetable function!). Nor must it be a lifetable function. In this case we could have used ANY age-pattern to reproject. You could use fertility to reproject any aspect of mortality, for instance (but there might be more details to sort out).
Maybe I'll post another example sometime.
I presented this idea to some graphics pros yesterday at the Fraunhofer Institute in Rostock, and you can find the code in a repo for that presentation.
One could simply plot remaining life expectancy on a Lexis surface. That'd be the smart thing to do. Not what I'm going to do here, which is Sunday goofing off.
First let's swap out age with prospective age. A prospective age is the age where you hit some some fixed level of remaining life expectancy. Since mortality changes depending on where and when you are prospective ages change too. This is a concept that Warren Sanderson and Sergei Scherbov use a lot. Here's the main go-to paper that explains it all in a compelling way. It's the basis of demographers starting to say things like "60 is the new 50", etc. People had been saying that kind of thing already, and noticing active aged people more and more, but the lifetable gives a nice objective basis for sayings like that. The idea is to take some fixed remaining life expectancy like 40 or whatever, and ask which age it belongs to. That usually requires some interpolation. So here's a calculator to do it (as well as a survival quantile calculator), and the image I want to explain (it's US females, by the way, HMD, as usual)
So, we have remaining life expectancy on the y-axis, and calendar year on the x-axis. The z-coordinate that is plotted is chronological age, which is represented both with color (darker blue higher age) and with labelled black contour lines. All the countours are increasing, which just means that remaining life expectancy is increasing at all ages. Where the contours are steeper it increased faster. There is a light 5x5 grid. for e(x) and year. Following any of the horizontals will tell you the Age at which the given remaining life expectancy was hit. So, for example, follow the e(x) = 25 line. Around 1945 it hits age 50, and in the most recent year it hits 60. Ergo 60-year olds today have the remaining life expectancy of 50-year olds in 1945, hence "60 is the new 50". It's awesome to find quick increases, but this plot doesn't have many (many other populations of the world have had more drammatic gains than the USA, by the way).
Finally the yellow descending lines are birth cohorts, and their wavy pattern shows how we've not only flipped, but also irregularly distorted the Lexis diagram. These allow for more comparisons. Of course the surface uses period mortality, so there's another level of distortion in it that simply isn't revealed. One could use cohort data for either a restricted range of e(x) and/or age, or else switch to a country like Sweden, which has a long series of data and lets you do pretty much any formal demog you want.
Here's the code, including a survival quantile lookup function, also based on a spline. You could also repeat the same Lexis-ish transformation with survival quantiles on the y-axis.... And you couldplot e(x) with one contour plot, and age with another contour plot above that. Oh dear, the possibilities are endless.
(FYI, to see this code, you probably need to actually click on the link to this post. I think it doesnt' show up at the top level blogish part of this website)
I'm a fan of Vaupel & Yashin (1987) Repeated rescuscitation: How lifesaving alters lifetables (this paper, possibly paywalled) Here is a Jstor Link to the same article.
Under some strict assumptions, they derive how hypothetical improvements in mortality rates can be translated to saved lives, but that the process can be repeated indefinitely. Among the items derived is the composition of the stationary population by the numbers of times individuals have been saved. In the article, they decompose period l(x) schedules by making period comparisons. One thing that I'm always messing around with is the idea that cohort longevity has typically (always?) been better than that belonging to the period lifetables in which cohorts are born. For example, I was born in 1981, with a period e(0) of 70.81 . Ya right! By cohort is going to outperform that by a mile! One could already say that the force of mortality that I've been winning against so far my whole life is a perturbation of that which was observed in the USA in 1981. In this way, the 1981 period mortality schedule is the one I'm comparing to. I'm now 34 (I think...), so I have 35 single-age values of cohort m(x) of my own that can be compared with the period m(x) from ages 0 to 34 in 1981. Make sense?
The difference in these two series of m(x) can translate into uppercase Lambda from equation 10 and onwards in the above-linked paper. I can also get l(x) from these period and cohort m(x) series for the sake of comparison, and this gives everything needed for eq 10 in the paper. Ergo, I can decompose my cohort into those whose lives have been saved 0,1,2,... times due to improvements in mortality since we were born. And likewise for all the other cohorts passing through the population pyramid this year.
In order to break down the upper ages of the pyramid into how many times they've hypothetically been saved thus far, we need a mortality series stretching far enough back in time. The HMD contains a few such series. I'll take Sweden, because it's everyone's toy dataset for stuff like this: you just know it's going to work before you even start!
So, following my period-cohort comparison of mortality schedules to derive cumulative rate improvements, we get the following decomposition of the 2012 Swedish population pyramid:
The central off-white area are those whose lives have 'never' been saved, and then each successive shade of purple increments the number of times you've been saved. Looks like most 80-year females (right side) have been saved at least once, for instance.
This is all hypothetical- it assumes that ongoing mortality risk is the same for those that were saved 0 or 1 or 2 or more times. Inclusion of frailty would change the whole picture. But then we've never seen a pyramid decomposed by a frailty distribution because they're hypothetical too, not really observable directly unless you make more assumptions to instrumentalize the notion.
Here's the code:
Note that one of the data objects required to reproduce this is uploaded at the bottom of this post. You could download the cohort death rates from the HMD, but these are not available for cohorts with fewer than 30 observations, which we need. So I derived them straight from the HMD raw data, which is a bit extra work.
It doesn't take that much work to convince oneself that the HMD population exposure formula (version 5, soon to be incremented) makes sense (if you accept the assumptions). The assumptions are simple: Assume that deaths in the upper and lower Lexis triangles are uniformly distributed (and no migration). This is the formula:
Exposure = 1/2 * January 1st population - 2/3 deaths in the upper triangle + 1/2 December 31st population + 1/3 deaths in lower triangle
It's easy to accept the (Jan+Dec) / 2 part of the formula, but a bit more difficult to wrap one's mind around the - 2/3 and the +1/3 parts for the triangles.
If you look in Appendix E of the version 5 protocol, you'll get the calculus-based explanation:
Fair is fair, but it's not intuitive to that many people, so I'll show it numerically (you need to play along in R), and then give a non-rigorous geometric explanation, which is however intuitive (I think).
Next some code and an inspirational Lexis square to prove it to yourself: Jan 1 pop is P1, and Dec 31 pop is P2. Deaths in upper triangle are DU, and DL is the lower.
This Lexis square is a bit awkward to prove the 1/3, 2/3 thing graphically, so I'll put an equilateral one at the end, which might help. First note that all the tdeaths in DU should have been counted alive in P1. So, 1/2 * P1 would be an overestimate of the years those people lived in the triangle. So, we need to subtract some portion of DU to account for the years not lived in the triangle due to deaths. Under uniformty, the number of years they lived in the triangle on average is 1/3, so we need to subtract 2/3. We still need to show that 1/3 is the average. In the same way, the deaths in DL are not counted in P2, which means 1/2*P2 is an underestimate so we need to add some portion of DL, and they average years they lived in the triangle is also 1/3.
Here's R code to simulate this numerically, which I'll explain a bit here. First, the years lived by individuals passing through the square are diagonals, but this way of drawing the Lexis diagram stretches lifelines by sqrt(2) because they're shown as a hypotenuse... So that's why the image is awkward. Instead, from this image, imagine the life lived by those dying in the upper triangle as the distance in x from P1, whereas the life lived by those dying in the lower triangle is the distance lived in y from the lower bound of the square.
Follow along in the code to see that the average years lived in each triangle are 1/3, and that the rest therefore makes sense.
And here is a more visually appealing proof using the equilateral version of Lexis (annotation below)
If we constrain age, period, and cohort to use the same units we end up with equilateral Lexis triangles. Convince yourself that the orange circles are the center of gravity of each triangle (they are!). Now focus on the lower triangle. The lower line is 1 year long (as is the diagonal, because it's equilateral...). If I cut the lower line into thirds, we get the points a,b,c, etc. We know that the distance from a to b is 1/3, ergo, and from b to c is 1/3. Now notice that the points b,c,d form a new equilateral triangle. And so we know that the segment b,d is 1/3 long as well. Ah! And that's the one that lifelines are parallel to! and since the uniform average of the triangle is at d, we know that it took on average 1/3 of a year to get to d. Voila, we are now convinced about the 1/3 thing in the triangles.
move along, nothing to see here.
Here is my PAA 2016 program entry:
You can also find me on Wednesday, March 30 at the HMD side meeting in the morning, and at the dataviz workshop in the afternoon. I intend to sleep at night. Most paper submissions currently on the website will be updated before March 7 too.
The following annoucement went out in the most recent issue of PAA Affairs:
PAA Data Visualization Workshop 2016: PAA attendees with an interest in data visualization are invited to attend a pre-PAA workshop to be held on Wednesday, March 30, 2:00-6:00 pm at the Population Reference Bureau office in Washington DC (close to the PAA venue and located at 1875 Connecticut Ave NW, Suite 520). This workshop will include a mixture of short presentations and lots of hands-on exercises with a special focus on visualizing demographic data (stocks, flows, intensities, etc.) in commonly used communication media, such as articles and presentations. All levels of experience are welcome. There is no participation fee, but space is limited. If you are interested in participating and/or would like to be on our listserv, please send an email to Audrey Dorélien and Tim Riffe at email@example.com. This workshop is supported by the Max Planck Institute for Demographic Research, the Minnesota Population Center, and the Population Reference Bureau.Here's a flyer that Erica Nybro put together (graphic from yours truly, code here)
More program details are available below. I think that all levels of dataviz saaviness could benefit from this workshop, and it's a short walk from the Marriot to PRB (or 1 subway stop):
Also, if you bring your PAA presentation, there's a good chance we can arrange from group critique of it's visual characteristics. Likewise for yet-to-be-printed posters, but in that case, you'd need to arrange for quick printing yourself, which might cost more than your other options. Also, it'd be best to let us know well in advance (via that special email address!, not our personal ones), so we can have a chance to work it in. The venue probably holds 35-40 people. Organizers and host participants might end up being 6-8 of those, so space is limited. If you're interested, please email
paadataviz2016 (at) gmail.com
Here's the agenda:
Welcome (10 min) Peter Goldstein
Session 1 (40 min)
Audrey Dorélien Visual Perception and Best Practices for Tables and Graphs
Jon Schwabish Visualizing and presenting data better
* break (10 min) *
Session 2 (40 min)
Jonas Schöley Guidelines for using color
Tim Riffe Visual communication in demography
* refreshments (20 min)*
Small group critique of participants’ active work (2hrs)
Participants are encouraged to send the organizers a current version of a figure or table with underlying data. We will make a selection to work on in small groups. These will be shared with all participants, as well as the workshop results. Selected participants will be asked to present their problem very briefly before starting group work. This section is then organized as follows:
And some commentary on the agenda: part I consists in wise people imparting wisdom. Audrey and Jon (pending) will give broad and general comments and Jonas will give a rigorous presentation on color (where we could all do a better job!). I'll lead up the snarky part II, which will consist in finding published figures or tables in demography journals and improving them. I'll try to keep the variety up! Finally, part III, which we hope will be the bulk of the workshop, will be hands-on. Participants that want to can email us with a project: a figure from a paper in progress that needs some help, an aspect of a poster for this very PAA that still hasn't been printed, a slide from this very PAA that still hasn't been finalized. You'd of course also need to provide the data. If there is a big response we'll have to do a selection, but we'll play it by ear. So you'd present what the goal is and what's been done so far (like 2 or 3 min maybe), and then we'd break into working groups, each led up by one of the presenters/organizers. I expect we can all learn from each other in these excercises. We'll just take it as far as we can in an hour. This is not software-specific, so we'll just make do with what we have. I think we'll get some awesome results!
published on 45th anniversary of the article (August, 1970), with month -precision....
I've heard Andrew Noymer bring up Schoen's Δ (del) a couple times now in the context of 'good lifetable summary indicators that have been passed over, but without any obvious reason'. Usually it takes three to tip me over, but this time it was two mentions. Maybe because we're talking about demography tools. Well, it should have been one mention!
Δ is just the geometric mean of a mortality rate schedule, and it has lots of neat properties that you can read about in the short linked article. One of them is that ratios of Δ for two populations (or sexes, or years, etc) can be interpreted in a straightforward way: if the ratio of male Δ to female Δ is 2, then male mortality rates are twice as high. This is not the case for life expectancy or the age standardized death rates. If you halve mortality rates, you don't double life expectancy, and so forth. Age standardized death rates are arbitrary due to the use of a standard (even if some standards are common practice...).
So here are all Δ in the HMD (or their inverse, actually...), for both sexes (males blue, females red):
Talk about divergence!!!
If we want to study trends in divergence / convergence, and if we want to make group comparisons in mortality, there is a good argument that this is the measure we should be using. You can decompose differences in much the same way as we decompose differences in life expectancy, and so forth, partitioning the difference out to ages and causes. Just say'n.
Here's the code:
If you haven't seen the Time Flies page yet, you ought to, it's super cool:
That viz (can you call it a dataviz? there's no data... concept viz?) got me thinking. The basic notion is that the meaning of a year get's relativized to the amount of time you've lived. As we grow older, the proportion of our life that a given year takes up is less and less. This is all because our reference period (lived life) is growing. Anyway, it's a theoretical optimum, rather than actual perception, but it coincides with the anecdotes people have about time flying faster all the time. I googled a bit and found that plenty of people actually study the perception of time as a function of age. That's awesome.
I then thought, if you knew how long you'd live, then you'd know how long you have left, and this could be the reference rather than years lived. Let's call this, forward-looking relativized, as opposed to backward-looking relativized. Forward-looking relativization is symmetrical to backward-looking relativization. If you knew when you were going to die, you'd have both durations to relativize to. Then what would you think? How would this change the way you make decisions? Does running out of time make you savor? Does it make you slow down and notice details? At mid life, would we switch perspectives, always referring to the shorter segment of life (the one behind or the one in front). Maybe we'd take an average? But which kind of mean? When you don't know, use the arthmetic mean! And here's how the directionally-averaged perception of a unit of time looks by years lived, years left, and lifespan (in an ATL diagram).
You'd of course need to weight the lifelines in there, possibly using d(x) from the lifetable, or some other population weights. Plenty of imagining to do in this direction.
* In this case, the notions of fast and slow can easily flip, depending on what kind of mean you take ... In fact, even in the given arithmetic representation, you could swap slow and fast, and it'd still be legit. mental yoga.