Hurricanes with male names are at least as deadly as those with female names

Lots of news services have been passing on the startling conclusions of a recent academic paper in The Proceedings of the National Academy of Sciences, a quite high-impact journal, that, and these are direct quotes from the paper in question's abstract:
feminine-named hurricanes cause significantly more deaths than do masculine-named hurricanes. Laboratory experiments indicate that this is because hurricane names lead to gender-based expectations about severity and this, in turn, guides respondents’ preparedness to take protective action.
I'll just show you this graph I made with the data published in the PNAS paper, then explain further below (click to enlarge):



The blue (male) line is almost always above the orange (female) line. What gives?

I was certainly not the only person to be skeptical of the paper's conclusions; Tyler Vigen used his usual satirical approach to good effect, showing how remarkable spurious correlations can be. But the thing is, this idea that people wouldn't take female-named hurricanes seriously as a threat may sound dubious, but it also sounds plausible, and I imagine it pushes some buttons.

I am not at all qualified to comment on the soundness of the social science in this paper, but I do think the data analysis is quite flawed.

First of all: it's kind of a rule that one should compare apples to apples. Before 1979, all hurricanes had female names. While this by itself does not invalidate their hypothesis that, all other things being equal, a feminine-named hurricane will result in more deaths, all other things were not equal before and after 1979. Off the top of my head, back when hurricanes had only female names, meteorologists were not as good at predicting the severity and paths of hurricanes as they later became with the help of experience and, especially, computers, and communications technology was not as good at relaying information and evacuation orders. Neither of these factors were addressed in the paper; isn't it worth looking into whether other factors besides name gender contributed to deaths?

So if we limit our analysis to post-1979, when we can directly compare male and female names of hurricanes, the masculine hurricanes caused more deaths up until 2012's Sandy. At present, the feminine names are barely ahead, 459 to 413. This is at least counter-evidence to the paper's claim that "changing a severe hurricane’s name from Charley to Eloise could nearly triple its death toll." (To be fair, they didn't just look at whether a name was male or female -- I know one male named Sandy -- but how "masculine" or "feminine" a study group considered the names. This doesn't change the fact that they ignored much more plausible reasons for deaths prior to 1979.)

I find it curious that the researchers limited their analysis to American deaths; hurricanes kill a lot more people before they ever reach the United States. Of course, a greater proportion of non-Americans are too poor to shelter or evacuate, but this strikes me as a combination of partial cherry-picking, circular reasoning and insufficient research: they limited their data to people affluent enough protect themselves against a hurricane, and then claimed they died because they didn't protect themselves a hurricane, without actually looking at whether or not they protected themselves against a hurricane.

For example, the second-most deadly hurricane on their list, Diane, killed 200 people in 1955 despite being only a Category One (Five is the strongest) for which evacuation orders are rarely if ever given. The reason it was so deadly is that Hurricane Connie passed through the same areas in Pennsylvania and Connecticut a few days before, saturating the ground so that Diane caused massive floods.

At least they left Katrina off the list; I think most people would agree it's probable that there were some social, economic and political factors that contributed more to its 1,833 deaths than its name. But their reason for considering Katrina an outlier was that it "leads to a poor model fit due to over-dispersion." There's kind of another rule in data analysis: you don't choose your data to fit your model, you choose your model to fit your data.

I will admit that the researchers' laboratory studies succeeded in convincing me that the people they studied (including Amazon Mechanical Turk users, not exactly a representative cross-section of people who might ignore a hurricane) answered questions in such a way that they appeared to assign lower risk to hypothetical hurricanes with more feminine names. It's just rather a stretch to claim that:

(a) This laboratory result is truly an indicator that in a real-world scenario these people would actively ignore the risk of dying in a hurricane; and

(b) That there is any risk-ignoring behavior correlated with hurricane deaths at all. (There very well might be. But the researchers didn't even attempt to find out. There was no historical data, no text mining of contemporary news sources, just a bare minimum of meteorological data, damage and death assessment.)

PNAS is a good journal (and always a barrel of laughs when you say the acronym out loud). I'm sure they'll get it better next time.

UPDATE Randal Olson, who is definitely an expert in such matters, pointed out that a more convincing graph would be one that showed deaths from hurricanes were more frequent in general before 1979 when they started giving them male names. So I whipped one up quick in Excel. Katrina of course incredibly skews the aggregate data, but you can see it was more common for any individual hurricane to have over a handful of deaths before 1979 (click to enlarge)


I didn't control for storm severity as Randal suggested, but I'm reasonably confident it will change nothing: the six storms that caused more than 100 deaths were categories 1, 1, 2, 4, 5 and 5.

Use of the f-word in Eddie Murphy: Delirious

I had planned to take a break from blogging during the holidays, but today I saw this post on reddit about the use of the f-word in movies in the dataisbeautiful subreddit, and I was inspired. The top movie on the list I had seen was Eddie Murphy: Delirious; I was 13 when it came out, but nobody I knew had HBO, so my best friend and I had to wait till it showed up in the Betamax tape rental place. We made a lo-fi audio recording (a microphone held up to the TV speaker), and soon had it memorized and spent several years quoting it in all sorts of inappropriate situations.

So, let's break down the use of the f-word (I admit, I'm being a total wuss, Google hosts this blog and I'd rather not deal with any automated fallout from using profanity, so I'm going to asterisk out all the naughty words) during the movie. Some simple poor man's calculus (for each use of the word at time x, y equals the inverse of the average of the times of the previous and next use) shows the clustering of swearing during different parts of the film:


It would be great to know what parts of the movie those clusters correspond to: if you go to the bottom of the post, there's a reversed version of the graph that allows you to see the dialogue (lightly Bowdlerized, again, I'm sorry) line by line.

I've been learning how to do Natural Language Programming in Python, and while I didn't bring out the big guns, I thought it would be interesting to look at some of the simple patterns in word use in the movie: 


Normally I would use a stop list to remove common words like "the" and "and", and a corpus to compare word frequencies, but I think the raw data is the most informative perspective, showing how the profanity rivals the most common syntactic words in Delirious. Here are the top N-grams (words that appear side-by-side):



I'm a contributor to the FullMovieGifs subreddit, so I couldn't resist the temptation to make one of Delirious. Hopefully Google doesn't OCR these things; if you want to see it larger, click on it.




Finally, here's a big, vertical version of the first graph in the blog, which you can mouseover to read the lines of dialogue (is it still called dialogue when only one person's talking?) to your heart's content. If you can't see a really huge graph right underneath this sentence, click here to see it.


I think I'll be hearing from my mom about this post.
Update Jan. 1, 2014: Whaddaya know, my mom was fine with it.

Weight of small change in USA, EU, UK and Canada

I'm interested in how much the SMALL change weighs, I don't want to get into the dollar bill/coin debate.
The graph is interactive, feel free to click and hover. [Blogger seems to be finicky with javascript; if you don't see a big interactive graph right underneath this sentence, click here.]
Bottom line: Brits need good pockets.


I started learning javascript a couple of months ago, and I'm comfortable enough to be able to lean heavily on a package and wrangle the API to give me what I want. Today Highcharts, tomorrow, D3.js!Coin weights are taken from Wikipedia.
If anyone prefers to see a simple non-interactive image, click on this:





Boston Celtics retired jerseys by year: when will they run out of numbers?


I'll admit I'm not a huge sports fan, but I am a huge numbers fan, and sports produces a lot of those. It also produces a lot of analysts: after all, there's lots of money riding on much of these numbers. So it's a bit of a challenge to find something original, and by definition it's going to be a bit frivolous.

It occurred to me that if teams keep retiring numbers and don't expand the pool of possible numbers, eventually they will run out. A bit of Googling revealed that the Boston Celtics have the most retired numbers of any major professional sports team. The NBA allows 100 numbers, from 1 to 99 and 00; they've retired 21 in the past 40 years, so a simple linear fit shows that at this rate they will run out in a couple of centuries.

I wouldn't worry about this problem too much; the Celtics have already shown how to solve it. When they retired Jim Loscutoff's jersey, he requested that they not retire his number (18), so their banner reads "LOSCY" instead. Later, Dave Cowens spoiled the gesture by wearing the same number and having it retired.

It occurs to me that I've seen these kinds of stepwise and extrapolation graphs on xkcd (e.g. here and here), except of course Randall Munroe is much better at them than me. So I decided to do a little tribute and rework the first graph xkcd-style using Dan Foreman-Mackey's xkcd D3.js template. My javascript skills being what they are, this was by far the longest part of this project; but it was a labour of love. I hope everyone will forgive me.


Population of Canada by latitude



Update: here's my final edit of the chart; I think the city labels are much less misleading now. I've come across a much more fine-grained data set, albeit from 1995; you can see it in my Nov. 27, 2013 blog post.



Here's the original, which seemed to imply that the bars were only made up of population from the indicated cities, whereas the bars indicate the population of the entire country at the same latitude of those cities:



A co-worker and friend happened to mention that Vancouver was further north than Montreal; I sort of knew that, but I was surprised to find out it was 400 km further north. So I was curious, and tried to find a histogram of Canadian population by latitude; maybe my Google fu was lacking, but I couldn't find one, so I decided to make one myself.

Little did I know what I would discover; that data is not easy to obtain. There is lots of population data available for download from the Statistics Canada website, but it does not contain geographical coordinates, and StatsCan uses its own defined areas called census subdivisions. They have available for download geographical boundary files, but they would have required an amount of computation rather disproportionate to the task of simply determining latitudes.

Luckily, StatsCan also makes the population available by Forward Sortation Area, the first three letters of the Canadian six letter postal code, e.g. the FSA of the Canadian parliament at postal code K1A 0A9 is K1A. So now it was just a matter of finding out the latitudes of FSAs or postal codes. Simple, right?

Wrong. Canada Post considers its postal codes intellectual property subject to copyright; a license to use and analyze it costs $892 a year for StatsCan's info, and over $5000 for many business products. They are suing a website for providing information on postal code geography. Universities used to be able to access Canada Post's geographical data, but no longer. I work for a university, and the reference library has someone who is able to take the publicly available ArcGIS files and determine the centroids using the expensive proprietary commercial software for which the university has a license.

So: the population data is divided into 1600 FSAs, which is pretty decent resolution. The centroid (geographical center) for most postal codes fits reasonably well within the 0.5 degree latitude (about 55 km) resolution of the graph, except of course for the very large FSAs the farther north you go. But in any case, these areas would have had to be aggregated somehow to even be visible on the scale (for example, if if the northernmost FSA, X0A, were spread out among its 14 degrees of latitude), so I think this is a reasonable compromise.

A note on the city labels: I tried to give the largest municipalities that contributed to the population in each bar of the histogram as an aid to understanding, not as a systematic data set. This became difficult for some of the larger FSA's; it was difficult to match the latitude of a town with the latitude of the centroid of its FSA. So in some cases, I may have used a town with a population of 2,000 when there was a town with 3,000 people at the extreme north or south of the FSA. And a note about Edmonton: it straddles two bars because the center of the city is almost exactly on the demarcation, 53.5 degrees north. Edmonton is a bit smaller than Calgary, but there are other sources of population in each latitude than the city mentioned, so do not draw the wrong conclusion from the size of the bars.

You can peruse the data I used in this Google Doc.

Comments are welcome, even, nay especially, critical ones.

EDIT 2013-10-16 14:49 GMT: Montreal straddles the 45.5 degree latitude, and by marking the 45.5-46.0 bar as "Laval", the graph appeared to be indicating that Laval had a larger population than Montreal. I've explained how the labels are generated, but it's an obvious conclusion to draw from a glance at the map without reading the methodology (and the methodology had to be tweaked for Edmonton and Montreal, which straddle the cusps of the graphs, and the centroids of the FSAs are problematic to begin with). Clarity is the most important thing, so I've updated the bar to read "Laval & Montréal". Thank you to the commenters in Reddit's dataisbeautiful forum for pointing this out.

EDIT 2013-10-16 15:33 GMT: When you're wrong, you're wrong, and I was wrong. My labels were utterly misleading. Now I have put the major contributor AND every Canadian city with over 100,000 population on the graph. I had intended the labels just as a geographical reference, but I definitely did not think through what fresh eyes coming to the graph would think.

EDIT 2013-10-16 21:53 GMT: These labels are really getting me in trouble. I produced the graph first without them, but I envisaged a torrent of "You should have indicated where these people live!" I've removed the most northerly ones, because again, they're misleading. Lesson learned: less is more.

EDIT 2013-10-16 22:41 GMT: Added hi-res version without labels. I think that's enough editing today. Enjoy! And thanks for all the feedback! The vast majority of it was very constructive, it's appreciated.