Death and Cases

July 15, 2020

Death and taxes are certain: cases and death are certain too in the USA these days. (We’ll, rich people certainly don’t have to pay taxes…)

The steeply rising curve shows the number of new confirmed COVID19 cases reported each day in the United States, minus the Tri-State area of NY, NJ, and CT. The declining curve at the bottom shows the number of new cases in the Tri-State area, which was the epicenter of explosive growth in the infected case count a couple of months ago. Remember that? These curves are plotted against the axis on the left.

The black line is the number of reported deaths from COVID19 reported each day throughout the USA, and it is plotted against the right-hand axis. The Tri-State area, like most of the European Union, even Italy, which was at first Exhibit A for COVID19 disaster, has gotten a strangle hold on the virus: new cases are precipitously down, and new deaths are in the single digits. If only the rest of the country will stay away, maybe we can stay safe!

The death rate is still relatively low, but it has stopped declining, and it rising. It’s only a matter of time before the spreading virus leaves the community of those bar hopping young ‘uns and hits the more vulnerable population, pulling up the death rate. (Young people die too!)

Can Trumpy Kool-Aid drinkers ignore a death toll of more than 250,000 by election day?

Hot Time, Summer in the City…

September 11, 2018


click here for larger image

I just cannot stop thinking about this graph that appeared with this article in the NYTimes recently.  The piece discussed how the number of hot summer days, those above 90 degrees F, are projected to increase in the future, and it allows readers to enter their town and date of birth to see how the weather has changed between then and now.

Hmmm….  Well, we all know that climate is always changing, and we all know that it is warmer now, in general, than it was 100 years ago, but beyond that what does this article and its interactive graphic tell us?

I imagine that a lot of readers misinterpret the data plot and believe that it represents the rise in temperature in NYC over the recorded period:  my experience is that most readers of these articles in the Times are not too concerned with details of data and data presentation.  In fact, it is more accurate to say that the chart shows the number of “above 90-degree F days” in NYC over the period.  That is, a count of days, not temperatures.   Except that it doesn’t show that…  On the left there is some text that says that it shows the “average number of days above 90-degrees F.”  What does that mean?

If we look at the data point for the year 2010, we find a value of about ten days.  Ten days above 90F in 2010?  You could easily check the record to see if that is accurate. But the text says that ten days is the “average number” in 2010.  In that year, there were either ten days above 90F or there were not ten days.  An average does not enter into the discussion.  That would be as if we said that June, on average, has thirty days.

The confusion is eliminated when we read the FAQ and Methodology document to which a link is provided at the end of the article:  How many people do that, do you think?  We learn that the data plot shows a twenty-year moving average of the above 90F days for each year.  For example, for the year 2000, the number of above 90F days for 1990,1991, 1992…2000…2008, 2009, 2010 are added up and and divided by twenty-one (there are twenty-one years’ values) and an average is obtained.  For 2001, the same process is used, but the summed years begin with 1991 and end with 2011.  Moving averages are often used to smooth out the data curve:  in this case, without doing it the plot would be very “spiky” with sudden changes in the number of above 90F days from year to year.  Smoothing the data gives a better idea of the trend, but it is good practice to make clear up front that you have done so, which the authors of the piece do not do.

On the other hand, what about the years 2008 through 2018?  For example, take the year 2015:  we get a twenty-year moving average by summing the data from 2005 to 2015, and adding that to the data for 2016 to 2026…  Oops!  There is NO DATA for the years after 2017!!  The kindly scientists at the Climate Impact Lab of Columbia University have used model data, simulated data, or shall we say, created data in place of actual historical data.  They do, obliquely, note this fact in their FAQ and Methodology text, but you’d never know it by looking at the graph.

Consider this:  their models show temperatures rising and above 90F days increasing, so the tendline after 2017 is rising.  But unlike the rest of the graph, that is NOT actual recorded data.  For all we know, the data record during that period is flat, or perhaps moving downward.

And speaking of flat data records, at least in NYC, the period from 1990 to 2017 (keeping in mind that the data for 2008 to 2017 is not actually the historical data) looks pretty much horizontal, i.e. constant, not increasing.  But sure enough, we can be completely confident that the upward trend that begins…next year, will come about.

Well, we cannot be completely sure because the Climate Lab also tells us – they are honest, if not forthcoming – that the results plotted here represent the data range that two-thirds of the models project.  I’m used to hearing the IPCC and other outfits talk about high or very high confidence in projections, i.e. a 90 or 95% confidence interval, but here we have a “just likely,” …mebbe… confidence interval of 66%.  Of course, this is simply a statistical sample of modeled results, described with the unspoken assumption that the models are correct, or nearly correct, or more correct than not correct… 🙂  If all the models share a few assumptions and parameters that later are disproved, then the fact that 66% predict this is hardly something to inspire confidence.  This, by the way, goes for all the climate projection models.

It would be nice if this graph for NYC were to be published every year in the NYTimes.  Then we could see each year how accurate the projections actually were.  Instead, this plot will be forgotten, and next year there will be a new batch, showing the rise in this or that frightful metric after the fateful year at hand.

Of course, it could happen exactly the way they are claiming it will.  We shall see…!

Shoot the Messenger: Comedy & Criticism with Nate Silver

November 4, 2012

Click for Richard Sala source.

Statistical reports and observed reality do not always correspond, as my favorite comics artist, Richard Sala, illustrates with the image above.  This gives an opening to right-wing critics of the statistician Nate Silver, who has consistently rated Obama the favorite in this election at his blog,  I find the attacks on him to be laughable:  yes, he says he votes Democratic; yes, he has strong opinions on the importance of state as opposed to national polls; yes, he predicts the popular vote to be rather close but still rates Obama at more than 80% likely to win.

So what?  As they say in the pundit world, “Here’s the thing…”  In a few days the election will be over and we’ll see whose predictions and analysis were good, and whose were bad.  Let’s just wait and see, heh?

OWS: The Beautiful and the Arcane

October 16, 2011

At the Occupy Wall Street site yesterday, I saw some people wearing a small enamel lapel pin with this design.  I searched in vain for the man who was giving them away – I want one!  It beautifully expresses the facts of income and social inequality in a clean, concise, and compelling graphic.  Bravo to the designer!

Occupy Wall Street + Walter Benjamin +Pauline Christianity = Anaphoric Solidarity.  Whaa?  One of the strangest amalgams of intellectual systems I’ve come across, represented at OWS by two young men at a small table in the center of Zucotti Park.

Playfair with Images

December 12, 2007


Playfair’s Commercial and Political Atlas and Statistical Breviary, much beloved by Tufte and others, is a monument in the history of communication with imagery. The simple chart here, showing the balance of trade between Norway and England as a time-series dual line plot looks totally modern and familiar to us, but was an incredible novelty in his day. Nor was he limited to linear charts: he worked with bar charts, innovative pie-charts, and combinations of several chart formats.

The text, now available in a complete facsimile edition at the link provided above, is, in addition, wonderful to read. If you enjoy reading intellectual strivers of the Enlightenment, as I do, you will enjoy this book thoroughly. He deals with sophisticated issues of data presentation and analysis in language so plain, you wonder how we got into our present mess with statistics being always associated with incomprehensible jargon. He also gets in some zingers against Adam Smith, with whom he had some differences.

Today we are inundated with statistical graphs, so it’s hard to accept that in his day, Playfair’s innovations were regarded with suspicion! The very informative introduction to this edition describes the intellectual prejudices of his day against graphical display of information. So much for a picture being worth a thousand words – in those days, they preferred the words! Pictures were thought to be unreliable, and subject to all sorts of hidden error, while words could be parsed to the bone to cut away the fatty tissue of falsity. It was Playfair’s genius to turn this on its head successfully, although he personally never made much of a go of it financially.