NASA and climate change: from weather dice to bell curves

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Earlier this week the National Aeronautics and Space Administration (NASA)’s Goddard Institute of Space Studies published a new statistical analysis which has found that the Earth’s land areas are more likely to experience extreme summer heat now than they were in the middle of the 20th century.
 
They looked at average (mean average) summer temperatures since 1951 and found that the odds have increased in recent decades for what they define as “hot”, “very hot” and “extremely hot” summers.
 
Between 1951 and 1980 less than 1% of the Earth’s land area experienced “extremely hot”  mean summer temperatures. Since 2006 about 10% of land area across the Northern Hemisphere has experienced those temperatures.
 
As the weather is governed by natural ups and downs or variability, lead author, NASA climatologist Dr James Hansen and colleagues were concerned that very large variations might be disguising a trend. They turned to  Statistics to help them work things out.  NASA used global temperature anomaly data (data on how much warming or cooling regions of the world have experienced) collected betweeen 1951 and 1980 as a base period with which they could compare subsequent surface temperature data from 1981 to the present. In this way, they were able to better understand the relatively stable climate bewteen 1951 and 1980 and to compare this with the increasing frequency of extreme heat events in the following 30 years.
 
They developed a bell or normal curve*, a much used type of graph, to describe how those anomalies are changing. They found that summer temperature anomalies during the base period of 1951 to 1980 when the climate was relatively stable, fitted well with the normal curve.
 
With mean average temperature at the top of the curve, decreasing in frequency to the left of centre with “cold” “very cold” and “extremely cold” events. Decreasing in frequency to the right of centre are “hot”, “very hot” and  then “extremely hot” events.
 
When plotting bell curves for the 1980s, 1990s and 2000s, the curve shifted to the right, meaning that more hot events have become normal. The curve also flattened and widened, including a wider range of variability.  An average 75% of land area across Earth experienced summers in the “hot” category during the past decade, compared with only 33% in 1951-1980. Widening the curve also led to a new category of  “extremely hot” events being developed….events which were all but non-existent between 1951 and 1980.   
 
Dr Hansen says summer 2012 in the US looks set to fall into the extreme category.  Global maps of temperature anomalies show that heatwaves in Texas, Oklahoma and Mexico in 2011, and in the Middle East, Western Asia and Eastern Europe in 2010 also fall into the new “extremely hot” category.
 
NASA colleagues are now settled on bell curves as the statistical tool which most easily communicates the change in temperature anomalies, particularly at the extremes.
 
However, in the past NASA has used other tools to communicate variability and the growing frequency of extreme temperature events. In the 1980s, Dr Hansen introduced the analogy of loaded dice  On one of the six-sided dice, he painted two sides blue, two white and two red to represent the chance of a cold, average, or warm summer season.  On the other – loaded –  die, he painted one side blue, one white and four red to represent how climate models suggested the dice would roll by the first decade of the 21st century. 
 
Looking back, the dice analogy seem to be a fair reflection of how many sides would now be red as opposed to blue to represent today’s climate.  Of course, Dr Hansen would now have to replace one of the red sides of the dice with an entrely new colour – brown, to represent ‘extremely hot events’ !
 
* e.g. a typical class’s grades would include a B grade at the top of the bell curve and then B- and then C grades to the left side and B+ and A grades to the right, showing that there are fewest As and Cs, with more B-s and B+s and most of all – Bs.
 
 

Pills and probability

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Pop a pill and you’re cured. In media reporting it often seems as simple as that. Take aspirin daily and you won’t get cancer. Put more subtly, take aspirin and your chances of getting cancer are reduced by a finite amount.
 
Except they’re not. What we have is a body of evidence, being added to over the months and years by new studies, but not evidence of the kind that allows a straightforward ‘positive’ result.
 
The Daily Telegraph says a daily dose will cut cancer risk for the over-60s by 40 per cent, but that is not what the evidence says. An academic article establishes an association between daily aspirin use and modestly reduced rates of death from cancer. But however much we might wish the relationship to be solid and causal, an association is all it is: the reduced death rate in the studies could be the result of some other, unobserved factor (people’s weight, their education and so on).
 
The admirable NHS Choices gives the detail and reaches the sober conclusion that ‘overall, the evidence is not strong enough to recommend that everyone take daily aspirin purely for cancer prevention’ — and that’s because we don’t know enough about the risks of taking aspirin, especially for people without cardiovascular complaints.
 

Let's stop playing around with playing field stats

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For most of us, our first experience of sports is on the school playing field..this  assumes you are lucky enough to have one.  But finding out the number of schools with playing fields in England is not that easy as evidenced by the government’s recent revisions of the number of sold/on sale playing fields.
 
After all, someone has to count school playing fields…. and maybe counting them is more of a challenge than we might think: what do we mean by ‘school playing field’? which type of school? geographical coverage? period?  etc. 
 
In light of this, it was good to know that we were not alone in suspecting that recent claims that an estimated 10,000 school playing fields were sold off between 1979 and 1997 needed to be challenged. Where had this strangely high (and nicely-rounded) number come from? BBC Radio Four’s ‘More or Less’  team found that the figure was less of an estimate and more of a guesstimate or ’ball park’ figure.  A check of Hansard found that until 2007 opposition MPs had been citing 5,000 – and not 10,000 – as the level of playing fields sold by previous governments. Why had this statistic suddenly doubled?  And where had the 5,0000 and 10,000 figures come from?
 
Well..the 10,000 figure had been taken from a 2008 Dept for Culture, Media and Sport report. Yet delving deeper into the figure’s provenance, the paper trail soon went cold. 
 
The source of the 5,000 playing fields figure was dubious too……a Lords debate in 2001 revealed it to to be a “guesstimate” and not a reliable count.  ’More or Less’ researchers found that the ’method’ used to guesstimate the 5,000 playing fields was pretty much a case of how not arrive at an estimate. It began with a 1983 snapshot survey* to which snapshot figures for grant-maintained schools were added (in this case, they added 64 fields across a particular 30-month period in the mid 1990s.)  Then came the assumptions. It was then assumed that all schools were sold off at the same rate as grant-maintained schools (unlikely as grant-maintained schools are more free than most to use their budgets as they wish) and it was further assumed that between 1979 and 1997, playing fields were sold at the same rate as during this chosen 30-month period. As a precaution (!), the figure was halved and then rounded to 5,000. 
 
Either way, this figure has stuck. It’s what More or Less’s Tim Harford calls a ‘zombie statistic’ that just keeps coming back at you….even a parliamentary question failed to exterminate it. 
 
In defence of guesstimates, they can offer a useful first step on a path towards a more thorough count as more accurate information is found.  Sometimes guesstimates ignore better information. Sometimes they reflect the absence of better information. In this case, “no one really knows the true figure because nobody counted“.  ‘More or Less’ researchers found that playing field sales have only been counted since 1998 (and we still have no idea how many there were then or how many there are now).
 
They suggest that as governments say that they have mainly sold playing fields when schools have been sold, a better way of estimating school playing field numbers might involve looking at school closures…apparently 3,000 schools closed in England between 1979 and 1997, many in response to changes in the birth rate and so somewhere in here are some of the school playing fields lost during this period.
 
Suddenly, everything seems a lot less certain and begs the question ‘was a guesstimate of this kind the right figure to take centre stage in parliamentary debate?’.
 
* not a survey of fields sold but of fields under threat (and, in fact, not just school fields) 
 

Official records: Ours, theirs, or shared?

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In a couple of months, the Administrative Data Task Force chaired by Sir Alan Langlands is due to report. Langlands, chief executive of the Higher Education Funding Council for England, is working on behalf of the Department of Business Innovation and Skills and the research councils on how the mass of data collected by public agencies about individuals and households might be pressed into more fruitful use.
 
More intense use of admin data could cut the cost of research and – for example in health – allow better testing of therapies and changes in medical practice.
 
But the exploitation of ‘personal data’ worries people, amid fears of Big Brother. Professor Ross Anderson fears confidentiality could be breached and our private lives made public knowledge against our wishes.
 
This is a debate RSS getstats needs to monitor closely. It seems there’s a class of number that the public feel ambiguous about; if alarmed, they might become more suspicious of statistics and impede efforts to promote a ‘numbers mindset’ and enlarged understanding of the necessity and beauty of stats.
 
Often, debate is confused. Are the numbers collected by the state public or private? In the UK tax records are regarded as highly confidential. In Scandinavia, what citizens owe the state is seen as legitimate public knowledge. Many public services are consumed collectively (public health and safety and education for example), and their statistical base needs to be open and shared.
 
The public are often very keen to see personal data – for example about people regarded as a threat to the community. The Cameron government, following its predecessor, wants maximum transparency for data from public bodies, as a way of holding them accountable: doesn’t that imply reciprocal transparency, affecting the data they collect from the public?
 
It feels like we’re on the edge of a new era in the use of administrative data, but that public attitudes are not yet fixed, and there’s a lot of argument yet to be made and won.
 

Getting fit won't win medals (but may save your life)

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The Olympic games have been a feast of numbers but one important set has gone missing. They are even more missed now the party is over and all the talk is about legacy, especially the effect of British sporting success on our everyday exercise and activity.
 
High levels of obesity prompt commentators and policy makers to reach for ways of stimulating an ageing population to get up and go, and what could be more inspiring than Olympic medals? The King’s Fund, the health policy thinktank, is one among many speculating about a post-Olympics boost, benefiting the area the games were supposed to regenerate in the east of London.
 
But where are the statistics linking attainment on the athletics or cycling tracks and activity levels among the population at large? Intuitively, young people might look up at the stars and tell themselves they too could mount the podium. But what 60-year old couch potato is going to hoist themselves on to a saddle and announce they are going to emulate Bradley Wiggins and get up the Col du Tourmalet ?
 
The relationship between elite sporting achievement and popular sporting participation does not have to be direct and could even be inverse – in the German Democratic Republic, for example, top sports people were in special programmes, while the public got on with life as best they could, smoking and drinking and ingesting polluted air.
 
There’s no ready evidence that high-profile events lead to waves of emulation and, besides, what is there to emulate. To get to Olympic standard is work – as the athletes pointed out – of decades and the most intense dedication. Is the contention that you see a gold medal performance then rush out to try to be at best adequate in your chosen sport – because being adequate is the most the majority of triers could ever hope for.
 
Exercise is good. Activity reduces the likelihood of the onset of life-threatening conditions. These are excellent nostrums of public health. But don’t let’s confuse them with our largely passive admiration of sporting achievement or assume that because Team GB did well that fact will cause any changes in behaviour.
 

Midata scheme set to make us all consumers of data

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You may have heard of the Cabinet-Office-initiated Open Data project, which provides us with more access to government information/official data. You may be a regular user of data.gov.uk . You might be interested to know that there is now a new kind of parallel scheme called Midata which is set to make consumer data more readily available to us all.
 
The people – the companies, the organisations – we do business with know a lot about our purchasing habits and they have the data to prove it. So far, although the Data Protection Act already allows us to ask any organisation – including private companies – to show us the information they hold on us, few of us seek it out and so the data these companies have on us has mainly benefited them.  Now the government is pushing for us all to have easier digital access to all our personal transactional data so that knowledge of our patterns of spending can benefit us too.
 
Under the Midata scheme, we should be able to use the information in the data held on us to, for example, monitor our usage of utilities, take our usage profile to new providers and, if we want to, switch products/organisations to save money.
 
On this morning’s ‘Today’ programme, Brian Glick, editor in chief of Computer Weekly and John Hayes, Minister of State for Further Education, Skills and Lifelong Learning said that the government’s new scheme is about  “allowing (consumers) to make more informed decisions”.
 
Even if the data – it’s about us – is data we will be somewhat familiar with, there will still be lots of different levels of experience of interpreting patterns in data so the new scheme will need to consider its users’  needs and abilities and make sure that it is supplying data in the most helpful form. It is good to hear that consultation on this is already underway (set to close in mid September).
 
Whist there are  26 large companies (e.g. EDF, Mastercard and British Gas) signed up to the scheme so far, there are plans to make this approach to opening up consumer data compulsory.  It will be interesting to see how quick companies are to get fully behind this scheme and  – very importantly – how much appetite there is on the part of the consumers for this data.
 
From a getstats perspective, it will simply be good to learn more about how to interest and engage the wider public in the use of useful data.
 

More workers, less work

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British workers are noticeably less productive on average than those in comparable countries. According to the Office for National Statistics, output per worker was 20 percentage points lower than in the other top countries, which make up the G7.
 
Output per worker has fallen, relative to most of those countries, since the recession started in 2008.
 
But the figures may reflect a characteristic of the UK labour market that has shown through other recent data. It’s not that British workers are noticeably less productive, at least in a European perspective. Last year the amount produced each hour by each worker grew in line with the average and faster than in the US and Germany.
 
It’s that more staff are employed, and that pushes down the average output per worker. Despite flat market conditions companies have maintained their staffing levels, preventing a big rise in unemployment.
 
No single firm or employer is making a trade off between keeping employment levels relatively buoyant and some falling away of the output of staff, but at an economy-wide level, we seem to making a collective choice in favour of jobs over output.
 

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