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The Development Team
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October 4, 2010– 5

Based on the vast number of queries we have been receiving from users all around the world, we thought it would be very interesting to draw some inferences from it. We started with “Human Body Measurements”, one of the many topic areas in Wolfram|Alpha. We thought it would be a safe assumption to make that in more cases than not, when users query for data based on weight or height values, they are most likely looking for data about themselves (narcissism, thy name is Homo sapiens). Based on this assumption, we plotted all of the height and weight inputs and ended up with the following distribution:

weightvsheightnonmetric

We can see from this that the average Wolfram|Alpha user is an individual who weighs about 154 pounds and is between 5′ 9″ and 5′ 11″ tall. This translates to a BMI of between 21.5-22.7 for men or women. From these results, we see that the average user falls within normal distribution.

Let us see how this hypothetical Wolfram|Alpha user compares with the average American male or female:

Male weight distribution

Female weight distribution

Similarly, we can compare user heights with the height distribution of the general population in America: More »

June 3, 2010– 2

Since Wolfram|Alpha launched in 2009, we’ve had numerous requests to add data on climate. As part of our one-year anniversary release, we recently added a vast set of historical climate data, drawing on studies from across the globe, which can be easily analyzed and correlated in Wolfram|Alpha.

You can now query for and compare the raw data from different climate model reconstructions and studies, as reported in peer-reviewed journals and by government agencies, many of them covering more than a thousand years of history. The full set of reconstructions was chosen from as broad a collection of sources as possible, from well-known records such as ice cores and tree rings, to corals, speleothems, and glacier lengths—and even some truly unusual ones, like grape harvest dates.

Or are you more interested in global greenhouse gas concentrations?

If you’re interested in exploring this vast area of climatology yourself, you can start by looking at a detailed summary of the most prominent models in literature: simply ask Wolfram|Alpha about “global climate”, which will bring up a selection of data sets that have figured prominently in the news over the past few years.

Global climate studies in Wolfram|Alpha

Wolfram|Alpha can also compute a more local analysis of recorded temperature variations. For example, you can compare the temperature variations recorded in specific parts of the globe, like the Northern Hemisphere. Or you can ask about studies conducted in specific countries, like the United Kingdom or Japan. More »

June 1, 2010– 11

We’re in the midst of major enhancements to military data in Wolfram|Alpha, with newly added information on army, navy, and air force personnel for over 150 countries as well as statistics on many armaments, including stockpiles of nuclear warheads.

Let’s start with the big numbers. Type “army size of all countries” and you’ll see China, India, and the Korean Peninsula topping the list. China’s army alone includes 1.4 million soldiers and dwarfs the population of many smaller countries. The size of its combined army, navy, and air force is nearly equal to the entire population of Macedonia.

The size of China's combined army, navy, and air force is nearly equal to the entire population of Macedonia.

There’s an abundance of data on armaments, around the world as well, including estimates on nuclear stockpiles of the nine countries known to have detonated nuclear weapons; according to the latest available estimates, Russia has the largest stockpile with 13,000 warheads. Also new in Wolfram|Alpha are figures on conventional weapons, including aircraft carriers, battle tanks, and fighter jets. Try comparing countries’ armaments, such as “tanks USA vs Russia”, or asking about the number of submarines in the NATO alliance. More »

May 24, 2010– 4

We recently added data on health indicators for more than 200 countries and territories. We now have World Health Organization data on health care workers, immunizations, water and sanitation, preventive care, tobacco use, weight, and more.

Data is also now available on specific types of health care personnel, such as physicians, nurses, and dentists, and Wolfram|Alpha can also compute per capita figures for each type of health professional. Check out the figures on midwives in South Africa or dentists in Iceland—or for a particularly interesting view, try asking about doctors per capita in all countries.

Other intriguing indicators include figures on hospital beds, drinking water and sanitation, tobacco use, weight and obesity, and reproduction and contraception.

Data on underweight children in Africa

Some data, such as for infant immunizations (including DTP, MCV, hepatitis B, and Hib), spans several years—which allows you to see dramatic increases in immunizations in many developing countries, as well as surprising declines in some first-world nations. More »

February 3, 2010– 10

When Wolfram|Alpha was introduced, Stephen Wolfram blogged about it being the first “killer app” that resulted from his work on A New Kind of Science (NKS). We can now use this application of NKS to further our exploration and study within the NKS field. For example, one class of systems discussed in NKS is that of substitution systems. Now that a host of string substitution systems have been integrated into Wolfram|Alpha, we can explore a variety of these systems—not just the ones that are well known.

A string substitution system is composed of two parts: a string and a set of rules. The string looks like a series of numbers, say “0″ and “1”. The rules describe what happens to each number in the string; for example,  “1” -> “0” and “0” -> “10”. Under our rules, our example string, “1”, transforms to “0”. In true NKS fashion, repeated iteration of these simple rules can give interesting behavior. Our example, which seems deceptively simple, can model the Fibonacci numbers. We simply document the length of the string each time we apply the rules to find that the series of lengths obtained at the end of each substitution corresponds to the Fibonacci series: {1, 1, 2, 3, 5…}. We see this in the following result:

Fabonacci-related sequence

Similarly, there is a string substitution system that models the Cantor set. The rules that define this substitution system are 1->101 and 0->000: More »

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