If you’re concerned about the US economy, you probably caught last week’s news that Standard & Poor’s Case–Shiller home price index for 20 large cities continued to decline in January. If you’re curious to know more about recent housing trends in the US, you can not only ask Wolfram|Alpha about the 20-city index, but also for details on any of the major metropolitan areas included in that composite. For example, query “Las Vegas, Phoenix, Los Angeles, Miami Case–Shiller index”, and you can see just how big the housing “bubble” was in each of these four cities.
As kids start to return to classes after the holidays, we’re happy to announce that Wolfram|Alpha has the ability to compute some interesting information about their school districts. You can now use Wolfram|Alpha to analyze and compare data on student-teacher ratios, expenditures, revenues, and salaries in more than 18,000 public school districts in the United States.
Let’s start with an example on the West Coast: Seattle Public Schools is one of the larger districts in the country, with over 100 schools and more than 45,000 students. The student-teacher ratio is 18:1, and if you scroll down you’ll see that total expenditures are about $14,000 per student per year.
Oh, the weather outside has been mighty frightful in many parts of the U.S. and Europe these past few weeks! Your mother has told you, and we will remind you, that it is never a good idea to forgo your mittens during cold weather.
How many times have you dashed outside to find that the advertised temperature does not feel the same as you had expected? The wind plays a big role in how the air temperature feels on your skin. For example, today in Champaign, Illinois, the temperature is 21 degrees Fahrenheit, but factor in the wind, and it feels like 9 degrees Fahrenheit outside. Enter your current city in this handy widget and it will provide a wind chill temperature. (The widget is live, so go ahead and try it!)
Icy temperatures can cause frostbite, a condition where tissue such as skin is damaged, and in some cases destroyed, due to exposure to extreme cold. As we encourage our users to create their own widgets, one of our users arwheelock did so by creating a popular related Wolfram|Alpha Widget. This widget allows you to quickly compute how long your skin can be exposed to such weather conditions before becoming susceptible to frostbite. By simply entering the temperature and wind speed for your location, Wolfram|Alpha will tell you approximately how long your skin can be exposed to the conditions before developing frostbite.
So whether you’re off for an evening of caroling or an afternoon on the slopes, be mindful of the risks associated with leaving your mittens (or other cold weather gear) behind.
When we talk on this blog about “making knowledge computable”, the knowledge in question is often mathematical or statistical in nature. But that’s not the only knowledge Wolfram|Alpha can compute. We’ve always had a solid backbone of dictionary-style information about words, but we’ve been steadily adding new features to that traditional output. Some of it should be quite useful, some of it is just for fun, and much of it takes advantage of Wolfram|Alpha’s ability to mash up algorithms and data from a wide variety of knowledge domains.
To celebrate National Dictionary Day (October 16)—which honors Noah Webster, often regarded as “the father of the modern dictionary”—you might like to take advantage of this classic word widget, which provides quick access to some of the more traditional areas of Wolfram|Alpha’s lexicographical data: definitions, pronunciations, synonyms, and more for most English words.
Or grab the next widget if you want to play around with a few of the “fun” features we’ve added, including the ability to compute anagrams and convert words to telephone keypad digits. More »
Renowned physicist Enrico Fermi’s name is synonymous with a type of estimation problem often illustrated by the classic question, “How many piano tuners are there in Chicago?” Finding a “Fermi estimate” of this number would typically involve multiplying a series of rough estimates, such as the population of Chicago, an approximate number of households owning pianos, the frequency with which a typical piano might be tuned, and so on. It’s unlikely that anyone would arrive at a precise, correct answer through this method, but a Fermi estimate should at least be able to generate an answer that is approximately the right order of magnitude.
A Fermi estimate usually seeks to measure a quantity that would be extremely difficult, if not impossible, to actually measure. “Piano tuners in Chicago” may have fallen into that category several decades ago, but as Wolfram|Alpha can now demonstrate, things have changed:
We recently overhauled our data on jobs and salaries in the United States, adding Bureau of Labor Statistics (BLS) data on more than 800 detailed occupations at the national, state, and metropolitan area levels. Which means Wolfram|Alpha can’t quite get you to an exact measurement of the number of piano tuners in Chicago (and presumably, many of them must at least dabble in other instruments), but it can come surprisingly close.
Wolfram|Alpha can also compute a number of interesting statistics that aren’t obvious from the source data, such as the fact that Chicago has quite a high density of musical instrument tuners and repairers—roughly 2.3 times the national average workforce fraction for this occupation—and that their median wage is roughly 1.3 times the national average. And it can also provide helpful context for any occupation, computing employment and wage information for related jobs and sub-specialties, according to BLS classifications.
You can also perform all kinds of interesting comparisons, of course: try asking Wolfram|Alpha to “compare producers and actors employment in California”, for example, or “garbage collectors vs waiters salaries in New York City”. Or if you’re contemplating a cross-country move, you might be interested to see a comparison between “computer programmers salaries in Seattle vs Philadelphia”.
And if you need to access salary and job-related data often, you can create your own Wolfram|Alpha Widgets tailored for specific jobs and regions. You can easily customize widgets, like the one below, and embed them in your website and share with your social networks.
Here at Wolfram|Alpha, we’re busy curating new data and knowledge from around the world. And as new data rolls in, we’re exploring how it might connect and provide insights to existing datasets. Since the launch of Wolfram|Alpha you’ve been able to explore a number of properties for cities, such as population, geographic properties, location and map coordinates, current local time and weather, economic properties, crime rates, and more. Now, thanks to a recent coupling between people and city data, Wolfram|Alpha can not only tell you that Memphis, Tennessee is the Home of the Blues, but it can also tell you that it’s the birth and/or death place of notable people such as the King of Rock ‘n Roll Elvis Presley and civil rights activist Martin Luther King.
At the present time Wolfram|Alpha contains deaths and births for some 38,000 notable people from around the world in places such as Cape Town, South Africa and Oxford, United Kingdom. Are you wondering where all of the data for notable people in Beijing, China and some other cities is hiding? Given the busy nature of birth and death data, we’re reaching out to Wolfram|Alpha volunteers who are contributing to the project with information from their parts of the world. Did you notice missing data on notable people from your area? You can help add data to Wolfram|Alpha by signing up to become a volunteer. Check out this recent blog post profiling the work of a few dedicated Wolfram|Alpha volunteers.
Since Wolfram|Alpha‘s launch in May 2009, one of its most talked-about features has been its ability to compute specific answers to questions about math, chemistry, economics, demographics, and much more. But as its knowledge base continues to grow, it’s also able to highlight interesting and useful connections between data sets, and to reveal information that you might not think to ask for on your own.
One of the coolest examples of this is our recently enhanced relocation calculator. For several months, we’ve been able to answer simple questions about the relative cost of living in various United States cities and metropolitan areas. If you told Wolfram|Alpha that you were relocating from Seattle to Miami with a salary of $35000, you’d get a comparison of the relative cost of groceries, housing, and other expenses in each city, plus an estimate of the salary required to maintain a comparable standard of living in your destination city. On its own, this is a useful little calculator—but it’s also something that dozens of other websites could do.
But because Wolfram|Alpha knows tons of other details about any given city, our relocation calculator can now do things that no other site can. In addition to salary and cost-of-living comparisons, you now get comparisons of each city’s population, median home sale prices, unemployment rates, crime rates, sales taxes, traffic congestion, and climate—a useful sampling of current and historical comparative data for anyone contemplating a move.
We’ll highlight similar enhancements as they are released. And as always, we welcome your suggestions for new data, or new ways of looking at existing data, in any domain covered by Wolfram|Alpha.
[Editor’s Note: This blog entry is a guest post from Laura Ketcham, a 7th grade technology instructor and coordinator at the Aventura City of Excellence School (ACES) in Aventura, Florida. If you are interested in sharing how you’ve incorporated Wolfram|Alpha into your everyday life inside or outside the classroom, please contact our blog team at firstname.lastname@example.org.]
I read the buzz about Wolfram|Alpha in an article in PC World this past summer. It was billed as a “computational” search engine with the advantage that the results of the computed search appear on one results page, not just in a list of links you need to search through to find the information. I quickly realized that Wolfram|Alpha is an innovative tool that I could definitely incorporate in the classroom! I am a 7th grade technology instructor and coordinator at the Aventura City of Excellence School (ACES) in Aventura, Florida. My students often use the web to find information for a variety of classroom activities, as well as for research in other classes. The students follow a process in which they evaluate websites to determine whether they contain reliable information that can be included for assignments; it’s one of the major topics I cover in the year-long technology course. Wolfram|Alpha provided me with a “cool tool” to introduce to the students that they knew could be trusted as reliable source. They can use Wolfram|Alpha in a variety of ways to “calculate” factual information.
What I really found helpful about Wolfram|Alpha was the Examples page. This provided me with a springboard to computing data in Wolfram|Alpha and with a quick way to evaluate its usefulness as a tool in the classroom. This is definitely a great place for teachers, of any grade, to get started!
I introduced Wolfram|Alpha to my students during a six-week project where the students infused Web 2.0 technology to build a website about South Florida oceans and beaches. They used Wolfram|Alpha to learn about a variety of topics that they had to include in their sites. Several examples are the taxonomy of a variety of plants and animals that call South Florida beaches home and the GPS/satellite technology being used to track a loggerhead sea turtle that the class adopted. More »
New curated data flows into Wolfram|Alpha every day. One addition that we haven’t highlighted before is crime data from the U.S. Department of Justice Statistics, including historical information on crimes and crime rates for all 50 states and thousands of individual cities.
A simple query for “U.S. Crime” will return the nation’s overall crime rate (the number of crimes per 100,000 people) and details on individual categories of violent and property crimes.
But Wolfram|Alpha’s true strength shows when you perform more-advanced comparisons and computations. For example, try comparing the crime statistics for two cities, such as “Crime Seattle vs. New York”; you can see clearly that although crime rates have fallen gradually over the last fifteen years, Seattle’s crime rate has maintained a level around 2.5 times that of New York City. More »
When we launched Wolfram|Alpha in May 2009, it already contained trillions of pieces of information—the result of nearly five years of sustained data-gathering, on top of more than two decades of formula and algorithm development in Mathematica. Since then, we’ve successfully released a new build of Wolfram|Alpha’s codebase each week, incorporating not only hundreds of minor behind-the-scenes enhancements and bug fixes, but also a steady stream of major new features and datasets.
We’ve highlighted some of these new additions in this blog, but many more have entered the system with little fanfare. As we near the end of 2009, we wanted to look back at seven months of new Wolfram|Alpha features and functionality.
If you caught Monday night’s Dallas Cowboys vs. Carolina Panthers American football game, then you certainly noticed the new Cowboys Stadium, which is one of the largest domed stadiums and has the largest single-span roof structures in the world. As a tribute to this monumental building, we want to take a moment to point out some of the cool comparisons that Wolfram|Alpha computes automatically whenever you type in a specific measurement or quantity.
The stadium’s roof, for example, measures 660,800 square feet. Type that figure into Wolfram|Alpha, and you’ll discover that it’s just slightly larger than another, possibly more familiar monument:
Each exterior arch of the stadium weighs 3,255 tons, which Wolfram|Alpha instantly computes as measuring a little bit more than the space shuttle’s launch mass, but just one-quarter of the mass of trash produced each day in New York City, or one-ninth the mass of the Titanic:
And those arches are an incredible 292 feet tall—greater than the length of a Boeing 747-400, and just shy of the length of the football field they cover:
For virtually any measurement or conversion query, Wolfram|Alpha will return a variety of dynamically computed comparisons like these. Try out a few of your own (like your age, height, and weight, for example) and let us know if you get any surprising results.
Does this summer seem hotter than last year’s? Are you debating between a trip to Miami or Florence in the springtime? Or perhaps heading to Tokyo in November, and wondering how to pack? Wolfram|Alpha has a number of helpful tools to answer your weather questions, by retrieving current conditions, forecasts, and historical data from weather stations located all over the world.
For example, simply enter “weather” into the computation bar, and Wolfram|Alpha’s geoIP capabilities identify your approximate location and produce the latest records from your nearest weather station. The “Latest recorded weather” pod may feature information like the current temperature, relative humidity, wind speed, and conditions, such as clear, thunderstorms, or fog. Go ahead and click here to give it a try for your area.
Baseball is the great American pastime. We’re at the midpoint of the Major League Baseball season, and fans are gearing up for the 2009 Major League Baseball All-Star Game, which will be played on Tuesday, July 14, 2009 in Saint Louis, Missouri. For baseball fans, this “Midsummer Classic” embodies much of what there is to love about baseball: a night at the park, hot dogs and Cracker Jacks, and top players from American and National League teams all on one diamond. But what we at Wolfram|Alpha love about baseball are all of the fast statistics that can be quickly computed and returned as easy-to-read graphs.
Wolfram|Alpha contains statistics and history for Major League Baseball teams’ wins, losses, pitching and batting histories, and more, from 1960–2008. This information allows you to easily compute statistics for a single season, or graph a visual history over decades. More »
We have been highlighting ways Wolfram|Alpha can be a part of your daily life, and we think you will find it a great addition to your other travel resources. Whether you are traveling for business or pleasure, Wolfram|Alpha can become a part of your planning by providing essential data.
Let’s say you live in San Francisco, California and want to fly to Miami, Florida. Type “San Francisco airports” into Wolfram|Alpha, and your results conveniently include the airport code “SFO” for the San Francisco International Airport. You can use Wolfram|Alpha to instantly access all codes for all U.S. airports, even those as obscure as 11II. Results also list elevation of the airport, number of runways, local time, and other nearby airports in case you want to search for better alternatives for your departure and arrival cities.
There’s new data flowing into Wolfram|Alpha every second. And we’re always working very hard to develop the core code and data for the system. In fact, internally, we have a complete new version of the system that’s built every day. But before we release this version for general use, we do extensive validation and testing.
In addition to real-time data updates, we’ve made a few changes to Wolfram|Alpha since its launch three weeks ago. But today, as one step in our ongoing, long-term development process, we’ve just made live the first broad updates to the core code and data of Wolfram|Alpha. More »
Soon everyone will have access to the first version of Wolfram|Alpha. Already some have asked: “What kinds of questions can Wolfram|Alpha help me answer?” “Will there be examples for me to use?” “How will I get started?”
As we make our final preparations to release Wolfram|Alpha over the next week, we thought it might be helpful to discuss questions like these in this blog.
Looking at the Examples by Topic page provides a good framework. You will be able to navigate from the Wolfram|Alpha home page to Examples: