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Summer has drawn to a close, and so too have our annual internships. Each year Wolfram welcomes a new group of interns to work on an exciting array of projects ranging all the way from Bell polynomials to food science. It was a season for learning, growth, and making strides across disciplinary and academic divides. The Wolfram interns are an invaluable part of our team, and they couldn’t wait to tell us all about their time here. Here are just a few examples of the work that was done. More »
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It’s been a while since we looked at American Community Survey data in Wolfram|Alpha. Our first efforts included surveying ACS data related to education, income, and diversity, only touching the tip of the iceberg.
Recently, we took a deeper look at the data to unearth some of the least “average” communities in the US.
As you might guess, at the national level, female and male populations are split almost evenly (50.8% and 49.2%, respectively). But there are metropolitan communities in the US where the split doesn’t hew to the national average, and the ACS data in Wolfram|Alpha lets us find them.
Take Susanville, CA, for example. At just 34.5%, this community in Northern California has the smallest percentage of female residents in the US. More »
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Are you looking to make a move in the near future? Budgeting for your next vacation? Before you go anywhere, check out Wolfram|Alpha’s data on costs of living and consumer goods. Whether you’re simply looking to get the most bang for your buck, or figuring out how your salary needs to change to maintain your lifestyle in a new city, look no further for some quick answers. More »
Whether you’re trying to find the perfect word in Scrabble or study the languages of the world, Wolfram|Alpha has always provided computational insights into how we communicate. Now we’re taking that a step further—with data from the American Community Survey, we can take a closer look at where different languages are spoken in the United States. More »
Happy Hispanic Heritage month! To celebrate, Wolfram|Alpha would like to spread some Hispanic computational knowledge! We’ve got some pretty nifty geographical gems to show you. More »
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For many of us, the end of summer is a time of change. You might be going to college, starting a new year of school, or taking a new job. Even if you’re not, there’s a decent chance that you’re still meeting some new friends and living a little bit differently in general. We’ve previously looked at what Wolfram|Alpha Personal Analytics for Facebook can tell us about the evolution of our society, but we can also use Personal Analytics to inform us about how we change over time as individuals. More »
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Today we’re excited to announce the first upgrade to Wolfram|Alpha’s Personal Analytics for Facebook. There’s much more to analyze, see, and do—here’s a quick look! More »
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First, my apologies: I didn’t quite follow through on my promise of a regular series of blog posts about American Community Survey data in Wolfram|Alpha. But when you’re trying to ingest all the world’s systematic knowledge… well, there’s a lot of competition for the top spot on your to-do list. So to make up for lost time, I’ll cover the remaining clusters of ACS data that you can currently explore in Wolfram|Alpha: education and income. More »
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In our first post on American Community Survey estimates in Wolfram|Alpha, we showed you how Wolfram|Alpha could answer questions about the age and sex of the population in practically any town or region in the United States. But that’s only a small fraction of what we can do with this wealth of detailed demographic data. Over the next few weeks, we’ll also share some examples of how Wolfram|Alpha can help you find and analyze information about education, income, and more.
But first, let’s take a look at two of the most frequently asked for demographic topics in Wolfram|Alpha: race and Hispanic origin. If you’ve never done so before, it’s worth taking a moment to brush up on the difference between these two concepts, in Census terminology. Although people often lump the two concepts together, race and Hispanic origin are two completely separate attributes in Census data: a person can be of any race and also be of Hispanic or non-Hispanic origin. Even with the basic data we’ve had in Wolfram|Alpha since its launch, people have regularly complained that our numbers “don’t add up”—and it’s always because they’ve added Hispanic population estimates to figures for the population by race and ended up with a figure larger than the country’s total population.
More »
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When Wolfram|Alpha launched three years ago, it did so with broad (but not very deep) socioeconomic data for most geographic places on Earth. Since then, each enhancement of this part of our knowledge base has tended to address just one type of place at a time. Sometimes we’ve added an entirely new category (like US congressional districts or school districts); other times, we’ve added a narrowly focused set of properties to an existing category (such as age pyramids for countries or home prices for US metro areas).
I’ve been proud of each of these individual features, but also frustrated by how hard it’s been to get detailed and directly comparable data for many different types of places at once—the kind of data, in other words, that Wolfram|Alpha is perfectly suited to work with.
But thanks to the outstanding work of our friends at the US Census Bureau, we’ve been able to take some big steps toward filling this “data gap.” The annual American Community Survey (ACS) is designed to replace the old long-form decennial census questionnaire, covering information about age, sex, race, ethnicity, education, income, and much more. In 2006, the Census Bureau released the first single-year ACS estimates, but only for areas with populations over 65,000; in 2008, three-year estimates came out for areas with populations of 20,000 or more; and in 2010, the first five-year estimates were released, covering every geographic area in the country.
More »
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We’re constantly expanding Wolfram|Alpha’s knowledge base in small ways. Sometimes we know from the start that a new feature is going to be “blog-worthy,” like pro football stats or live aircraft-tracking data. Lots of other additions are useful, but don’t seem worth crowing about too loudly. We recently added some data on each of the 94 district courts of the US federal court system, and I confess that it seemed like a project in the latter category—but it turned out to reveal some genuinely fascinating bits of information about the justice system in this country.
Most people probably don’t have a natural sense of the jurisdiction of each court—or even how many there are in their state—but an input like “California courts” will give you a summary of key stats about all the district courts in a given state, including a list of the largest cities in each of them. From there, you can click a specific court to see a map of that court’s jurisdiction and detailed information about overall caseloads and judgeships, as well as annual filings for a variety of civil and criminal case types.
The United Nations (UN) was officially founded 66 years ago this week, bringing together “peace-loving states” (as the Charter of the UN described them) to cooperate on issues of international law, economic and social development, human rights, and other matters of critical importance to global human development. From the time it launched, Wolfram|Alpha has relied on a wide variety of datasets provided by various UN organizations—and as recent blog posts indicate, these agencies remain an important source of information for international data. More »
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We’ve blogged before about Wolfram|Alpha’s powerful relocation calculator, which has turned out to be one of our more popular—and practical—features. Our last round of enhancements added information about broad topics like population, home sale prices, unemployment rates, and more; now we’ve added more detail to the core cost-of-living categories, so you can see how prices of specific retail goods and services differ among US cities and metropolitan areas. More »
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Earlier this year, we added data on all 18,000+ US public school districts to Wolfram|Alpha, allowing you to analyze student and teacher populations and detailed revenue and expenditure data for individual districts or across all the districts in a given city or county. We had a terrific response to this—lots of people wrote in to tell us they learned some useful information about their own districts and others across the country. And virtually all of those people ended their messages with “But when will you have information about individual schools?”
We’re happy to announce that Wolfram|Alpha now has data on nearly 108,000 public schools in the United States, which is based on 2009-10 school year data from the National Center for Education Statistics (NCES). So if you go back and try the first input from our earlier post on school districts, you’ll notice that the result looks quite different—the result for “Seattle public schools” now defaults to a summary of data on individual schools rather than the district itself (which is still available by clicking “Use the input as a US school district instead”).
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We’ve highlighted data from the World Bank’s World Development Indicators (WDI) database in previous blog posts about employment and business statistics. As many of our users head back to school, it seems like the right time to show off some additional World Bank statistics about education. Wolfram|Alpha can now answer a broad range of simple questions about student and teacher populations in various countries, such as:
- How many high school students are there in Djibouti? »
- How many grade school teachers are there in Africa? »
- High school student/teacher ratio in Japan vs. South Korea »
You can also ask questions about student performance and progression in a given country or between multiple countries:
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A few weeks ago, we pointed out some new additions to Wolfram|Alpha from the World Bank’s World Development Indicators database, primarily focused on labor and employment around the world. We’ve also incorporated data about doing business in most countries, collected by a World Bank project called, appropriately, “Doing Business”.
This data covers a wide range of issues, including import/export costs, business tax rates, and the time required to complete various business-related activities. Try the following examples to get a better sense of the breadth of this dataset:
- Export costs in Brazil vs. Argentina »
- New businesses registered in Japan vs. India »
- Credit bureau coverage in Asia »
- Europe total business tax rate »
- How much does it cost to start a business in India, Mexico, Singapore »
- Time required to start a business »
Wolfram|Alpha can also rank countries according to a number of business-related indices. Ask about the ease of doing business in Eurozone countries, for example, and you’ll see that Ireland and Greece occupy the ends of this spectrum. In this case, a higher index score indicates a regulatory environment that is generally unfriendly to business operation; in light of Greece’s recent economic woes, it should come as no surprise to see that country at the bottom of the list. (Note that for all the properties in this dataset, you can click the “Definition” button in the input interpretation or in other pods to get more details on each property.)
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In 2010, our friends at the World Bank opened up their highly regarded World Development Indicators (WDI) database, making hundreds of economics, education, health, and other indicators free to download and explore. As part of our own mission to make data more accessible and comprehensible, we’re pleased to announce that we’ve been steadily adding WDI and other World Bank data to Wolfram|Alpha, so you can answer thousands of new questions about key components of global development.
One of the first sets we tackled was data on labor and employment, which means Wolfram|Alpha can now generate some quite detailed computations and comparisons of employment-related data for most of the world’s countries and territories. Try an input like “fraction of people working in agriculture in US, Russia, and Japan” to see find out how much less agrarian these economies have become over time. More »
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By popular demand, Wolfram|Alpha recently expanded population data for most of the world’s countries, based in part on statistics from the United Nations Population Division. Populations are shaped by factors such as disease, war, genocide, and famine as well as more benign phenomena such as immigration. One of the more common user requests in this area has been to support queries like “China population distribution”, which now returns an age pyramid and detailed table of population by age and sex:
You can also query for specific age groups, as indicated on the pyramid, or just query for a single age, and Wolfram|Alpha will return data for the appropriate five-year age “bin”:
More »
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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.
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Wolfram|Alpha recently added information about the minimum wage in U.S. states (from 1967 through today) based on data from the U.S. Department of Labor. This means you can ask Wolfram|Alpha about simple historical facts, like the U.S. minimum wage in 1980, or perform simple analyses, like comparing the current minimum wage in Ohio and Alaska.
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As temperatures start falling across the U.S., many of us are looking more closely at our home heating and energy bills, wondering how much they might go up this winter. Wolfram|Alpha can’t yet predict the future, but now it can help you explore historical and recent energy-price trends in most U.S. states, thanks to data from the U.S. Energy Information Administration (EIA).
Ask Wolfram|Alpha about “heating oil prices in New York”, for example, and you’ll see that as of November 1, the statewide average price was about $3 per gallon—slightly higher than at the start of last winter, but quite a bit below the peak in late winter of 2008. Propane prices are also higher than a year ago, and you can also see that prices climbed dramatically over the course of last winter. You can keep checking back over the course of the season to see which way prices are trending in your state.
(Note that the jagged appearance of heating oil and propane plots is due to the fact that prices are only reported for part of the year; these prices are also reported for only about 20, mostly northern, U.S. states.)
You can also ask Wolfram|Alpha about natural gas and electricity prices. The EIA keeps these figures less up to date than figures for heating oil and propane, but you can clearly see long-term price trends and seasonal fluctuations for both of them. More »
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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.
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If you’re keeping a close eye on the U.S. economy—and who isn’t, these days?—you probably noticed yesterday’s news that retail sales increased in August for the second month in a row. But you may not have noticed that Wolfram|Alpha is now picking up these Department of Commerce reports as soon as they are released, and allows you to explore and compute U.S. retail sales data so you can better understand these trends.
Try simply asking Wolfram|Alpha about “U.S. retail sales” and you’ll see the latest monthly figure, along with automatic computations of that number as a per capita value and as a fraction of total U.S. GDP, as well as the annual growth rate for overall retail sales. To filter out the seasonal variation in many sales categories, you can also ask for “seasonally adjusted retail sales“—which more clearly shows the retail sector’s dramatic plunge in late 2008.
You can also explore trends in individual retail categories (click “More” in the “Retail sales categories” pod for a detailed list), such as clothing stores or electronic shopping and mail order houses.
Or you can mash up this retail sales data with other economic data in Wolfram|Alpha. Try comparing retail sales at building-supply dealers with housing starts, for example, or retail sales at jewelry stores compared with civilian unemployment. (Note that advance figures for August aren’t available for all individual retail categories, so Wolfram|Alpha will default to the latest available values.) More »
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Wolfram|Alpha launched with an extensive database of United States economic data, derived from the Federal Reserve Bank’s FRED database. Over the past year, we’ve continued to improve our handling of this data in a variety of ways—teaching Wolfram|Alpha to return more related statistics along with any specific result, improving our linguistic abilities so we can answer more complex questions, and increasing the frequency with which we update this data. Wolfram|Alpha is now refreshing its collection of FRED-derived data on a daily basis, so you can always access the latest available data on the national economy.
We’ve also begun to expand our coverage of economic data for smaller geographic areas in the United States, starting with state-level statistics. This means Wolfram|Alpha users can now query for the latest available information on a variety of economic topics, including gross state product, unemployment, health insurance coverage, and housing-related data.
As always, one of the strengths of Wolfram|Alpha is that it allows you to compare and analyze different pieces of data—and with this data set, you can quickly uncover strong correlations between various economic properties. It’s easy to see that the house price index tends to move together with employment and state tax collections. You can also use Wolfram|Alpha to run simple calculations of productivity in U.S. states, or to find out a given state’s share of the national economy or workforce.
To make it even easier to explore this data, you can also use the new Wolfram|Alpha Widget Builder to create simple tools for analyzing and comparing the economic properties of states. To get you started, here’s a small selection of widgets focused on US state economies—ranging from the serious to the slightly silly. Try them out:
Feel free to customize and share these on Twitter or Facebook, in your blog, or anywhere else—and let us know in the comments if you create any useful new widgets of your own.
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This Sunday, July 11, is World Population Day—an event established in 1989 by the United Nations to raise awareness of global population issues. This year, the emphasis is on the 2010 World Population and Housing Census Programme and the importance of collecting, analyzing, and disseminating data in a way that supports good health and social policy development.
In the past few months, we’ve added a variety of international data sets to Wolfram|Alpha, such as data on food consumption and worldwide health indicators. But Wolfram|Alpha launched with an enormous collection of global socioeconomic data, much of it from the UN and other authoritative repositories of international statistics, and we’ve continued to expand and curate that collection.
As we’ve said before, we’re committed to “democratizing data”—to making it easier for everyone to access and understand the wealth of important data produced by a multitude of sources. For good examples of our own ability to analyze and disseminate relevant socioeconomic data, try some of the following queries pertaining to topics from past and present World Population Days:
- All countries’ population in 1900 »
- All countries’ population in 2050 »
- Poverty in all countries »
- Female literacy in all countries »
- Life expectancy in all countries »
We’ll soon be introducing some new functionality that will give “power users” the ability to do more advanced analysis and comparison of properties between groups of countries, and in other knowledge domains. And as always, if you’d like to see additional data in Wolfram|Alpha, please send us your suggestions.
PS: If you’re interested in the absolute latest information on world population, try asking Wolfram|Alpha for the current world population. Reload that page in your browser a few times and see how fast that number is going up!
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In any news report about the Deepwater Horizon oil spill, a lot of statistics get thrown around—mainly about the rate at which oil has been spewing out of a pipe on the floor of the Gulf of Mexico. Recent estimates put the flow up to 60,000 barrels per day, but it’s hard for most of us to comprehend exactly what that number means. Wolfram|Alpha has always been able to provide some useful comparisons for any quantity you care to input, and can easily tell you that 60,000 barrels of oil is roughly equivalent to 3.8 times the volume of an Olympic-sized swimming pool.
With the recent addition of data on production and consumption of energy resources in every country, Wolfram|Alpha can also give you a more precise socioeconomic context for numbers like this. Try “60000 barrels per day / US crude oil production”, for example, and you’ll learn that the daily output of the leak is a little less than 1.2% of total crude oil production per day for the United States.
You can also get a better sense of global production or consumption of petroleum products, as well as information on coal and natural gas.
Because each of these energy resources is measured in different units, it can be difficult to understand exactly how they compare to one another—so Wolfram|Alpha can also compute the energy equivalents of each resource, measured in quadrillions of BTUs. For example, you can compare the United States consumption of energy from coal, natural gas, and petroleum on a single scale to better visualize the relative importance of each resource. More »
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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.
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 »
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Wolfram|Alpha couldn’t do your taxes for you this year, but we did just wrap up a quick project to add some interesting historical tax statistics. Now that all of our U.S. users have filed their taxes (we hope), they can explore IRS data about individual income taxes, broken down by adjusted gross income (AGI), from 1996 to 2007—the latest year for which the IRS has released statistics broken down by AGI. Users can also investigate less-detailed data about sources of individual taxable income from 1916 to 2007.
The basic input for this new dataset is simply an income, such as “AGI $35000”—type it in, and Wolfram|Alpha matches that input to a specific AGI bracket (in this case, $30,000–$40,000) and calculates a broad range of statistics.
First, the average effective federal tax rate, which is calculated by dividing total tax receipts in this bracket by total adjusted gross income:
Next, the average tax paid in the input’s bracket—which in this case dropped by nearly 50% over the decade covered by this dataset. You’ll also note that nearly a quarter of all tax returns in this AGI bracket had no tax due:
Third, average exemptions and deductions for all taxpayers in the input’s bracket. In this case, those increased by nearly $4,000 over this period, and in 2007 accounted for an average of 48% of AGI:
For some particularly interesting numbers, try asking about high income brackets (average tax on AGI $400k, average exemptions and deductions on $5 million) and very low brackets (average tax on AGI $500). More »
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“One of the very nicest things about life is the way we must regularly stop whatever it is we are doing and devote our attention to eating,” said Luciano Pavarotti. Let’s stop whatever we’re doing now to devote our attention to data on eating, as a kind of food for thought.
Wolfram|Alpha now has food supply estimates from the Food and Agriculture Organization of the United Nations, covering more than fifty foods spanning over forty years for countries all over the world. Let’s visit three countries to see what we can find.
First stop, the Caribbean. Type in “cuba wheat” and you’ll see a dramatic downturn in the early 1990s, following the demise of the Soviet Union (Cuba’s most important trading partner).
Now let’s go over to the Korean peninsula. Let’s check out South Korea’s coffee versus tea consumption.You’ll see that coffee intake has increased by several factors since 1970, as the country has become increasingly westernized, while tea consumption has gone up just a little:
Final stop, North America. In contrast to South Korea, we can see a slow decline in per capita coffee consumption in the United States; according to the United States Department of Agriculture (USDA), increased availability of carbonated soft drinks may be one cause of the downturn. More »
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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.
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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 »
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We have been highlighting ways that Wolfram|Alpha can be a useful tool in your everyday life, and we believe you will find our salary and wage data helpful in navigating your decisions in today’s job market. A lot of people are searching for full-time employment, relocating, exploring going back to school to change professions, or considering taking on multiple jobs. Many factors play into these decisions, and Wolfram|Alpha’s U.S. occupational salary data, and salary computations for local currencies, help you make informed choices.
Perhaps you are considering changing professions. In addition to supplying data on specific occupations, Wolfram|Alpha can compare U.S. occupational information for multiple jobs, including the median salary, the number of people employed at those jobs, and more. For example, here is the comparative information for a registered nurse, an elementary school teacher, and an accountant: More »