Posts tagged data
Map Shows All The Devices In The World Connected To The Internet | IFLScience

The image above isn’t your average map: it shows the location of all devices connected to the Internet in the world. The redder the area, the more devices there are.

The map was created by John Matherly, founder of the search engine Shodan and self-proclaimed Internet cartographer. To produce it, Matherly sent ping requests on August 2 to every IP address on the Internet and plotted the positive responses. There’s nothing shady or illegal about this; pings are simply network utilities which transmit an echo-request message to an IP address.

It took him just five hours to collect the data, but a further 12 to generate the image. Matherly notes on reddit that his ping requests would only reach devices that are directly connected to the Internet, such as routers. However, he has sometimes picked up smart phones.

Map Shows All The Devices In The World Connected To The Internet | IFLScience

The image above isn’t your average map: it shows the location of all devices connected to the Internet in the world. The redder the area, the more devices there are.

The map was created by John Matherly, founder of the search engine Shodan and self-proclaimed Internet cartographer. To produce it, Matherly sent ping requests on August 2 to every IP address on the Internet and plotted the positive responses. There’s nothing shady or illegal about this; pings are simply network utilities which transmit an echo-request message to an IP address.

It took him just five hours to collect the data, but a further 12 to generate the image. Matherly notes on reddit that his ping requests would only reach devices that are directly connected to the Internet, such as routers. However, he has sometimes picked up smart phones.

Let’s say you’re a family making $50,000, married with one child. Let’s also say you put 2 percent of your wages toward a 401(k), don’t itemize, and claim the Saver’s Credit and Child Tax Credit. This is what your tax receipt might look like. You’re paying $440 to have the finest military on the planet. You’re paying $9.59 on unemployment insurance. You’re paying $15.98 to ensure that the federal government can help you out if there’s a natural disaster that takes out your town. You’re also paying about $4,000 in Social Security and Medicare taxes. 

The Details, plus more charts: How America Pays Taxes—in 10 Not-Entirely-Depressing Charts)

Let’s say you’re a family making $50,000, married with one child. Let’s also say you put 2 percent of your wages toward a 401(k), don’t itemize, and claim the Saver’s Credit and Child Tax Credit. This is what your tax receipt might look like. You’re paying $440 to have the finest military on the planet. You’re paying $9.59 on unemployment insurance. You’re paying $15.98 to ensure that the federal government can help you out if there’s a natural disaster that takes out your town. You’re also paying about $4,000 in Social Security and Medicare taxes.

The Details, plus more charts: How America Pays Taxes—in 10 Not-Entirely-Depressing Charts)

This year’s Ivy League admissions totals are in. The 8.9 percent acceptance rate is impressively exclusive, but compared to landing a job at Wal-Mart, getting into the Ivy Leagues is a cakewalk.

Last year when Wal-Mart came to D.C. there were over 23,000 applications for 600 jobs. That’s an acceptance rate of 2.6%, twice as selective as Harvard’s and over five times as choosy as Cornell. 

Wal-Mart has a lower acceptance rate than Harvard

This year’s Ivy League admissions totals are in. The 8.9 percent acceptance rate is impressively exclusive, but compared to landing a job at Wal-Mart, getting into the Ivy Leagues is a cakewalk.

Last year when Wal-Mart came to D.C. there were over 23,000 applications for 600 jobs. That’s an acceptance rate of 2.6%, twice as selective as Harvard’s and over five times as choosy as Cornell.

Wal-Mart has a lower acceptance rate than Harvard

As the Erie Railway grew, so did the amount of data it had to wrangle: which superintendents were responsible for which set of tracks; schedule changes; who the various conductors, laborers and brakemen worked under. 

As Caitlin Rosenthal writes over at McKinsey Quarterly, if any one data point was mismanaged it could bring dire results: “One delayed train, for example, could disrupt the progress of many others. And the stakes were high: with engines pulling cars in both directions along a single set of rails, schedule changes risked the deadly crashes that plagued 19th-century railroads.” 

The First Org Chart Ever Made Is a Masterpiece of Data Design | Wired.com

As the Erie Railway grew, so did the amount of data it had to wrangle: which superintendents were responsible for which set of tracks; schedule changes; who the various conductors, laborers and brakemen worked under.

As Caitlin Rosenthal writes over at McKinsey Quarterly, if any one data point was mismanaged it could bring dire results: “One delayed train, for example, could disrupt the progress of many others. And the stakes were high: with engines pulling cars in both directions along a single set of rails, schedule changes risked the deadly crashes that plagued 19th-century railroads.”

The First Org Chart Ever Made Is a Masterpiece of Data Design | Wired.com

For years, social scientists have tried to explain why living together before marriage seemed to increase the likelihood of a couple divorcing. Now, new research released by the nonpartisan Council on Contemporary Families gives an answer: It doesn’t. And it probably never has. 

This is despite two decades of warnings from academics and social commentators who pointed to studies that claimed a correlation between “shacking up” and splitting up—warnings that increased as the number of couples living together before marriage skyrocketed. 

As it turns out, those studies that linked premarital cohabitation and divorce were measuring the wrong variable, says Arielle Kuperburg, a professor at the University of North Carolina, Greensboro, who produced much of the research released Monday. 

The biggest predictor of divorce, she says, is actually the age at which a couple begins living together, whether before the wedding vows or after. 

Best predictor of divorce? Age when couples cohabit, study says

For years, social scientists have tried to explain why living together before marriage seemed to increase the likelihood of a couple divorcing. Now, new research released by the nonpartisan Council on Contemporary Families gives an answer: It doesn’t. And it probably never has.

This is despite two decades of warnings from academics and social commentators who pointed to studies that claimed a correlation between “shacking up” and splitting up—warnings that increased as the number of couples living together before marriage skyrocketed.

As it turns out, those studies that linked premarital cohabitation and divorce were measuring the wrong variable, says Arielle Kuperburg, a professor at the University of North Carolina, Greensboro, who produced much of the research released Monday.

The biggest predictor of divorce, she says, is actually the age at which a couple begins living together, whether before the wedding vows or after.

Best predictor of divorce? Age when couples cohabit, study says

The biggest retail hack in U.S. history wasn’t particularly inventive, nor did it appear destined for success. In the days prior to Thanksgiving 2013, someone installed malware in Target’s (TGT) security and payments system designed to steal every credit card used at the company’s 1,797 U.S. stores. 

At the critical moment—when the Christmas gifts had been scanned and bagged and the cashier asked for a swipe—the malware would step in, capture the shopper’s credit card number, and store it on a Target server commandeered by the hackers. 

Target Missed Warnings in Epic Hack of Credit Card Data

The biggest retail hack in U.S. history wasn’t particularly inventive, nor did it appear destined for success. In the days prior to Thanksgiving 2013, someone installed malware in Target’s (TGT) security and payments system designed to steal every credit card used at the company’s 1,797 U.S. stores.

At the critical moment—when the Christmas gifts had been scanned and bagged and the cashier asked for a swipe—the malware would step in, capture the shopper’s credit card number, and store it on a Target server commandeered by the hackers.

Target Missed Warnings in Epic Hack of Credit Card Data