April 25, 2013
election:

Apply to be a Tumblr Fellow at the Personal Democracy Forum!
How is technology changing society, politics and civic life? Thats the question tackled every summer at the Personal Democracy Forum (PDF) conference at NYU. If it’s also a question that gets you excited, then you’ll be happy to know that Tumblr and PDF are teaming up to offer conference fellowships that cover the full registration costs and a meal with the team from Tumblr at PDF13. Ten applicants will be selected to attend this year’s conference on June 6-7.
We’re looking for people and organizations who are using technology and social media in innovative, meaningful ways to affect positive change. If that’s you, apply by taking a few minutes to fill out this brief form.
The deadline for applications is 6pm EST on Friday, April 26th, 2013. Winners will be notified the following week.
GIF created with permission from bindersfullofburgers.tumblr.com

Sometimes chart junk has a place.

election:

Apply to be a Tumblr Fellow at the Personal Democracy Forum!

How is technology changing society, politics and civic life? Thats the question tackled every summer at the Personal Democracy Forum (PDF) conference at NYU. If it’s also a question that gets you excited, then you’ll be happy to know that Tumblr and PDF are teaming up to offer conference fellowships that cover the full registration costs and a meal with the team from Tumblr at PDF13. Ten applicants will be selected to attend this year’s conference on June 6-7.

We’re looking for people and organizations who are using technology and social media in innovative, meaningful ways to affect positive change. If that’s you, apply by taking a few minutes to fill out this brief form.

The deadline for applications is 6pm EST on Friday, April 26th, 2013. Winners will be notified the following week.

GIF created with permission from bindersfullofburgers.tumblr.com

Sometimes chart junk has a place.

April 3, 2013
Tabula

Tables in a PDF? No problem. Take a look at the demo or get working with your own install.

I’ve secretly had an account since just before NICAR and I’m smitten. Well done Manuel Aristarán, Mike Tigas, Jerey B. Merrill, La Nación and ProPublica.

2:07pm  |   URL: http://tmblr.co/ZjZlBwhpYw3q
Filed under: pdfs tables data tabula mozilla 
March 6, 2013
He Said/She Said, Now With Numbers

image

By Elliott Ramos

Chicago Transit Authority president Forrest Claypool had some biting words for the Chicago Sun-Times — on its own pages.

On Thursday, the CTA chief penned a letter to the editor, chastising the newspaper’s article on CTA crime that ran on Tuesday.
On Monday…

Crime data is complicated. Fare evasion is a crime but does a rise therein mean I need to clutch my purse tighter?

(via lifeandcode)

March 5, 2013
How To Not Screw Up Your Data

lifeandcode:

veltman:

At NICAR 2013, there were lots of great sessions how to work with data effectively, talking about things like where to find good data and how to do sound statistical analysis for journalism. There’s also an important and time-consuming middle piece: you just got some very raw data, and before…

I LOVE THIS POST READ IT NOW PLZ

9:06pm  |   URL: http://tmblr.co/ZjZlBwfcncWc
  
Filed under: Data Nicar Nicar13 
February 23, 2013
journo-geekery:

fastcodesign:

Infographic: An Amazing, Invisible Truth About Wikipedia
Every Wikipedia entry has an optional feature we take for granted—geotagging. An entry on the Lincoln Memorial will be linked to its specific latitude and longitude in Washington D.C. On any individual post, this may or may not be a useful thing. But what about looking at these locations en masse?
That was a question asked by data viz specialist and programmer Olivier Beauchesne. To find out, he downloaded all of Wikipedia (it’s open-source, after all) then used an algorithm that would assemble 300 topical clusters from popular, related keywords. Then he placed the location of each article in these topical clusters on a map. What he found was astounding.
“Eventually, Beauchesne’s maps evolve to something more than the locations of everything in the world. They become the locations of, quite simply, everything we know.”

Lovely work.

journo-geekery:

fastcodesign:

Infographic: An Amazing, Invisible Truth About Wikipedia

Every Wikipedia entry has an optional feature we take for granted—geotagging. An entry on the Lincoln Memorial will be linked to its specific latitude and longitude in Washington D.C. On any individual post, this may or may not be a useful thing. But what about looking at these locations en masse?

That was a question asked by data viz specialist and programmer Olivier Beauchesne. To find out, he downloaded all of Wikipedia (it’s open-source, after all) then used an algorithm that would assemble 300 topical clusters from popular, related keywords. Then he placed the location of each article in these topical clusters on a map. What he found was astounding.

“Eventually, Beauchesne’s maps evolve to something more than the locations of everything in the world. They become the locations of, quite simply, everything we know.”

Lovely work.

(via lifeandcode)

February 12, 2013
new-aesthetic:

Cell Phone Disco is a surface that visualizes the electromagnetic field of an active mobile phone. Several thousand lights illuminate when you make or receive a phone call in the vicinity of the installation. Cell Phone Disco makes an invisible property of the environment perceptible to our senses. It reveals the communicating body of the mobile phone.
Cell Phone Disco » ABOUT, via Dan W.

new-aesthetic:

Cell Phone Disco is a surface that visualizes the electromagnetic field of an active mobile phone. Several thousand lights illuminate when you make or receive a phone call in the vicinity of the installation. Cell Phone Disco makes an invisible property of the environment perceptible to our senses. It reveals the communicating body of the mobile phone.

Cell Phone Disco » ABOUT, via Dan W.

(via kthread)

December 12, 2012

Looking for a winter break project? Here are two:

One: Duplicate this Krugman story and add Connecticut to the mix. Use the FRED Data. Now try making the chart in HighCharts.

Two: Use census data about county-level populations and incomes to help people figure out whether or not they’re rich. Play around with defining your terms: are you rich if you earn more than $250,000? What if you earn twice the local median household income?

 image

Not sure where to start? Try some pseudocode. What’s the math? What would you need to look up? From there you can try to write your first django app (or, try it in Rails, but I’m no good with Rails). If that’s all already over your head, take a step back. Note that Zed Shaw recommends that you type everything yourself instead of cutting and pasting. Heed that recommendation. Your goal here is to learn a new skill, not to get to the end of the tutorial. 

October 17, 2012
Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) - FRED - St. Louis Fed

September 20, 2012
Federal Criminal Case Processing Statistics

Comprehensive information on suspects and defendants processed in the federal criminal justice system, compiled by the Bureau of Justice Statistics.

August 21, 2012
Mr. Data Converter

Mr. Data Converter will convert your Excel data into one of several web-friendly formats, including HTML, JSON and XML.

9:55am  |   URL: http://tmblr.co/ZjZlBwRr-IFQ
Filed under: data json tools 
July 27, 2012
Power Reporting: sample data files

A nice round up of data sets to play with. All a little old to be news.

4:40pm  |   URL: http://tmblr.co/ZjZlBwQDhyPx
Filed under: data catalog 
July 1, 2012
On The Media: The Data Show

On The Media: The Data Show

June 26, 2012
"

In 1988, for example, there were 234 homicides in Baltimore. Tracking all of those cases, a reporter would learn that 22 of them were cleared by the police department through arrest, but then later dropped by the state’s attorney’s office prior to indictment. What did that mean to the police department? To the prosecutor’s office? To the city of Baltimore?

Well, for the police department it meant credit for solving 22 cases that they didn’t actually solve, given that the evidence was insufficient for prosecutors to obtain even a grand jury indictment. Why? Because the FBI’s crime reporting logic allows police departments to take credit for all cases cleared by arrest, regardless of whether those arrests are any good at all. Once an arrest has been made — even if charges are subsequently dropped — the case is credited as cleared, and its status as a cleared case doesn’t change.

For the Baltimore state’s attorney’s office, there was no institutional cost to be paid for these 22 weak sisters. Why not? Because the State’s Attorney only computes his overall conviction rate — a statistic that he will run on for reelection every four years — using cases that have been indicted by a grand jury. Cases dismissed prior to indictment don’t count against him either.

"

— David Simon, Dirt Under the Rug; Know what your numbers tell you and what they don’t tell you.

9:49pm  |   URL: http://tmblr.co/ZjZlBwOBwTqT
Filed under: data ethics stories context 
June 18, 2012
From Planet Money, “What America Spends on Groceries” — a series of great (flat) visualizations of changes in food spending in the US.

From Planet Money, “What America Spends on Groceries” — a series of great (flat) visualizations of changes in food spending in the US.

June 7, 2012
Open Book New York - Office of the State Comptroller

The NY State Comptroller makes a whole lot of raw data public, including contrating information.