Most of the familiar statistical packages social scientists work with are not well-equipped for analysis of text. Python is one tool often used with text data.
Here is a series of Python tutorials posted on Neal Caren’s Github site. Notice the wide-prevalence of code sharing. That is a feature of much of the folks who work in this field.
You can follow his tutorials on Python or take a Coursera course by a UM professor in February. Another option is the Coursera Data Science specialization offered via Johns Hopkins. This set of courses skips Python but includes a snapshot of the variety of concentrations in this field.
Learning Python for Social Scientists [list curated by Neal Caren]
Programming for Everybody (Python) [University of Michigan via Coursera]
Data Science Specialization [Johns Hopkins via Coursera]
Here’s a rendering of that specialization from a student in the Data Toolbox course:
Source: Uri Grodzinski
Who knew that the name Violet was such a good example of a bi-modal distribution?
This was drawn from a very fun post:
How to Tell Someone’s Age When All You Know is Her Name
Nate Silver and Allison McCann | FiveThirtyEight blog
May 29, 2014
We had a previous post on fun with the Social Security names database.
This age of names example is a great applied demography exercise – calculating the median age of names. For that you’ll need a link to the full names database and cohort life tables:
Beyond the Top 1000 Names
Cohort Life Tables for the Social Security Areas by Calendar Year
Here’s also a nice link to some Big Data exercises via Python. There is a lot of code sharing in this GitHub repository.
The Pew Research Center analyzed data from the 2013 American Community Survey and discovered the cliche is true: a man is more likely to marry much a younger woman the second time around.
Read the full article
By: Emily Badger
A couple of years ago, the city of Chicago started a summer jobs program for teenagers attending high schools in some of the city’s high-crime, low-income neighborhoods. The program was meant, of course, to connect students to work. But officials also hoped that it might curb the kinds of problems — like higher crime — that arise when there’s no work to be found.
Read the rest of the article
Research on the program was conducted by the University of Chicago Crime Lab and published in the journal Science.
By: Emily Badger
Via: Washington Post Wonkblog
Despite their ubiquity in the media, gentrifying neighborhoods that evolve over time from low-income to well-off are quite rare. It is far, far more common that once-poor neighborhoods stay that way over time — or, worse, that they grow poorer.
The article is based on the report “Lost in Place” (PDF) by Joe Cortright and Dillon Mahmoudi for City Observatory
See also Marketplace’s series, York & Fig, on neighborhood gentrification.
Alcohol is an entrenched reality of campus life. Read and share this collection of articles on college drinking to inform colleagues and campus discussions, beginning with “A River of Booze: Inside one college town’s uneasy embrace of drinking” by Karin Fischer and Eric Hoover.
By Dan Keating
Over the past 20 years, whites and blacks have experienced opposite trends in segregation. Asians, Hispanics and blacks are moving into historically white neighborhoods. Vastly fewer whites live surrounded by just other white people. Whites look around and see multi-ethnic neighbors. They perceive expanded opportunity and integration because that is what they see. And they think everyone else is experiencing the same things.
But a Washington Post analysis of Census data shows that the experience in historically African American neighborhoods of major cities has been far different, as they have remained heavily isolated. Whites, Asians and Hispanics are not moving into those neighborhoods, and blacks who remain there experience persistent segregation.
Read the full story
Both the Pew Research Center and the FiveThirtyEight blog have done write up about the trouble the U.S. Census has counting same-sex couples.
Pew’s story (which came out in September) discusses the way gender reporting on the census confounds the data.
The story in FiveThirtyEight reports on how the Census Bureau is working to make it’s questions gather more accurate data.
The Antidote for “Anecdata”: A Little Science Can Separate Data Privacy Facts from Folklore
Daniel Barth-Jones | Info/Law Blog [Harvard]
November 21, 2014
This is a great piece that shows again that most of the publicity about re-identification in data are overblown:
The 11 in 173 million risk demonstrated for this celebrity ride re-identification (or 1 in 15,743,614) is truly infinitesimal. To put this in perspective, this risk is over 1,000 times smaller than one’s lifetime risk of being hit by lighting. With proper de-identification applied and the cryptographic hash problem fixed in any future data releases, this spooky specter of celebrity cyber-stalking using TLC taxi data is likely to vanish as soon as one turns on the lights.
This blog post is in reaction to the release of NYC taxi medallion data, which were improperly anonymized. A previous blog post described the data.
Here is the piece that sensationalizes the possibility of re-identification, based on famous people who ride cabs.
Riding with the Stars: Passenger Privacy in the NYC Taxicab Dataset
Anthony Tockar | Neustar Blog
September 15, 2014
This is a big data resource, and more. Check out the reaction to the bad anonymization here.
20GB of uncompressed data comprising more than 173 million individual trips. Each trip record includes the pickup and dropoff location and time, anonymized hack licence number and medallion number (i.e. the taxi’s unique id number, 3F38, in my photo above), and other metadata.
Before the link to the data, here’s an analysis based on similar data:
Why New Yorkers Can’t Find a Taxi When It Rains
Eric Jaffe | City Lab Blog
October 20, 2014
Provides a nice synopsis of some research using taxi cab rides. Read it for the links to the formal research papers.
New York City Taxi Cab Trips [in small chunks]
FOILing NYC’s Taxi Trip Data
Chris Whong | personal website of an Urbanist, Mapmaker, Data Junkie
March 18, 2014
a synopsis of how he got the data via a FOIA request & a link to the data on rides/fares as single files, instead of the chunked version above.
and the story about how the taxicab medallion IDs were improperly anonymized:
Poorly anonymized logs reveal NYC cab drivers’ detailed whereabouts
Dan Goodin | ars technica
June 23, 2014
On Taxis and Rainbows: Lessons from NYC’s improperly anonymized taxi logs
Vijay Pandurangan | Medium blog