Gentrification in America Report
Mike Maciag | Governing
This resource is city-specific and provides both counts and maps of gentrified census tracts for the 50 largest cities. To be eligible for gentrification a census tract’s median household income and median home value were both in the bottom 40th percentile of all tracts within a metro area at the beginning of the decade. The gentrified tracts recorded increases in the top third percentile for both measures when compared to all others in a metro area.
And more broadly, this resource has a special issue on gentrification:
The G-Word: A Special Series on Gentrification
The titles in this series are:
Do Cities Need Kids?
The Neighborhood Has Gentrified, But Where’s the Grocery Store?
Just Green Enough
Gentrification’s Not So Black and White After All
The Downsides of a Neighborhood ‘Turnaround
Some Cities Are Spurring the End of Sprawl
Keeping Cities from Becoming “Child-Free Zones”
From Vacant to Vibrant: Cincinnati’s Urban Transformation
Can Cities Change the Face of Biking?
This is an index of happiness created from tweets. The index provides a daily score, which can be toggled to exclude weekends, Mondays, etc.
This is an excellent resource because the creators of this happiness index describe the calculation of the index, the words used in it, provide an API, have links to articles based on the index, etc. It is a valuable resource, even if you do not care about happiness as it provides a template for many other uses of data from Twitter.
Instructions [Documenation of index via video or written - click on links]
Words [Words used in index, ranks, etc.]
Blog [The Computational Story Lab. . . mostly related to happiness]
Press [press coverage]
Papers [refereed papers by research team]
Talks [maybe you need a clip for a lecture]
API [lots of examples]
I ran across this in the Wall Street Journal (slide 58 of 93):
Can happiness from tweets reduce drawdowns from selling VIX?
Selling VIX futures has been profitable historically. However, the strategy can be subject to drawdowns, when there is risk aversion . . . . Using the Hedometer index as an input, we have created a Happiness Sentiment Index (HSI), which can be sued to proxy market risk sentiment. . . .
See next post for more on the Hedometer Index.
The Census Bureau has released its last 3-year ACS product with the 2011-2013 release. This is a cost-cutting move, although the Census Bureau might argue that it never meant for there to be a 3-year product in the first place.
The Census Bureau is not cutting back on data collection – it is eliminating the tabular release of the 3-year data (geographic areas of 20,000+). The 1-year data are for geographies of 65,000+ and the 5-year data have no population limits. These will continue to be released.
The microdata products have share the same release types: 1-year, 3-year, and 5-year. These all share the same geographic limit (PUMAs), but the 3-year and 5-year products are not just concatenations of the 1-year files. They have been re-weighted and income-denominated items are inflated to the last year (e.g., 2013). [See explanatory note from IPUMS].
The ACS 3-year Demographic Estimates are History
Brendan Buff | APDU Blog post
Feb 3, 2015
Census Bureau Statement on American Community Survey 3-Year Statistical Product
Stanford University Libraries | Ron Nakao’s Blog
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 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
The PSC Infoblog has reported on this earlier here, but this is still of interest.
The U.S. Census Is Trying To Get A More Accurate Count Of Arab Americans
Ben Casselman | fivethirtyeight.com Blog
November 24, 2014
Note that this article mentions that the Census Bureau did a special tabulation for Homeland Security to provide counts of Arab populations by geography (place and zip code).
Some Arabs have expressed reluctance to identify themselves on a government form, especially after the Census Bureau shared detailed data on the Arab-American population with the Department of Homeland Security in the early 2000s
These “detailed tabulations” referenced above, were public use tables from American FactFinder. Here’s the original FOIA request from the Electronic Privacy Information Center:
FOIA request: Department of Homeland Security Obtained Data on Arab Americans From Census Bureau [Source: EPIC]
Here is the example for Places drawn from DP-2. Here’s the example for Zip Codes drawn from (Tables PCT16 and PCT17).
Researchers at the Census Bureau and the Minnesota Population Center matched census responses across the 2000 and 2010 Census and examined changes in the Race/Hispanic origin responses. They found that the biggest change was Hispanics changing their “Other Race” choice in 2000 to “White” in 2010. The groups least likely to change responses were those who identified themselves as non-Hispanic white, black or Asian in 2000.
America’s Churning Races: Race and Ethnic Response Changes between Census 2000 and the 2010 Census
Center for Administrative Records Research and Applications & Minnesota Population Center
CARRA Working Paper #2014-09
August 8, 2014
This paper is nicely summarized here – based on the PAA version:
Millions of Americans changed their racial or ethnic identity from one census to the next
D’Vera Cohn | Pew Research Center
May 5, 2014
As a reminder, the Census Bureau has spent the better part of the past year, looking to change how it collects data on race and ethnicity.
U.S. Census looking at big changes in how it asks about race and ethnicity
D’Vera Cohn | Pew
March 14, 2014
2010 Census Race and Hispanic Origin Alternative Questionnaire Experiment[website]
2010 Census Race and Hispanic Origin Alternative Questionnaire Experiment[report]