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?
President Obama recently appointed Dr. DJ Patil the Deputy U.S. CTO for Data Policy and Chief Data Scientist.
Read Patil’s Memo to the American People from February 20 and watch his address, Data Science: Where are We Going? with an introduction by President Obama.
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.
Nathan Yau of Flowing Data recently published a tutorial on loading data and basic formatting in R. The tutorial covers loading data from CSV files, subsetting data frames, editing data to make it easier to manage and merging multiple datasets.
[click here for link to NYT graphic]
The Equal Justice Initiative has documented 4,000 lynchings in the South between 1870 and 1950. This resource is potentially useful for examining out-migration of blacks, particularly men, from the South during this era. It could also be useful for explaining current race-based inequalities, including incarceration.
Lynching in America: Confronting the Legacy of Racial Terror
Summary Report | Equal Justice Initiative
Supplement: Lynchings by County [pdf only]
Note that the graphic by the New York Times has a time-dimension in it. I am awaiting the full report from the Equal Justice Initiative to see what additional detail is available in it.
Press Coverage [scroll down]
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
The above cartoon is from the Florida Sun Sentinal back in early 2014 as New York just held on to its ranking as the third largest state. With the release of the most recent population estimates, Florida has now edged out New York.
Florida Passes New York to Become the Nation’s Third Most Populous State, Census Bureau Reports
December 23, 2014
We’ve updated our Apportionment Calculator. See which states are projected to lose/gain seats in 2020 based on the 2014 results.
And, no. North Dakota is not gaining a seat, even as it is the fastest growing state.
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.