The Washington Post argues that the growth of cities results in a loss of African-Americans. FiveThirtyEight argues that spatial growth and demographic growth are different and the way city limits are defined complicates the definition of a city’s population.
Wonkblog highlights four maps created by Seth Kadish of Vizual Statistix.
The maps show … the percentage of a county’s population that receives OASDI benefits; the percentage of OASDI beneficiaries who are retired, rather than disabled; the areas where payments to men most greatly outweigh those given to women; and the average monthly OASDI payment, in hundreds of dollars.
Japan population shrank by 268,000 in 2014, the largest reduction on record, and the government has done a terrible job at predicting it’s fertility rate.
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.
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.
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
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:
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.
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.