Congressional redistricting occurs across the states after every decennial census, and is typically a fiercely partisan process marked by partisan strategizing, cries of gerrymandering, and endless court battles. Single-member districts are drawn to be contiguous, equipopulous, and compliant with the Voting Rights Act; states also attempt to make compact districts, align them with political subdivisions, and maintain communities of interest. A few states seek to create competitive districts that reduce the incumbent’s advantage. Redistricting law has changed markedly in the past century, beginning with the enforcement of equal population in the 1960s; since then emphasis has shifted to racial and partisan gerrymandering. One innovative technique to reduce gerrymandering is the independent redistricting commission; these commissions are reviewed for the six states who use them for congressional districts. Based on this comparative analysis, recent attempts in Texas to reform redistricting do not appear strong enough. Public input into the redistricting process should be strengthened and the current proposal to give undue influence to rural interests should be modified. But given the difficulty of enacting redistricting reform, more conservative alternatives should also be considered: a constitutional amendment prohibiting mid-decade redistricting, federal legislation on electoral procedures, and multi-member districts with proportional representation.
Table of Contents
Chapter 1. Principles of Redistricting - page 1
Chapter 2. Redistricting Law and Precedent - page 17
During my flight from DC to Texas today I finally got around to creating my first draft of a logic map (or model) for new media in a political advocacy context - a simpler abridged version has been sketched out on my office whiteboard for a few months now. See below:
My goal here is to lay out the interactions and chain of events that leads a visitor to a new media campaign, and then to document the primary engagement methods after they're in new media land. I didn't include every possible linkage arrow, and perhaps need to cut out some existing low priority ones to clean it up. The extent to which I go into other departments and the general level of detail is also somewhat arbitrary.
I used ArgoUML to create the model, which I was able to download for free and seems to be more for developers, so please excuse the ugliness. I had wanted to use Omnigraffle but haven't purchased it yet and waited until after my trial license had expired (only 14 days??) to begin this project. Working in 2d space was limiting: I would have preferred three or more dimensions to help organize & separate the different units.
What do you think - any missing areas, disagreements, or other thoughts? Have people used logic models for other projects? I plan to continue improving this so would be glad to get feedback. I would like to help develop stronger theoretical infrastructure for new media, and I think logic models are one good approach to support more rigorous program management & evaluation.
This chart takes a look at voter registration, turnout, and turnout-of-registered trends across the past four presidential elections in the United States: 1996, 2000, 2004, and 2008. The data come from the Current Population Survey and are smoothed to reduce the variability across age due to survey sampling. My previous version showed voter registration and turnout for 1996-2004, so this version has the added comparison of the 2008 elections.
A few things that pop out:
Voter registration of newly eligible voters (18-20 years old) fell slightly in 2008 compared to 2004, and is very slightly higher for the rest of the youth cohort (21-29).
Those older than 30 have a drop in registration compared to 2004, falling back to 2000 levels.
Turnout for youth is similar for the 18-20 year old group with small increases for the 21-29 repeat voters compared to 2004; the huge increases compared to 1996/2000 are maintained all the way up to age 40. Older voters have the same or lower levels of turnout compared to 2004.
At the top, turnout of those registered was the highest for young people in 2008. This suggests that GOTV to young people has continued to improve, particularly if we look back to the much lower curves for 1996 and 2000.
What other thoughts or interpretations do people have? And what do you think about the chart - any ideas for improvement? The main thing I would like to add is an additional table or bar chart showing the distribution of the population across age groups, which will help with gauging each age cohort's relative importance in terms of total votes.
Finally, I have attached the Excel file with the data and the chart itself, so feel free to download it and play around with things. It also includes an alternative black-background version which should be better for projection and makes the colors pop - it was inspired by Al Gore's climate change slides, which were in the background as I updated this chart.
The student elections were last week at The University of Texas - congrats to Liam, et al. The focus was on Student Government but elections also included Texas Student Media, Graduate Student Assembly, and others. Overall turnout was 10,018 votes (PDF), or 20% of the student body based on Fall 2008 enrollment figures. While not yet available, the actual student body in the spring semester tends to be slightly lower than the fall due to drop-outs and transfers. Once spring enrollment data is released the estimated turnout will likely be 0.5 to 1.5 percentage points higher.
Here is a long-term graph of turnout:
It's pretty clear that 1) online voting increases turnout, 2) a larger student body tends to have lower turnout, 3) while recent turnout is relatively high, we haven't gotten close to pre-1970s turnout. Thoughts?
The role of online social media continues to grow in importance for political organizations. Facebook, MySpace, YouTube, Flickr, Twitter, and dozens of other sites need to be managed, tracked, and integrated. This takes a lot of staff time, but luckily these services continue to expand their support for external programs that can manipulate data or perform actions. APIs (application programming interfaces) allow different websites or computers to talk together and do things in the background. Even sites which don't provide official APIs can often be automated using third-party tools.
Loading emails into Gmail contacts fosters integration
In this article I'll show one simple script that uses Python and a third-party library (libgmail) to load a file of emails into gmail contacts. It's very common for web applications to support interfacing with gmail accounts to add users, so once your emails are in gmail it's simple to import them into Flickr, Twitter, etc.
The benefits of data integration
Why do this? Well for one, it helps organizations bootstrap their social media relationships. Normally your email list will be the largest medium for contacting members, so syncing that list into other services can really jump start your campaigns there. Rather than start from scratch you can build off your existing members, connect with them in different ways, and expand your online communication offerings. Even better, integrate social network synchronization into your data workflow: e.g. on a nightly (or hourly) basis sync new email subscribers into gmail, then sign into your socnets and add any new accounts you find. The "add any new accounts" step can be automated as well and will be explored in a later article.
It will take a while to load the emails into gmail, but once they are there it's just a few clicks to add all your members on Flickr, Twitter, or any other service that can import from gmail contacts!
Following our volunteers on Twitter
I tested this concept by loading 5,215 volunteer records into gmail. Of those, 590 could be found on Twitter (11.3% match rate) - not too shabby. And because the contacts are stored in gmail, I can always import again, say once a month, and add members who have recently joined. It would be interesting to see people try this for their own members and see what the match rates are for different services and organizations.
Create a new gmail account specifically for this data sync - don't use an existing account.
Every 2,000 records or so gmail will stop processing records and the script will abort. Just run it again and it will resume where it left off.
Twitter seems to only add up to 200 followers at a time, but you can repeat as needed.
Because it is slow, this method works best as part as incremental workflow on new records. For loading existing email records it may be easier to use gmail's import contacts via csv feature. The problem with the csv method is that it's limited to 3,000 contacts per file, so in order to handle more than a few thousand records you'll want an automated solution.
In theory, once you import email records into a service, you will want to capture the respective username & id for that email record. Then you can integrate that into your main database and use it for future messaging (potentially via that service's API). This would be automated.
Feel free to fork and make improvements to gmail_add_emails.py. It's easy to do on github. Or post feedback here.
Tip of the hat to KF for thinking of an email -> Twitter data sync in the first place. Good call!
It's a little tricky to show registration, turnout, and turnout of registered by age for three different elections all at once. I hope that the chart makes clear the great gains in 2004 for young voters, driven by huge increases in registration combined with strong GOTV efforts.
Selected items to note:
Turnout is essentially the same between 1996 and 2000, with a noticeable uptick in 2004 concentrated in young voters.
Voter registration is roughly the same for all three elections other than an increase in 2004 among those 35 years old or younger.
In 2004 turnout of 18 year olds (44%) was higher than registration among 18 year olds in 2000. In other words, even if 100% of registered 18 year olds had voted in 2000, overall turnout of 18 year olds could not have been as high as 44% without first increasing registration rates.
Among those who are registered, turnout has risen across the board from 1996 to 2000 to 2004, but again this increase is concentrated among young voters.
Among the registered population, those age 85 or older were the least likely to vote in 2004.
I was playing around with Census data tonight and wanted to see how a Lowess (bandwidth=.15) or polynomial (6-term) smoother compared. Both deal with the sampling variability of Census data by smoothing out a line as an approximation; the polynomial version takes all of the data into account while the Lowess version only incorporates nearby data (i.e. local vs. global smoothing).
It looks like either is fine for registration or turnout, with Lowess being a little bit better at showing local changes as one would expect. I still need to look at turnout of registered though Adding in turnout of registered there is a stronger case for Lowess in that it better shows the 18 year old turnout bump among those who are registered.
Update: For those following along at home, here is Stata code you can use to try it out (data compliments of NBER):
drop if prcitshp == 5 | prtage
gen voted = pes1 == 1
gen registered = voted == 1 | pes2 == 1
gen age = prtage if prtage > 0
collapse (mean) registered (mean) voted, by(age)
lowess registered age, bwidth(0.15) gen(registered_smooth) nograph
lowess voted age, bwidth(0.15) gen(voted_smooth) nograph
* Repeat if registered == 1
* Copy results into Excel and make a chart (see attached .xlsx file for Excel 2007/2008).