ACS, SABINS: Wrangling census data for school-level analysis

[Post in progress] Your nerd giggle of the day: My phone keeps auto-correcting ACS (the American Community Survey) to ACA (the Affordable Care Act). It has also been correcting TTFN (ta-ta for now!) to TANF (Temporary Aid for Needy Families, or, the federal grant that provides public assistance or "welfare"). I find this really amusing.

Continuing the project from earlier  - one step forward, two steps back, more forward.

Looking for resources on graphics for another project, I found the choropleth package.

Alas, while it DOES do zip codes, this package does not go all the way down to the block or even block group level data.

Nested Census geographies (from census.gov)

BUT! This lead me to the acs package, from Ezra Haber Glenn outta MIT. And although the documentation only discusses up thru ACS 5-year 2011, the Census API now goes up through 2012. Yay! Could have saved me a bunch of time in pulling the raw data itself.... Alas. Now to install. Walking through the tutorial (essentially) and pulling the necessary code for the county, by block or block group. [Use pretty R to make the R snippets look pretty]

% related to links to look at later - http://gadm.org/ 

Childfind: Data and the 4YO children in Nashville (Part 1)

Well, this is going to be complicated.  

As I wrote at the outset of this project, one of the policy/planning questions that needs to be addressed is how many four-year-old children live in Davidson County? Generating a smart estimate has turned out to be more complex than I anticipated initially.

In short, no wonder nobody has tackled this process before!

Turns out, isn't really the responsibility of any one particular government entity to track children in this age cohort. Or, rather, lots of entities track some of these kiddos, but nobody tracks all of them. So I need to bring the relevant data together and build the model myself.

Initially, my plan was to (a) fit kindergarten enrollment numbers, by year and by school cluster, and then (b) use that model as a proxy for the number of five-year-old children who would have been eligible for four-year-old (universal, voluntary) pre-K had it been in place in the previous year. From this figure I could determine trend lines for PK need, and predict/forecast  But as I discovered, there are some characteristics of the data itself that will necessitate adjustments to the plan.

School Geography is a Cluster...

Before you scold me for the pun implied above, you need to know that MNPS is organized into twelve geographically delineated units called "clusters" -- a zoned high school, and its feeder elementary and middle schools. You can access the (updated as needed) cluster maps on the district website, but here is how the clusters were organized in AY1314(1):

Each of the clusters can include zoned and charter schools alike. If you open it in another window, you can zoom in, etc. The colors alone should give you a sense of how these clusters are jumbled across the county.