Estimating Supply & Demand in PK4 Planning

As I have described earlier (here, and here), I am working on projection estimates for PK4 in MNPS. Given the limitations of a summer-only role and the many, many factors at play in the decisions, I do not know what role these estimates will ultimately play in the strategy around PK4 expansion. But I can at least feel confident that I am providing accurate and useful information around specific questions, namely: the (1) available supply and the (2) current unmet need for PK4 across the district and in each cluster, and (3) illustrate trends in each cluster’s distinct PK4 needs and assets. So! In the interest of sharing what I have learned thus far, here is a progress report of sorts.

Definitions:    I have already sought and obtained initial figures on each of these inputs, and am in the process of cleaning, re-organizing, and crunching the numbers so that I can pull them together in a meaningful way. 

1.     Demand = Number of students who applied for this year’s PK4 through MNPS, as organized by their zone of residence

2.     Need = Students who are projected to enroll in a MNPS zoned school for Kindergarten the following year (Proxy for PK4)

3.     Need Trend = Anticipated growth in Kindergarten enrollment over time, for each zoned school

4.     Public Supply, Total = PK4 slots currently available thru MNPS and Head Start, by cluster 

5.     Public Supply, EE = Public PK4 slots allocated exclusively for children receiving Exceptional Education services

6.     Private Supply = Slots currently provided by private entities, by cluster.

7.     Private "High-Quality" Supply = Slots that are provided by entities that are accredited by the National Association for the Education of Young Children (NAEYC), or that are three-star providers as determined by the voluntary Star-Quality Child Care Program (determined by the TN Department of Human Services Child Care Services office).

8.     Accessibility = Characteristic of each school zone's residents, which indicates families' reliance on public transportation vs. private vehicles (aggregated at the census tract level, using the ACS package in R - about which I have a blog post under construction).

Assumptions:  Every child who attends a MNPS elementary for kindergarten is likely to consider enrolling in a MNPS PK4 classroom if it were available in their residential cluster. Therefore, it is appropriate to consider projected Kindergarten enrollment in AY1415 as an appropriate proxy for projected PK4 need in AY1314. 

Limitations:    (1) Analysis captures anticipated trends for zoned schools, rather than trends for schools of choice or specialty schools. (2) Although it is intended to reflect the reality that some parents will always choose to keep their 4-year-old at home or choose private providers rather than enroll them in public PK4, the distinction between Need and Demand seems a little bit fussy, and ultimately may not be useful. (3) This is a preliminary working construct of "high quality" and cannot, by definition, capture all the characteristics that make up a quality ECE experience. But we must start somewhere, and so we start with NAEYC and Three-Star.

Thoughts, questions, suggestions? Did I miss something really obvious? 

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.