Little Rascals - A View of Pre-K Schools in NYC

Posted on Feb 17, 2020
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


I designed a reporting dashboard project examined the locations of pre-school (β€œPre-K”) locations throughout the New York City (NYC) metro area. The project focused on identifying areas which had a lower footprint based upon capacity in terms of number of slots.

The end goal of this analysis was to aid city or state officials in determining which locations were in greatest need of additional expansion. Β 


Before getting into the core of the analysis, we will touch upon a brief history of the public Pre-K program in NYC and discuss the geographical segmentation utilized for the analysis.

New York City initially rolled out its universal Pre-K program in September 2014. Being achieved within the first year of mayor Bill de Blasio’s first term, this start date marked an expedient start to an ambitious initiative. Initially, the program encompassed only four-year old children. Four years later, for the 2018-2019 school year, the program was expanded to include three-year old children as well.

The lowest level at which population information is available is the segmentation utilized by the U.S. Census Bureau, which is referred to as the census tract. New York City, for population forecasting purposes, utilizes a slightly more aggregate segmentation called the Neighborhood Tabulation Area (β€œNTA”). Because NTAs represent combinations of entire census tracts put together, census population information can easily be aggregated by city government from the census tract to the NTA level.

The metro area of NYC divided by its approximately 195 Neighborhood Tabulation Areas (NTA)

For my initial structuring of the analysis, NTAs represented a nice middle ground between something at a high-level such as an entire NYC borough and the very detailed census angle.

Analysis setup

The analysis of Pre-K coverage in NYC was performed at the NTA level utilizing population information from the most recently completed census in 2010 and school location information from September 2018. At the time of this article (February 2020) a new census tabulation was in process and I am eager to see how much the analysis changes with more recent population information.

Seat coverage was determined according to two metrics, the first a gross metric representing total Pre-K seats for a given NTA and the second being a ratio of seats scaled to 1,000 population.

Viewing lens

The primary vehicle for examining Pre-K coverage throughout NYC was facilitated through an R Shiny application. The use of the popular map library, Leaflet, was also instrumental in creating a dynamic, interactive map.

Below is the initial landing page of the application. The side panel includes four items for user input and the main panel includes a heat map and a table that itemizes the information that underpins the map.

The first input is what type of seat metric is being charted, either total seats or the seat ratio discussed earlier. The second option allows either all boroughs to be viewed at once or individual ones to be selected one at a time. The third is an option to highlight in red a certain number of areas that were the lowest based upon the metric and borough being examined.

Given that the heat map utilizes a color scheme where darker shades represent a higher relative amount of either total seats or seat ratio and lighter shades represent the lower end of this spectrum, the highlighting feature helps to visually call-out areas with relatively less seat coverage.

Brooklyn Map with Highlighting

The final metric is population threshold, which is particularly useful for excluding the impact of a disproportionately small denominator for areas such as the airports or parks that do not have a high living population but do have some seats.

The final metric is population threshold, which is particularly useful for excluding the impact of a disproportionately small denominator for areas such as the airports or parks that do not have a high living population but do have some seats.

Some general observations

Most NTAs have a concentration in the 5 to 15 range with a heavier concentration of areas with fewer than 10 seats per 1,000 than there are areas at the high end of the range.

Looking at range of seats per population across the five boroughs most of them fall in a similar band, with Manhattan a little bit on the lower end.

In revisiting the main app page, my hunch for this distinction is perhaps due to Manhattan having a higher concentration of commercial areas relative to its population size. We see that a lot of the lower end of the spectrum are areas such as Midtown and Upper East Side.

Seat ratios for Manhattan. Note the different color scheme (green) for the seat ratio metric.


Manhattan NTAs with lowest seat coverage in terms of the ratio metric

Future Ideas

While a good start, I have some ideas for refinements that I would like to incorporate. One idea is to have a measure of population that is a more appropriate proxy for seat capacity demand. The current iteration utilizes a simple total count for the population, but different areas may have differing proportions made up by families or children in the target range for Pre-K schools.

The other main area that I would like to update is allowing for the metrics to be determined at the school district level. This segmentation may be more appropriate for education officials and would give credit for NTAs that are within the same school district, which the current analysis does not in examining these areas on an individual basis.

About Author

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI