When neighborhoods gentrify, what happens to the zoned school?
A reproducibility log: the data this analysis touched, the queries it ran, the outside sources it leaned on, and the steps a third party would follow to re-run it.
Summary
Joined NYC Department of City Planning gentrification proxies (median income change by census tract) with the zoned-school ID for each tract, then looked at the zoned schools' outcome trajectory over the same window.
Data sources
- Database tableschools
One row per New York City public school (DBN, name, district, borough, school_type, grade_band, latest_enrollment, proclivity_decile, proclivity_score, closed_at, admission_category).
- Database tableschool_year_metrics
Long-format per-school per-year metric facts (school_dbn, year, metric_key, subgroup, value, suppressed). Loaded from DOE/NYSED public files via scripts/loaders/*.
- External datasetNYC DCP — Census Tract median income by year
Steps
Join zoned-school DBN per ZIP/tract with tract median-income change 2010-2020.
Caveats
Zoned-school assignment shifts; the join is approximate. The story is more pattern-hunting than identified causal claim.
Reproduce
Clone the repo, set DATABASE_URL to a Postgres with the project schema loaded, run `npx tsx scripts/loaders/<source>.ts` for any not-yet-loaded data, then issue the queries in the Steps section. The story page also lists the exact `metric_key`/`subgroup`/`year` filters used. Searchable by the answer's headline number — every figure is recomputable from the queries shown.
The recipe lives at data/stories/recipes.ts in the repo. Corrections welcome.