SparkyData

Questions about New York, answered from public data

The city publishes an enormous amount about itself, in hundreds of tables that were never meant to be read together. Most of what gets written from them is an average and a narrative, and the average hides most of what is going on. Each article here takes one question, finds the datasets that bear on it, and works out what they can and cannot say — with interactive charts you can cut your own way, the data, and the code that produced every number.

All models are wrong; some are useful. We spend our time on the second clause.

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Latest articles

  1. New York City's in-person Election Day share fell from 96 in 100 before 2019 to about half in 2024
  2. A third of New York City's adult citizens are naturalized, nearly twice the share of 1990, and the share has held there since about 2012
  3. New York City's mayoral turnout nearly doubled in 2025, to 39 in every hundred adult citizens, the most for a mayor since 1993
  4. In New York City's midterms, the gap in reported voting between the oldest and youngest adult citizens widens to 19 points, from 12 in presidential years
  5. Most of New York City's recent unemployment gap is people out of work six months or more

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New York City's in-person Election Day share fell from 96 in 100 before 2019 to about half in 2024

In the Board's count, a third of 2025's ballots were cast early; the survey's long view puts in-person Election Day voting at 96% before 2019 and 47% in 2024.

Until 2019, almost every New York City voter cast a ballot in person on Election Day. By 2024, about half did; most of the rest voted early or by mail. Early voting for the governor's race began on 24 October. Is this year running ahead of 2022, and do most early voters really wait for the last weekend?

Open the article Dataset (aggregate statistics) Code

What SparkyData is

A research publication about New York City, built on the public record: the Census Bureau's surveys and microdata, the city's own housing, shelter and tax data, the Federal Reserve, the Bureau of Labor Statistics, and whatever else a question turns out to need. Every article starts from a number someone has already published and ends with what that number was hiding.

What is published about a city is a collection of slices, each careful and correct, no two cut from the same block. The interesting questions live in the gaps between them, and in the intricacies that a citywide average smooths over. Finding out how far the data can be pushed into those gaps, and saying plainly where it stops, is the whole job. Rather than offer one narrative, an article gives a general view and the controls to explore the facets that interest you.

How an article is built

  1. Start from the published table. Quote the official number, reproduce it from the underlying microdata, and reconcile the two, measuring and explaining any difference, before going anywhere the table does not.
  2. Cut the microdata the way the table cannot. Household by household, with the survey's own margins of error on every bar.
  3. Bring in the second dataset — tax schedules, a housing survey, a shelter census — and say exactly how it was joined and what that join assumes.
  4. Separate what is measured from what is inferred. A relationship observed in one survey is a finding. A relationship stitched across two is an estimate, reported with a range or not at all.
  5. Ship the working. The dataset, the script that computes every figure, and the charts you can filter yourself.

Why the interesting questions are hard

The census knows a household's income and rent but not whether its lease is stabilised. The city's housing survey knows the lease but has a tenth of the sample. The Federal Reserve knows the balance sheet but only for the nation. No table crosses all three, so a question like "who really lives in a rent-stabilised apartment, and what do they own?" has no published answer.

Most of the time the honest answer is partial: this much can be measured, this much can be estimated under a stated assumption, and the rest is not knowable from public data. Articles here say which is which, in the text and on the chart.

What every article promises

Sourced

Every quoted figure links to the table it came from, with the vintage and, where the source supports one, the margin of error. Nothing is typed in from memory.

Reproducible

The numbers in the text are computed by the shipped script from the shipped data. Rerun it on the same inputs and you get the same numbers.

Labelled

Measured, estimated or assumed — each number says which, and every chart says what it cannot show.

How the work is done

A lot of the research is done with the assistance of a combination of AI agents: pulling and harmonising datasets, drafting and running the analysis code, checking figures against their sources. A project of this scope would not be possible for one person otherwise. What they do not do is decide what is true.

There is a human in the loop at every stage. Every article is read, checked and proofread by an actual expert in the field before it is published: a data scientist with over ten years in the financial industry, anonymous for now to keep the focus on the models and the procedures. The tools are there to manage the scope, not to add to the slop. The site starts with New York because it is a dynamic and unusually well-documented city; it may wander further later. More about the site.

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