Note: This post was originally published April 29, 2011, and updated in June 2020. In February 2022, I updated it again using 2020 Census data.
Anyone doing population analysis by NYC police precinct might find this post helpful, especially if you're interested in race and/or ethnicity analysis by precinct.
Back in 2011, I wanted to compare the racial and ethnic breakdown of low-level marijuana arrests — reported by police precinct — with that of the general population. The population data, of course, is available from the US Census, but it's not provided by police precincts, which also don't follow any major census boundaries like census tracts. Instead, they generally follow streets and shorelines. Fortunately, census blocks (which in New York, are often just city blocks) also follow streets and shorelines.
So I used US Census block maps and precinct maps from the city to figure out which blocks are in which precincts. Since population data is available at the block level, that data can then be aggregated into precincts.
In this, the third version of this post, I've updated the counts now that the 2020 population data is available.
The 2020 data
• nyc_precinct_2020pop.csv is the 2020 Census population, race, and ethnicity (Hispanic/non-Hispanic) data by NYPD police precinct. The column headers from the US Census are a little cryptic, but you can translate them using the P1 table metadata file and the P2 table metadata file.
• nyc_block_precinct_2020pop.csv — every populated block in NYC is identified by its ID (called "GEOID20"), is matched to the police precinct it sits within, and contains the block's race/ethnicity information. Use the same metadata tables to translate the column headers. Also be sure to read about the caveats below.
• nyc_precincts.geojson depicts the geographic boundaries of the NYPD precincts I used for the files above, as they existed in February 2022. As of this post, the information on the NYC Open Data portal indicates it was last updated on Nov 24, 2021.
Caveats for the 2020 data
The biggest caveat is that the US Census has introduced data fuzziness, or "noise," to make it difficult to identify individuals based on census data. This fuzziness is more pronounced at smaller geographies — the smallest being census blocks, which I've used for these calculations. Hansi Lo Wang did a great primer on these data protections for NPR, and the US Census Bureau has put out a lot of material on how it uses "differential privacy."