Joe Germuska JoeGermuska

A question came up on the US Census slack, leading to the recognition that the US Census Bureau API doesn't support queries for data for "all ZCTAs in a state". Nothing about the Census Bureau's definition of ZCTA requires that they be contained within a single state, which is probably why the API rejects the query with a message, error: unknown/unsupported geography heirarchy.

I've been looking for a general method to answer these kinds of questions for a long time. This Gist demonstrates a workable approach. It's based on data published by the Census LEHD LODES program, which provides, for every Census block in the US, a crosswalk indicating which geographies that block is in. (The set of geographies is limited but still very useful. See the technical doc PDF for more details.)

For any two geography types, one can simply select those two columns from the crosswalk and eliminate dupli

I had a repo with dozens of abandoned or merged remote branches. Before deleting them wholesale, I wanted to know just how old they were.

I adapted this Stack Overflow answer, but dressed it up in a for loop since I wanted all of them. Also, since I wanted to sort them, I switched the date format to %cs (Short ISO), which didn't work in git 2.18.0 but does work in 2.28.0

Some quick work to facilitate reading data for the Census 2020 PL94-171 data release into Pandas dataframes.

Sample data for Providence County, RI can be downloaded from https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html, as can auxiliary materials.

The file headers.py was created by parsing the SAS import scripts from the link above.

It seems as though the Census Bureau removed the sample data for Providence County, RI, against which this code was tested. You can get a copy of it from http://files.censusreporter.org/ri2018_2020Style.pl.zip

Note: The full data release is now available at https://www2.census.gov/programs-surveys/decennial/2020/data/01-Redistricting_File--PL_94-171/

I had reason to want a list of Chicago Community Areas annotated to assign each to a region of the city. I couldn't find it in structured form, so I built it.

I used this research guide from Harold Washington College Libraries, which assigned each community area to a region and listed key neighborhoods. I simplified "Central, Near North, and Near South Side" to just "Central". I also simplified "West and Near West Side" to just "West."

Since part of my project included aligning population numbers, the CSV in this gist also includes a simplified version of the 2020 Decennial Census redistricting table P2, "Hispanic or Latino, and not Hispanic or Latino by Race," as downloaded from Census Reporter. The simplifications were to rename columns, and to omit all of the detailed columns for "two or more races." I also dropped the 2010 and percentage change columns, and computed the per

	const IdyllComponent = require('idyll-component');

	const http = require('https');

	const jsonPromise = ((url) => {

	// via https://www.tomas-dvorak.cz/posts/nodejs-request-without-dependencies/
	// return new pending promise
	return new Promise((resolve, reject) => {
	const request = http.get(url, (response) => {

	{
	"01": "Alabama",
	"02": "Alaska",
	"04": "Arizona",
	"05": "Arkansas",
	"06": "California",
	"08": "Colorado",
	"09": "Connecticut",
	"10": "Delaware",
	"11": "District of Columbia",

	-- Sometimes one accidentally loads data that is in ISO8859-1 (aka "Latin-1") encoding having assumed that it was actually UTF-8
	-- so far it seems like Ã is a good flag although if your data might also have that correctly, this is less simple...
	update tiger2020.census_name_lookup
	set simple_name = replace(simple_name, 'Ã±', 'ñ' ),
	display_name = replace(display_name, 'Ã±', 'ñ' ),
	prefix_match_name = replace(prefix_match_name, 'Ã±', 'ñ' );
	update tiger2020.census_name_lookup
	set simple_name = replace(simple_name, 'Ã¼', 'ü' ),
	display_name = replace(display_name, 'Ã¼', 'ü' ),
	prefix_match_name = replace(prefix_match_name, 'Ã¼', 'ü' );