AI, startup hacks, and engineering miracles from your friends at Faraday

How to get U.S. Census data as CSV — censusapi2csv

Bill Morris on

This post is part of our data science series.

The U.S. Census and American Community Survey (ACS) are the crown jewels of open data (bother your Representative today to make sure they stay that way), but working with data from the Census API isn't always intuitive. Here's an example response to an API call for ACS per capita income data:

[["B19301_001E","state","county","tract","block group"],
["25611","50","007","000100","1"],
["36965","50","007","000100","2"],
["29063","50","007","000200","1"],
. . .

It's not a CSV, it's not exactly JSON, it's just . . . data. We tend to use CSVs as our basic building blocks, so we built a tool to nudge this response into a pure format. Here's how to use it:

Install

npm install censusapi2csv -g

Usage

Let's grab a few things from the ACS API: total population (B01001) and per capita income (B19301), for every block group in Chittenden County, Vermont:

censusapi2csv -l 'block group' -f B01001,B19301 -s 50 -c 007

. . . we can even pipe this into our favorite CSV-parsing tool, xsv:

censusapi2csv -l 'block group' -f B01001,B19301 -s 50 -c 007 | xsv table

. . . and we get a formatted look at the data:

B01001_001E  B19301_001E  state  county  tract   block group
3057         25611        50     007     000100  1
1200         36965        50     007     000100  2
1641         29063        50     007     000200  1
1882         28104        50     007     000200  2
699          61054        50     007     000200  3
. . .

This is just a tiny step in the process of working with census data - and there are many alternative approaches - but we thought it was worth sharing.

Geochunk: fast, intelligent splitting for piles of address data

Bill Morris on

aurora

This post is part of our practical cartography and data science series.

The problem: you want to split up a few million U.S. address records into equally-sized chunks that retain spatial hierarchy. You want to do this without anything other than a street address (geocoding is expensive!). Maybe you want to do this as part of a map/reduce process (we certainly do), maybe you want to do some sampling, who knows?

The solution: Muthaflippin' Geochunk

Anyone who's ever used U.S. ZIP codes as a way to subdivide datasets can tell you: 60608 (pop 79,607) is a totally different beast than 05851 (pop 525). They're not census tracts; it's not really appropriate to compare them statistically or thematically.

Our solution - largely the work of platform wizard and Rust enthusiast Eric Kidd - is to bake census data into a tool that does the splitting for you at a level that allows for easy comparison. More specifically:

It provides a deterministic mapping from zip codes to "geochunks" that you can count on remaining stable.

Check out the Jupyter notebook that explains the algorithm in detail, but it works like so:

Install

Install rust first if you don't have it:

curl https://sh.rustup.rs -sSf | sh

. . . then geochunk, using the rust package manager:

cargo install geochunk

. . . or install from one of the prepackaged binaries.

Use 1: Indexing

Build a table that assigns every U.S. zipcode to a geochunk that contains 250,000 people:

geochunk export zip2010 250000 > chunks_of_250k_people.csv

Use 2: List processing

Alternately, let's try a pipeline example that uses geochunk csv: say you want to parallel-process every address in the state of Colorado, and you need equal-size but contiguous slices to do it.

wget -c https://s3.amazonaws.com/data.openaddresses.io/runs/283082/us/co/statewide.zip && unzip statewide.zip
  • Pipe the full file through geochunk, into slices of about 250,000 people each:
cat us/co/statewide.csv | geochunk csv zip2010 250000 POSTCODE > statewide_chunks_150k.csv

. . . and now you have 2 million addresses, chopped into ~8 equally-sized slices with rough contiguity:

denver

Geochunk works on this scale in 1.38s (Have you heard us evangelizing about Rust yet?), leaving you plenty of time for the real processing.

This tool is serious dogfood for us; it's baked into our ETL system, and we use it to try making a tiny dent in the Modifiable Areal Unit Problem. We hope you'll find it useful too.

Be not afraid of ZCTAs

Bill Morris on

This post is part of our practical cartography series.

Most American geographers will note that - as much as we'd like it to be otherwise - ZIP Codes are not polygons. Rather, they're constantly-changing lines used by the USPS to coordinate delivery in an efficient network. Many of us polygon-happy mappers use ZIP Code Tabulation Areas (ZCTAs) instead; these are provided by the US Census as a reasonable open data alternative to ZIPs. They're particularly nice for thematic mapping (though their shortcomings have also been well-documented):

map

But why use ZCTAs if they can never be reconciled with their ground-truth ZIP cousins?

Because the difference is small.

Faraday has address and location records for every household in the country, and it was straightforward to check for disagreement between the ZIP Code of each physical address and the ZCTA polygon that contains it.

Here are the results, broken down by state

The national error rate of ZCTAs is 1.4%. That might be too high for some use cases, but perfectly acceptable for others. There's some regional variation, too: you're usually safe to use ZCTAs in Hawaii and Maine, but might want to exercise caution in Oregon and Utah.

Happy mapping!

Plancha: how to flatten multi-sheet excel workbooks

Bill Morris on

This is part of our series on data science because it belongs in your toolchain.

If you work with data long enough - actually scratch that; if you work with data for more than a week - you'll run into the dreaded multi sheet (or tab) excel workbook. Sometimes the sheets are unrelated, but other times they should really all be stacked together in the same table, ideally in a more-interoperable format than .xlsx:

in

Enter plancha. Named for the trusty tortilla press, we built this simple CLI tool to flatten multi-sheet excel files, resolve header mismatches, and return a pipeline-friendly csv, like this:

out

Install

This is a node.js tool, so use npm:

npm install plancha -g

Usage

Just feed it an input .xlsx file:

plancha -i myfile.xlsx


Happy data-pressing!

How we made our CSV processing 142x faster

Bill Morris on

This post is part of our data science hacks series

At Faraday, we've long used csvkit to understand, transform, and beat senseless our many streams of data. However, even this inimitable swiss army knife can be improved on - we've switched to xsv.

xsv is a fast CSV-parsing toolkit written in Rust that mostly matches the functionality of csvkit (including the clutch ability to pipe between modules), with a few extras tacked on (like smart sampling). Did I mention it's fast? In a standup comparison, I ran the "stats" module of XSV against "csvstat" from csvkit, on a 30k-line, 400-column CSV file:

  • Python-based csvkit chews through it in a respectable-and-now-expected 4m16s.

  • xsv takes 1.8 seconds. I don't even have time for a sip of my coffee.

The difference between csvkit and xsv is partly defined by scale; both tools are plenty fast on smaller datasets. But once you get into 10MB-and-upward range, xsv's processing speed pulls away exponentially.

If you've been using csvkit forever (like me), or if you want to be able to transform and analyze CSVs without loading them into a DB, give xsv a shot:

Install Rust

curl https://sh.rustup.rs -sSf | sh

. . . which also gives you the rust package manager cargo, which lets you:

Install xsv

cargo install xsv

Then be sure your PATH is configured correctly:

export PATH=~/.cargo/bin:$PATH

. . . and try it out on a demo CSV with 10k rows, some messy strings, and multiple data types:

curl https://gist.githubusercontent.com/wboykinm/044e2af62fc0c7f77e17f6ccd55b8fb0/raw/fca391e6c03a06a7be770fefca6c47a9acdd2305/mock_data.csv \
| xsv stats \
| xsv table

(xsv table formats the data so it's readable in the console):

field           type     sum                 min                  max                  min_length  max_length  mean                stddev
id              Integer  5005000             1                    1000                 1           4           500.49999999999994  288.6749902572106
first_name      Unicode                      Aaron                Willie               3           11                              
last_name       Unicode                      Adams                Young                3           10                              
email           Unicode                      aadamsp5@senate.gov  wwrightd8@upenn.edu  12          34                              
gender          Unicode                      Female               Male                 4           6                               
ip_address      Unicode                      0.111.40.87          99.50.37.244         9           15                              
value           Unicode                      $1007.98             $999.37              0           8                               
company         Unicode                      Abata                Zoovu                0           13                              
lat             Float    243963.82509999987  -47.75034            69.70287             0           9           24.42080331331331   24.98767816017553
lon             Float    443214.19009999954  -179.12198           170.29993            0           10          44.36578479479489   71.16647723898215
messed_up_data  Unicode                      !@#$%^&*()           𠜎𠜱𠝹𠱓𠱸𠲖𠳏       0           393                             
version         Unicode                      0.1.1                9.99                 3           14                              

Happy parsing!