pa-gov/income-poverty-us-census-acs-5-year-estimates-6npu-erdk
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Query the Data Delivery Network

Query the DDN

The easiest way to query any data on Splitgraph is via the "Data Delivery Network" (DDN). The DDN is a single endpoint that speaks the PostgreSQL wire protocol. Any Splitgraph user can connect to it at data.splitgraph.com:5432 and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.

For example, you can query the income_poverty_us_census_acs_5_year_estimates table in this repository, by referencing it like:

"pa-gov/income-poverty-us-census-acs-5-year-estimates-6npu-erdk:latest"."income_poverty_us_census_acs_5_year_estimates"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "state", -- These are the first 2 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the State association. Each State has its own 2-digit number and each County within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county. For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.). Information gathered from census data - https://www.census.gov/library/reference/code-lists/ansi.html
    "per_capita_income", -- Per capita income in the past 12 months (in 2019 inflation-adjusted dollars)
    "pop_abovepoverty_85plus", -- Population age 85 and over with income in the last 12 months at or above the poverty level
    "pop_abovepoverty_75to84", -- Population age 75 to 84 with income in the last 12 months at or above the poverty level
    "pop_abovepoverty_60to74", -- Population age 60 to 74 with income in the last 12 months at or above the poverty level
    "pop_abovepoverty_6to11", -- Population age 6 to 11 with income in the last 12 months at or above the poverty level
    "pop_abovepoverty_lt6", -- Population age less than 6 with income in the last 12 months at or above the poverty level
    "pop_abovepoverty", -- Population with income in the last 12 months at or above the poverty level
    "pop_belowpoverty_85plus", -- Population age 85 and over with income in the last 12 months below the poverty level
    "pop_belowpoverty_75to84", -- Population age 75 to 84 with income in the last 12 months below the poverty level
    "pop_belowpoverty_60to74", -- Population age 60 to 74 with income in the last 12 months below the poverty level
    "pop_belowpoverty_18to59", -- Population age 18 to 59 with income in the last 12 months below the poverty level
    "fam_households_income12mntsbelowpoverty", -- The number of family households with Income in the last 12 months below the poverty level
    "nonfamily_households", -- The total number of nonfamily households
    "median_household_income", -- Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
    "pop_abovepoverty_18to59", -- Population age 18 to 59 with income in the last 12 months at or above the poverty level
    "pop_abovepoverty_12to17", -- Population age 12 to 17 with income in the last 12 months at or above the poverty level
    "pop_belowpoverty_12to17", -- Population age 12 to 17 with income in the last 12 months below the poverty level
    "pop_belowpoverty_6to11", -- Population age 6 to 11 with income in the last 12 months below the poverty level
    "pop_belowpoverty", -- Population with income in the last 12 months below the poverty level
    "county", -- This is the name of the Pennsylvania County. Pennsylvania has 67 counties. 
    "year", -- The end year of the 5-year period of the estimate. Ex. 2019 refers to years 2015 - 2019. 
    "pop_belowpoverty_lt6", -- Population age less than 6 with income in the last 12 months below the poverty level
    "population", -- The total poverty population
    "nonfam_households_income12mntsbelowpoverty", -- The number of nonfamily households with Income in the last 12 months below the poverty level
    "family_households", -- The total number of family households
    "county_1" -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code (FIPS 6-4) which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. This is the 3-digit part of the 5-digit county FIPS code specifically standing for the county.
FROM
    "pa-gov/income-poverty-us-census-acs-5-year-estimates-6npu-erdk:latest"."income_poverty_us_census_acs_5_year_estimates"
LIMIT 100;

Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query pa-gov/income-poverty-us-census-acs-5-year-estimates-6npu-erdk with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.pa.gov. When you querypa-gov/income-poverty-us-census-acs-5-year-estimates-6npu-erdk:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).

We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.

Query Your Local Engine

Install Splitgraph Locally
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"
 

Read the installation docs.

Splitgraph Cloud is built around Splitgraph Core (GitHub), which includes a local Splitgraph Engine packaged as a Docker image. Splitgraph Cloud is basically a scaled-up version of that local Engine. When you query the Data Delivery Network or the REST API, we mount the relevant datasets in an Engine on our servers and execute your query on it.

It's possible to run this engine locally. You'll need a Mac, Windows or Linux system to install sgr, and a Docker installation to run the engine. You don't need to know how to actually use Docker; sgrcan manage the image, container and volume for you.

There are a few ways to ingest data into the local engine.

For external repositories (like this repository), the Splitgraph Engine can "mount" upstream data sources by using sgr mount. This feature is built around Postgres Foreign Data Wrappers (FDW). You can write custom "mount handlers" for any upstream data source. For an example, we blogged about making a custom mount handler for HackerNews stories.

For hosted datasets, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Mounting Data

This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.pa.gov, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.

First, install Splitgraph if you haven't already.

Mount the table with sgr mount

sgr mount socrata \
  "pa-gov/income-poverty-us-census-acs-5-year-estimates-6npu-erdk" \
  --handler-options '{
    "domain": "data.pa.gov",
    "tables": {
        "income_poverty_us_census_acs_5_year_estimates": "6npu-erdk"
    }
}'

That's it! Now you can query the data in the mounted table like any other Postgres table.

Query the data with your existing tools

Once you've loaded the data into your local Splitgraph engine, you can query it with any of your existing tools. As far as they're concerned, pa-gov/income-poverty-us-census-acs-5-year-estimates-6npu-erdk is just another Postgres schema.