Data Access

Direct Download

Data Dictionaries

Not sure where to start? These data dictionaries provide a comprehensive overview of what variables are available for each geography—State (S), County (C), Census Tract (T), and Zip Code Tabulation Area (Z).

• S_Dict.xlsx
• C_Dict.xlsx
• T_Dict.xlsx
• Z_Dict.xlsx

Individual Datasets

OEPS datasets are merged into single CSVs, one per geography (State, County, Tract, ZCTA) per year (1980, 1990, etc.). Each CSV can be joined to an appropriate geometry file using the HEROP_ID field (see below).

State

County

Tract

Zip Code Tabulation Area (ZCTA)

Geometry Files

For spatial analysis we provide our geographic datasets generated from the US Census Bureau's Cartographic Boundary files (500k scale). We provide the following formats: Shapefile, GeoJSON, or PMTiles.

• Go to all download links and metadata

Use 2010 geometry files when joining to any OEPS data from 1980, 1990, 2000, or 2010. Use the 2018 geometry files for any later datasets.

Frictionless Data Package

We provide a single data package with all CSV and Shapefile assets which is structured to match the Data Package specification published by Frictionless Data.

• Download package (208mb)
• Frictionless Data Package v1 spec

Programmatic Access

oepsData — R Package

We maintain a small R package called oepsData. This package is the best way for researchers who use R to load and analyze OEPS data directly, without the need to download CSVs or Shapefiles and worry about joins.

• Documentation: Learn how to install and use the package.
• Usage examples: Within the package docs we have a few examples of what it looks like to load and use OEPS data.
• GitHub: Use the GitHub repo to report issues you have with the package, or suggest new features or datasets.

Current release: v0.1

Google BigQuery

We have loaded the OEPS data warehouse into Google BigQuery, a data storage platgorm that provides the ability for researchers to run SQL queries (including spatial queries) to retrieve or perform analysis on specific data subsets. Google publishes many different clients through which you can access a BigQuery database, and for R users there is bigrquery. Here's how to get started:

• Introduction to OEPS in Google BigQuery: The oepsData documentation includes a detailed overview that is relevant no matter what client you use.
• Setting up BigQuery in R: The oepsData documentation also has a walkthrough guide illustrating how R users can connect directly to our data in BigQuery.
• Database Table Reference: Full reference document, provides the project id and the names of all tables and columns.