Administrative Data Research Facilities Site
This repository replaces the Administrative Data Research Facilities (ADRF) website.
Across multiple disciplines, from healthcare to justice to poverty to housing, there are projects that benefit greatly from use of the administrative datasets made available by the government. Our project seeks to clean and combine two of these administrative datasets and then build an easily accessible, open source, cloud-based platform that can be used by social scientists to analyze the data. This was a joint effort between Urban's Housing Finance Policy Center and the Data Science and Technology team at Urban.
This project was funded by the Alfred P. Sloan Foundation.
The project explored data collection and sampling from public sources that offer robust information regarding mortgages, people and place. We have standardized and linked key data variables over time from two government data sources, the Home Mortgage Disclosure Act (HMDA) and the Census American Community Survey (ACS).
Citation: Urban Institute Sloan ADRF Database. Retrieved from http://adrf.urban.org. 2017.
Note that we no longer make the data available, but it can be reproduced using the files, data, and links in this repository.
Geographic Crosswalks Used to Create the Data
Crosswalk data sourced from: Missouri Census Data Center, MABLE/Geocorr2k and MABLE/Geocorr14, Version 1.0: Geographic Correspondence Engine. Web application accessed August, 2017 at: http://mcdc.missouri.edu/websas/geocorr14.html
Sample Research Products
Housing Profile of Areas Affected by Hurricane Harvey Bing Bai, Sarah Strochak, Bhargavi Ganesh October 27, 2017
Housing Profile of Areas Affected by Hurricane Irma Bing Bai, Sarah Strochak, Bhargavi Ganesh October 27, 2017
Housing Affordability: Local and National Perspectives Laurie Goodman, Wei Li, Jun Zhu March 28, 2018
Is Limited English Proficiency a Barrier to Homeownership? Edward Golding, Laurie Goodman, Sarah Strochak March 26, 2018
Spark for Social Science
SPARK FOR SOCIAL SCIENCE Urban has developed an elastic and powerful approach to the analysis of massive datasets using Amazon Web Services’ Elastic MapReduce (EMR) and the Spark framework for distributed memory and processing. For tutorials and to use Spark to analyze the linked datasets we’ve created using HMDA and ACS data, visit our project on GitHub.
Code for Generating the ADRF Data
This repository holds the code for creating the linked Home Mortgage Disclosure Act (HMDA) and American Community Survey Public Use Microdata (ACS PUMS) files. All code is written in SAS and authored by Jun Zhu, of the Urban Institute.
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