Conference Paper
Estimates with errors and errors with estimates: Using the R "acs'" package for analysis of American Community Survey data

Over the past decade, the U.S. Census Bureau has implemented the American Community Survey as a replacement for its traditional decennial "long-form" survey.  This year---for the first time ever---ACS data was made available at the census tract and block group level for the entire nation, representing geographies small enough to be useful to local planners; in the future these estimates will be updated on a yearly basis, providing much more current data than was ever available in the past.  Although the ACS represents a bold strategy with great promise for planners working at the neighborhood scale, it will require them to become comfortable with statistical techniques and concerns that they have traditionally been able to avoid.

To help with this challenge the author has been working with local-level planners to determine the most common problems associated with using ACS data, and has implemented these functions as a package in the R statistical programming language. The effort is still a work-in-progress, with some tweaks remaining on the "punch-list" (to say nothing of the additional features on the "wish list"), but the basic framework is in place.  The package defines a new "acs" class object (containing estimates, standard errors, and metadata for tables from the ACS), with methods to deal appropriately with common tasks (e.g., creating and combining subgroups or geographies, automatic fetching of data via the Census API, mathematical operations on estimates, tests of significance, plots of confidence intervals, etc.).

Title
Publication TypeConference Paper
Year of Publication2015
AuthorsGlenn E
Conference NameACS Data Users Conference
Date Published05/2015
Conference LocationHyattsville, MD
KeywordsACS, census, demographics, R, sampling
Abstract

Over the past decade, the U.S. Census Bureau has implemented the American Community Survey as a replacement for its traditional decennial "long-form" survey.  This year---for the first time ever---ACS data was made available at the census tract and block group level for the entire nation, representing geographies small enough to be useful to local planners; in the future these estimates will be updated on a yearly basis, providing much more current data than was ever available in the past.  Although the ACS represents a bold strategy with great promise for planners working at the neighborhood scale, it will require them to become comfortable with statistical techniques and concerns that they have traditionally been able to avoid.

To help with this challenge the author has been working with local-level planners to determine the most common problems associated with using ACS data, and has implemented these functions as a package in the R statistical programming language. The effort is still a work-in-progress, with some tweaks remaining on the "punch-list" (to say nothing of the additional features on the "wish list"), but the basic framework is in place.  The package defines a new "acs" class object (containing estimates, standard errors, and metadata for tables from the ACS), with methods to deal appropriately with common tasks (e.g., creating and combining subgroups or geographies, automatic fetching of data via the Census API, mathematical operations on estimates, tests of significance, plots of confidence intervals, etc.).