Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach

Abstract

In 2010 the American Community Survey (ACS) replaced the long form of the decennial census as the sole national source of demographic and economic data for small geographic areas such as census tracts. These small area estimates suffer from large margins of error, however, which makes the data difficult to use for many purposes. The value of a large and comprehensive survey like the ACS is that it provides a richly detailed, multivariate, composite picture of small areas. This article argues that one solution to the problem of large margins of error in the ACS is to shift from a variable-based mode of inquiry to one that emphasizes a composite multivariate picture of census tracts. Because the margin of error in a single ACS estimate, like household income, is assumed to be a symmetrically distributed random variable, positive and negative errors are equally likely. Because the variable-specific estimates are largely independent from each other, when looking at a large collection of variables these random errors average to zero. This means that although single variables can be methodologically problematic at the census tract scale, a large collection of such variables provides utility as a contextual descriptor of the place(s) under investigation. This idea is demonstrated by developing a geodemographic typology of all U.S. census tracts. The typology is firmly rooted in the social scientific literature and is organized around a framework of concepts, domains, and measures. The typology is validated using public domain data from the City of Chicago and the U.S. Federal Election Commission. The typology, as well as the data and methods used to create it, is open source and published freely online.