Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach
Seth E. Spielman; Alex Singleton (2015). Annals of the Association of American Geographers, 105(5), 1003-1025. DOI: 10.1080/00045608.2015.1052335
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 emphasises 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 organised 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.
Extended Summary
This research examines how to overcome the large margins of error in American Community Survey (ACS) data that make neighbourhood-level demographic analysis challenging. The paper argues that whilst individual demographic variables may be too uncertain for reliable census tract analysis, combining multiple variables into composite neighbourhood types can provide meaningful insights about places. The research develops a comprehensive geodemographic classification system covering all US census tracts, moving from a focus on single variables to a contextual approach that views neighbourhoods as ensembles of multiple characteristics. The methodology employs k-means clustering followed by hierarchical clustering analysis to group 70,466 census tracts based on 136 carefully selected variables covering population demographics, environmental factors, and economic characteristics. These variables span domains including age structure, racial composition, education levels, family structure, housing characteristics, population density, commuting patterns, occupational types, and wealth indicators. The analysis creates a two-level classification system with ten broad neighbourhood groups and fifty-five more detailed types, each representing distinct sociospatial patterns across America. The research validates this classification using two external datasets: Federal Election Commission campaign contribution data and Chicago crime statistics, demonstrating that the neighbourhood types successfully predict behaviours and outcomes not used in their creation. Key findings reveal stark differences between neighbourhood types - for instance, low-income minority neighbourhoods contain 43% of Chicago’s crime despite housing only 28% of the population, whilst creative professionals concentrate heavily in high-density urban areas. The classification identifies distinct neighbourhood patterns from Hispanic and immigrant communities to wealthy suburban families, elderly rural populations, and young urban professionals. This contextual approach proves particularly valuable given the ACS’s measurement uncertainties, as random errors in individual variables tend to cancel out when multiple variables are considered together. The research contributes methodologically by providing an open-source framework for geodemographic analysis that other researchers can adapt for different regions or purposes. The practical implications extend to urban planning, public health interventions, and social policy, offering policymakers a data-driven tool for understanding neighbourhood diversity and targeting resources effectively across America’s complex social landscape.
Key Findings
- Combining multiple uncertain ACS variables into neighbourhood typologies produces more reliable results than analysing single demographic variables.
- The research creates a validated ten-group classification system covering all 70,466 US census tracts using 136 demographic and socioeconomic variables.
- External validation reveals significant behavioural differences between neighbourhood types, with low-income areas experiencing disproportionately high crime rates.
- Creative professionals and political donors concentrate heavily in high-density urban neighbourhoods, whilst rural areas show distinct occupational patterns.
- The open-source methodology provides a replicable framework for geodemographic analysis that addresses data uncertainty challenges.
Citation
@article{spielman2015studying,
author = {Seth E. Spielman; Alex Singleton},
title = {Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach},
journal = {Annals of the Association of American Geographers},
year = {2015},
volume = {105(5)},
pages = {1003-1025},
doi = {10.1080/00045608.2015.1052335}
}