ArticlePloS one2015
Reducing uncertainty in the american community survey through data-driven regionalization.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Hierarchical clustering for disentangling population vulnerability to asthma.Environmental advances · 2026Article
- Assessing specification assumptions in urban pharmacy accessibility in New York City.Spatial and spatio-temporal epidemiology · 2026Article
- Quantifying uncertainty due to sampling error in a social vulnerability index: A Monte Carlo approach.PloS one · 2026Article
- Pharmacy deserts and health inequities in St. Louis City: a geospatial analysis of access disparities and determinants.BMC public health · 2025Article
- The development of the Community Deprivation Index and its application to accountable care organizations.Health affairs scholar · 2024Article
- Reliable event rates for disease mapping.Journal of official statistics · 2024Article
- A BAYESIAN HIERARCHICAL SMALL AREA POPULATION MODEL ACCOUNTING FOR DATA SOURCE SPECIFIC METHODOLOGIES FROM AMERICAN COMMUNITY SURVEY, POPULATION ESTIMATES PROGRAM, AND DECENNIAL CENSUS DATA.The annals of applied statistics · 2024Article
- Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States.Environmental health perspectives · 2024Article
- Assessment of Population-Level Disadvantage Indices to Inform Equitable Health Policy.The Milbank quarterly · 2022Article
- Structural modeling of COVID-19 spread in relation to human mobility.Transportation research interdisciplinary perspectives · 2022Article
- Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information.Annals of the American Association of Geographers · 2022Article
- Locations of licensed and unlicensed cannabis retailers in California: A threat to health equity?Preventive medicine reports · 2020Article
- Exploring the Spatial Determinants of Late HIV Diagnosis in Texas.Preventing chronic disease · 2020Article
- Census Demographics and Chlorpyrifos Use in California's Central Valley, 2011-15: A Distributional Environmental Justice Analysis.International journal of environmental research and public health · 2020Article
- Advances in mapping population and demographic characteristics at small-area levels.International journal of epidemiology · 2020Article
- Quantification of Neighborhood-Level Social Determinants of Health in the Continental United States.JAMA network open · 2020Article
- Understanding Geographic and Neighborhood Variations in Overdose Death Rates.Journal of community health · 2019Article
- Measuring global spatial autocorrelation with data reliability information.The Professional geographer : the journal of the Association of American Geographers · 2019Article
- Do Income, Race and Ethnicity, and Sprawl Influence the Greenspace-Human Health Link in City-Level Analyses? Findings from 496 Cities in the United States.International journal of environmental research and public health · 2018Article
- Using Indirect Measures to Identify Geographic Hot Spots of Poor Glycemic Control: Cross-sectional Comparisons With an A1C Registry.Diabetes care · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.