The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Patterns and causes of uncertainty in the American Community Survey
Open accessAbstract
In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.
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Cites
Rao, J. N. K. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0731 · Cited by 2,049
Small Area Estimation: An Appraisal
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Algorithms for Reengineering 1991 Census Geography
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Creating the UK National Statistics 2001 Output Area Classification
Vickers, D., & Rees, P. (2007) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2007.00466.x · Cited by 194
Using the American Community Survey: Benefits and Challenges
Citro, C. F., Biemer, P. P., Dunton, N., Frankel, Holt, D., Lohr, S. L., Purvis, C., Salvo, J. J., Stern, H., Cohen, M., et al. (2006) · Cited by 128
Racial diversity and change in metropolitan neighborhoods
Farrell, C. R., & Lee, B. A. (2011) · Social Science Research · doi:10.1016/j.ssresearch.2011.04.003 · Cited by 119
Using High-Resolution Population Data to Identify Neighborhoods and Establish Their Boundaries
Spielman, S. E., & LOGAN, J. R. (2012) · Annals of the Association of American Geographers · doi:10.1080/00045608.2012.685049 · Cited by 116
Self-citationDasymetric Modeling and Uncertainty
Nagle, N. N., Buttenfield, B. P., Leyk, S., & Spielman, S. E. (2013) · Annals of the Association of American Geographers · doi:10.1080/00045608.2013.843439 · Cited by 108
Self-citationAn Investigation of Some Estimators of Variance for Systematic Sampling
Wolter, K. M. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477095 · Cited by 107
Cited by
Krieger, N., Waterman, P. D., Spasojevic, J., Li, W., Maduro, G., & Van Wye, G. (2016) · American Journal of Public Health · doi:10.2105/ajph.2015.302955 · Cited by 432
The widespread and unjust drinking water and clean water crisis in the United States
Mueller, J. T., & Gasteyer, S. (2021) · Nature Communications · doi:10.1038/s41467-021-23898-z · Cited by 247
Contested boundaries: Explaining where ethnoracial diversity provokes neighborhood conflict
Legewie, J., & Schaeffer, M. (2016) · American Journal of Sociology · doi:10.1086/686942 · Cited by 168
Trends in the geospatial distribution of inpatient adult surgical services across the United States
Diaz, A., Schoenbrunner, A., & Pawlik, T. M. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000003366 · Cited by 65
Measuring, mapping, and anticipating climate gentrification in Florida: Miami and Tampa case studies
Tedesco, M., Keenan, J. M., & Hultquist, C. (2022) · Cities · doi:10.1016/j.cities.2022.103991 · Cited by 18
Metadata
- Authors
- Spielman, S. E., Folch, D., & Nagle, N.
- Venue
- Applied Geography
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 46
- Pages
- 147-157
- ISSN
- 0143-6228 (ISSN-L)
- OpenAlex
- W1987541658
- Cited by
- 278
- References
- 13
- FWCI
- 11.47
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
