Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

References

• Alexander, C.H., 2002. Still rolling: Leslie Kish’s “rolling samples” and the American Community Survey. Survey Methodology, 28 (1), 35–41.
   Google Scholar
• Anselin, L., 1995. Local indicators of spatial association—LISA. Geographical Analysis, 27 (2), 93–115. doi:10.1111/j.1538-4632.1995.tb00338.x
   Web of Science ®Google Scholar
• Anselin, L., Syabri, I., and Kho, Y., 2006. GeoDa: an introduction to spatial data analysis. Geographical Analysis, 38 (1), 5–22. doi:10.1111/gean.2006.38.issue-1
   Web of Science ®Google Scholar
• Assunção, R.M. and Reis, E.A., 1999. A new proposal to adjust Moran’s I for population density. Statistics in Medicine, 18, 2147–2162. doi:10.1002/(ISSN)1097-0258
   PubMed Web of Science ®Google Scholar
• Assunção, R.M., et al., 2005. Empirical Bayes estimation of demographic schedules for small areas. Demography, 42 (3), 537–558.
   PubMed Web of Science ®Google Scholar
• Bazuin, J.T. and Fraser, J.C., 2013. How the ACS gets it wrong: the story of the American Community Survey and a small, inner city neighborhood. Applied Geography, 45, 292–302. doi:10.1016/j.apgeog.2013.08.013
   Web of Science ®Google Scholar
• Bradley, J.R., Wikle, C.K., and Holan, S.H., 2016. Bayesian spatial change of support for count-valued survey data with application to the American Community Survey. Journal of the American Statistical Association, 111 (514), 472–487. doi:10.1080/01621459.2015.1117471
   Web of Science ®Google Scholar
• Cliff, A.D. and Ord, J.K., 1981. Spatial processes: models and applications. London: Pion.
   Google Scholar
• Cressie, N., 1992. Smoothing regional maps using empirical Bayes predictors. Geographical Analysis, 24 (1), 75–95. doi:10.1111/j.1538-4632.1992.tb00253.x
   Web of Science ®Google Scholar
• Datta, G.S., et al., 2011. Bayesian benchmarking with applications to small area estimation. Test, 20 (3), 574–588. doi:10.1007/s11749-010-0218-y
   Web of Science ®Google Scholar
• Dima, A.L., 2018. Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol. Health Psychology and Behavioral Medicine, 6 (1), 136–161. doi:10.1080/21642850.2018.1472602
   PubMed Web of Science ®Google Scholar
• Erickson, T. and Whited, T.M., 2002. Two-step GMM estimation of the errors-in-variables model using high-order moments. Econometric Theory, 18, 776–799. doi:10.1017/S0266466602183101
   Web of Science ®Google Scholar
• Fay, R., 2007. Imbedding model-assisted estimation into ACS estimation. In: Proceedings of the 2007 Joint Statistical Meetings, Salt Lake City, UT. Alexandria, VA: American Statistical Association, 2946–2953.
   Google Scholar
• Folch, D.C., et al., 2016. Spatial variation in the quality of American Community Survey estimates. Demography, 53 (5), 1535–1554. doi:10.1007/s13524-016-0499-1
   PubMed Web of Science ®Google Scholar
• Frost, C. and Thompson, S.G., 2000. Correcting for regression dilution bias: comparison of methods for a single predictor variable. Journal of the Royal Statistical Society: Series A, 163 (2), 173–189. doi:10.1111/1467-985X.00164
   Web of Science ®Google Scholar
• Fuller, W.A., 1987. Measurement error models. New York: Wiley.
   Google Scholar
• Gage, L., 2006. Comparison of census 2000 and American Community Survey 1999–2001 estimates: San Francisco and Tulare Counties, California. Population Research and Policy Review, 25 (3), 243–256. doi:10.1007/s11113-006-9005-6
   Web of Science ®Google Scholar
• Ghosh, M. and Rao, J.N.K., 1994. Small area estimation: an appraisal. Statistical Science, 9 (1), 55–93. doi:10.1214/ss/1177010647
   Web of Science ®Google Scholar
• Griffin, R.A. and Albright, K., 2015. Variance issues related to generalized regression estimation in the housing unit weighting operation. Washington, DC: U.S. Census Bureau. No. ACS15-RER-05.
   Google Scholar
• Hausman, J., 2001. Mismeasured variables in econometric analysis: problems from the right and problems from the left. Journal of Economic Perspectives, 15 (4), 57–67. doi:10.1257/jep.15.4.57
   Web of Science ®Google Scholar
• Hough, G.C. and Swanson, D.A., 2004. The 1999–2001 American Community Survey and the 2000 census data quality and data comparisons: Multnomah county, Oregon. Portland, OR: Population Research Center, Portland State University.
   Google Scholar
• Hutcheon, J.A., Chiolero, A., and Hanley, J.A., 2010. Random measurement error and regression dilution bias. BMJ, 340, c2289. doi:10.1136/bmj.c293
   PubMed Web of Science ®Google Scholar
• Jung, P.H., Thill, J.-C., and Issel, M., 2018. Spatial autocorrelation statistics of areal prevalence rates under high uncertainty in denominator data. Geographical Analysis [ online]. doi:10.1111/gean.12177
   Web of Science ®Google Scholar
• Jurjevich, J.R., et al., 2018. Navigating statistical uncertainty: how urban and regional planners understand and work with American Community Survey (ACS) data for guiding policy. Journal of the American Planning Association, 84 (2), 112–126. doi:10.1080/01944363.2018.1440182
   Web of Science ®Google Scholar
• Kafadar, K., 1996. Smoothing geographical data, particularly rates of disease. Statistics in Medicine, 15 (23), 2539–2560. doi:10.1002/(ISSN)1097-0258
   PubMed Web of Science ®Google Scholar
• Kish, L., 1990. Rolling samples and censuses. Survey Methodology, 16 (1), 63–71.
   Google Scholar
• Logan, J., et al., 2018. The uptick in income segregation: real trend or random sampling variance. American Journal of Sociology, 124 (1), 185–222. doi:10.1086/697528
   Web of Science ®Google Scholar
• Macdonald, H., 2006. The American Community Survey: warmer (more current), but fuzzier (less precise) than the decennial census. Journal of the American Planning Association, 72 (4), 491–503. doi:10.1080/01944360608976768
   Web of Science ®Google Scholar
• Marshall, R., 1991. Mapping disease and mortality rates using empirical Bayes estimators. Applied Statistics, 40 (2), 283–294. doi:10.2307/2347593
   Web of Science ®Google Scholar
• National Research Council, 2007. Using the American community survey: benefits and challenges. Washington, DC: National Academy Press.
   Google Scholar
• Navarro, A., 2012. An introduction to ACS statistical methods and lessons learned. In Measuring People in Place Conference. Boulder, CO. Available from: https://cupc.colorado.edu/workshops/measuring_people_in_place/themes/theme1/navarro.pdf [Accessed 22 April 2018].
   Google Scholar
• Porter, A.T., Wikle, C.K., and Holan, S.H., 2015. Small area estimation via multivariate Fay-Herriot models with latent spatial dependence. Australian & New Zealand Journal of Statistics, 57 (1), 15–29. doi:10.1111/anzs.12101
   Web of Science ®Google Scholar
• Rao, J.N.K., 2005. Inferential issues in small area estimation: some new developments. Statistics in Transition, 7 (3), 513–526.
   Google Scholar
• Rao, J.N.K. and Molina, I., 2015. Small area estimation. 2nd ed. Hoboken, NJ: Wiley.
   Google Scholar
• Salvo, J., Lobo, P., and Calabrese, T., 2004. Small area data quality: a comparison of estimates 2000 census and the 1999–2001 ACS Bronx, New York Test Site. New York. New York: Department of City Planning, Population Division.
   Google Scholar
• Schennach, S.M., 2004. Nonparametric regression in the presence of dynamic heteroskedasticity. Econometric Theory, 20, 1046–1093. doi:10.1017/S0266466604206028
   Web of Science ®Google Scholar
• Smith, G.D. and Phillips, A.N., 1996. Inflation in epidemiology: “The proof and measurement of association between two things” revisited. BMJ, 312, 1659–1661. doi:10.1136/bmj.312.7047.1659
   PubMed Web of Science ®Google Scholar
• Spielman, S.E., Folch, D., and Nagle, N., 2014. Patterns and causes of uncertainty in the American Community Survey. Applied Geography, 46, 147–157. doi:10.1016/j.apgeog.2013.11.002
   PubMed Web of Science ®Google Scholar
• Spielman, S.E. and Folch, D.C., 2015. Reducing uncertainty in the American Community Survey through data-driven regionalization. PLoS ONE, 10 (2), 1–21. doi:10.1371/journal.pone.0115626
   Web of Science ®Google Scholar
• Spielman, S.E. and Singleton, A., 2015. Studying neighborhoods using uncertain data from the American Community Survey: a contextual approach. Annals of the Association of American Geographers, 105 (5), 1003–1025. doi:10.1080/00045608.2015.1052335
   Web of Science ®Google Scholar
• Starsinic, M.D., 2005. American Community Survey: improving reliability for small area estimates. In: Proceedings of the Joint Statistical Meetings, ASA Section on Survey Research Methods. (CD-ROM). Alexandria, VA: American Statistical Association, 3592–3599.
   Google Scholar
• Sun, M., Wong, D.W., and Kronenfeld, B.J., 2015. A classification method for choropleth maps incorporating data reliability information. Professional Geographer, 67 (1), 72–83. doi:10.1080/00330124.2014.888627
   Web of Science ®Google Scholar
• Sun, M. and Wong, D.W.S., 2010. Incorporating data quality information in mapping American Community Survey data. Cartography and Geographic Information Science, 37 (4), 285–300. doi:10.1559/152304010793454363
   Web of Science ®Google Scholar
• Sun, M. and Wong, D.W.S., 2017. Spatial aggregation as a means to improve attribute reliability. Computers, Environment and Urban Systems, 65, 15–27. doi:10.1016/j.compenvurbsys.2017.04.007
   Web of Science ®Google Scholar
• U.S. Census Bureau, 2000. U.S. Census 2000 long form questionnaire. Available from: https://www.census.gov/dmd/www/pdf/d02p.pdf [Accessed 22 April 2018].
   Google Scholar
• U.S. Census Bureau, 2002. Census 2000 basics. Washington, DC: U.S. Government Printing Office.
   Google Scholar
• U.S. Census Bureau, 2004. Meeting 21 st century demographic data needs — implementing the American Community Survey. Washington, DC: U.S. Census Bureau.
   Google Scholar
• U.S. Census Bureau, 2008. A compass for understanding and using American Community Survey data. Washington, DC: U.S. Government Printing Office.
   Google Scholar
• Wei, R. and Grubesic, T.H., 2017. An alternative classification scheme for uncertain attribute mapping. Professional Geographer, 69 (4), 604–615. doi:10.1080/00330124.2017.1288573
   Web of Science ®Google Scholar
• Wei, R., Tong, D., and Phillips, J.M., 2017. An integrated classification scheme for mapping estimates and errors of estimation from the American Community Survey. Computers, Environment and Urban Systems, 63, 95–103. doi:10.1016/j.compenvurbsys.2016.04.003
   Web of Science ®Google Scholar