Folch David C, Arribas-Bel Daniel, Koschinsky Julia, Spielman Seth E
Department of Geography, Florida State University, Tallahassee, FL, USA.
Department of Geography and Planning, University of Liverpool, Liverpool, UK.
Demography. 2016 Oct;53(5):1535-1554. doi: 10.1007/s13524-016-0499-1.
Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.
社会科学研究、公共和私营部门的决策以及联邦资源的分配常常依赖于美国社区调查（ACS）的数据。然而，这个关键的数据源在其一些最常用的估计值中存在高度不确定性。以2006 - 2010年人口普查区尺度的美国社区调查家庭收入中位数估计值为例，我们探究了美国社区调查估计质量的空间和非空间模式。我们发现，美国北部不确定性的空间模式与南部不同，而且郊区与城市核心区也不同。在这两种情况下，前者的不确定性低于后者。此外，低收入地区的不确定性更高。我们使用一系列多元空间回归模型来描述估计值不确定性与经济、人口和地理因素之间的关联模式，并控制了回复数量。我们发现，即使在考虑了回复数量之后，估计质量的这些人口和地理模式仍然存在。我们的结果表明，数据质量因地点而异，这使得区域内和区域间的横断面分析可靠性降低。最后，我们为数据使用者提供了建议，并针对所发现的挑战提出了潜在的解决方案。
