Data from the American Community Survey (ACS) provides a wealth of information useful for learning more about social determinants of health and their spatial distributions within a defined region. Although available data includes quantified indicators of uncertainty in aggregated location-specific estimates for a range of variables, this uncertainty is often ignored, the consequences of which may include estimate bias and reduced statistical power. Fortunately, the measurement error literature provides a range of useful tools for handling such error. We propose and demonstrate a new application of existing, well-supported measurement error models to spatial regression models. We show that the existing solution of ignoring the measurement error inherent in these data precludes precise effect estimation and that straightforward modifications to traditional estimators can be made to correct for this error. We intend for this work to establish the basic principles of error correction in spatial data and a new method for applying corrected regression estimators to such data.

来自美国社区调查 (ACS) 的数据提供了大量有用的信息，可用于更多地了解健康的社会决定因素及其在特定区域内的空间分布。尽管可用数据包括一系列变量的特定地点汇总估计中不确定性的量化指标，但这种不确定性往往被忽略，其后果可能包括估计偏差和统计能力降低。幸运的是，测量误差文献提供了一系列用于处理此类误差的有用工具。我们提出并展示了现有的、得到充分支持的测量误差模型在空间回归模型中的新应用。我们表明，忽略这些数据中固有的测量误差的现有解决方案排除了精确的效果估计，并且可以对传统估计器进行直接修改以纠正此误差。我们打算在这项工作中建立空间数据误差校正的基本原理，以及将校正回归估计量应用于此类数据的新方法。