Scholars frequently use counts of populations aggregated into geographic units like census tracts to represent measures of neighborhood context. Decades of research confirm that variation in how individuals are aggregated into geographic units can dramatically alter analyses conducted with these units. While most researchers are aware of the problem, they have lacked the tools to determine its magnitude or its capacity to affect analytical results obtained using these contextual measures. Using confidential access to the complete 2010 U.S. Decennial Census, we can construct—for all persons in the U.S.—individual‐specific contexts, which we group according to Census‐assigned block, block group, and tract. We compare these individual‐specific measures to the published statistics at each scale, and we then determine the degree to which published measures could be affected by how boundaries are drawn using a simple statistic, the standard deviation of individual context (SDIC). For three key measures (percent Black, percent Hispanic, and Entropy—a measure of ethno‐racial diversity), we find that block‐level Census statistics frequently contain a high degree of uncertainty meaning that they may not capture the actual context of individuals within them. More problematic, we uncover systematic spatial patterns in the uncertainty associated with contextual variables at all three scales.

中文翻译：

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谁是我附近的人？：使用人口普查地理位置衡量个人上下文的“上下文谬误”

学者们经常使用人口统计汇总成地理单位（如人口普查区）来表示邻域环境的度量。数十年的研究证实，将个人聚合为地理单位的方式的不同会极大地改变使用这些单位进行的分析。尽管大多数研究人员都知道这个问题，但他们缺乏确定其大小或影响使用这些上下文测量方法获得的分析结果的能力的工具。通过使用对完整的2010年美国十年期人口普查的机密访问，我们可以为美国境内的所有人构建个人特定的环境，我们可以根据人口普查分配的街区，街区组和区域对其进行分组。我们将这些特定于个人的指标与各个级别的已发布统计数据进行比较，个人情况的标准差（SDIC）。对于三个关键指标（黑人百分比，西班牙裔百分比和民族种族多样性的一种衡量指标），我们发现总体人口普查统计数据经常包含高度不确定性，这意味着它们可能无法捕获其中的个体的实际情况他们。更成问题的是，我们发现了在所有三个尺度上与上下文变量相关的不确定性中的系统空间格局。