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.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Earth-Surface Processes