Scholars deploy census-based measures of neighborhood context throughout the social sciences and epidemiology. Decades of research confirm that variation in how individuals are aggregated into geographic units to create variables that control for social, economic or political contexts can dramatically alter analyses. While most researchers are aware of the problem, they have lacked the tools to determine its magnitude in the literature and in their own projects. By using confidential access to the complete 2010 U.S. Decennial Census, we are able to construct-for all persons in the US-individual-specific contexts, which we group according to the 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 magnitude of variation in context for an individual with respect to the published measures 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 do not capture the actual context of individuals within them. More problematic, we uncover systematic spatial patterns in the contextual variables at all three scales. Finally, we show that within-unit variation is greater in some parts of the country than in others. We publish county-level estimates of the SDIC statistics that enable scholars to assess whether mis-specification in context variables is likely to alter analytic findings when measured at any of the three common Census units.
This paper reports our review of research on domestic climate extremes conducted by US physical geographers over the past 15 years. Sections cover extremes in wind, precipitation, lightning, and temperature, as well as derivative climate extremes (droughts, floods, and storm surges). Themes considered include: the spatial and temporal distribution of the climate extreme; its implications for our understanding of the physical processes that produce it; the spatial and temporal distributions of the extreme's economic and human costs; lessons for assessment, policy, and management; and scale. We conclude that most of the works reviewed inadequately address the human basis of vulnerability to climate extremes, and encourage physical geographers to work with colleagues from the other subfields of geography and the social sciences to develop the holistic understanding of vulnerability needed to effectively adapt to the more extreme climate projected under climate change.
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 alteranalyses 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.-individualspecific 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.
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