Schroeder Jonathan P, Pacas José D
Institute for Social Research and Data Innovation, University of Minnesota, 50 Willey Hall, 225 19th Ave S, Minneapolis, MN 55417, USA.
Spat Demogr. 2021 Apr;9(1):131-154. doi: 10.1007/s40980-021-00081-y. Epub 2021 Mar 15.
Microdata from U.S. decennial censuses and the American Community Survey are a key resource for social science and policy analysis, enabling researchers to investigate relationships among all reported characteristics for individual respondents and their households. To protect privacy, the Census Bureau restricts the detail of geographic information in public use microdata, and this complicates how researchers can investigate and account for variations across levels of urbanization when analyzing microdata. One option is to focus on metropolitan status, which can be determined exactly for most microdata records and approximated for others, but a binary metro/nonmetro classification is still coarse and limited on its own, emphasizing one aspect of rural-urban variation and discounting others. To address these issues, we compute two continuous indices for public use microdata-average tract density and average metro/micro-area population-using population-weighted geometric means. We show how these indices correspond to two key dimensions of urbanization-concentration and size-and we demonstrate their utility through an examination of disparities in poverty throughout the rural-urban universe. Poverty rates vary across settlement types in nonlinear ways: rates are lowest in moderately dense parts of major metro areas, and rates are higher in both low- and high-density areas, as well as in smaller commuting systems. Using the two indices also reveals that correlations between poverty and demographic characteristics vary considerably across settlement types. Both indices are now available for recent census microdata via IPUMS USA (https://usa.ipums.org).
美国十年一度人口普查和美国社区调查的微观数据是社会科学和政策分析的关键资源,使研究人员能够调查个体受访者及其家庭所有报告特征之间的关系。为保护隐私,人口普查局限制了公开使用微观数据中地理信息的详细程度,这使得研究人员在分析微观数据时难以调查和考虑城市化水平的差异。一种选择是关注大都市地位,对于大多数微观数据记录可以准确确定,对于其他记录可以进行近似确定,但二元的大都市/非大都市分类本身仍然粗糙且有限,只强调城乡差异的一个方面而忽略其他方面。为解决这些问题,我们使用人口加权几何平均数为公开使用微观数据计算了两个连续指标——平均街区密度和平均大都市/微区域人口。我们展示了这些指标如何对应城市化的两个关键维度——集中度和规模,并通过研究城乡全域的贫困差距来证明它们的效用。贫困率在不同居住类型之间呈非线性变化:在主要大都市地区中等密度部分贫困率最低,在低密度和高密度地区以及较小通勤系统中贫困率较高。使用这两个指标还表明,贫困与人口特征之间的相关性在不同居住类型之间差异很大。现在可以通过美国综合公共微观数据系列(IPUMS USA,https://usa.ipums.org)获取最近人口普查微观数据的这两个指标。