{"title":"跨越城乡宇宙:美国人口普查微观数据的两个连续城市化指数。","authors":"Jonathan P Schroeder, José D Pacas","doi":"10.1007/s40980-021-00081-y","DOIUrl":null,"url":null,"abstract":"<p><p>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. 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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. 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引用次数: 5
摘要
来自美国十年一次的人口普查和美国社区调查的微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人受访者及其家庭的所有报告特征之间的关系。为了保护隐私,人口普查局限制了公共使用微数据中的地理信息细节,这使得研究人员在分析微数据时如何调查和解释不同城市化水平的变化变得复杂。一种选择是关注大都市状态,这可以对大多数微数据记录精确确定,对其他微数据记录近似确定,但二元大都市/非大都市分类本身仍然是粗糙和有限的,强调城乡差异的一个方面而忽略其他方面。为了解决这些问题,我们使用人口加权几何平均数计算了公共微数据的两个连续指数——平均区域密度和平均地铁/微区域人口。我们展示了这些指数是如何对应城市化的两个关键维度——集中度和规模——并通过对整个城乡世界贫困差距的考察,展示了它们的效用。不同居住类型的贫困率呈非线性变化:在主要都市地区人口密度适中的地区,贫困率最低,而在人口密度较低和人口密度较高的地区,以及较小的通勤系统中,贫困率都较高。使用这两个指数还表明,贫困与人口特征之间的相关性在不同定居类型之间差异很大。这两个指数现在都可以通过IPUMS USA (https://usa.ipums.org)获得最近的人口普查微观数据。
Across the Rural-Urban Universe: Two Continuous Indices of Urbanization for U.S. Census Microdata.
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).
期刊介绍:
Spatial Demography focuses on understanding the spatial and spatiotemporal dimension of demographic processes. More specifically, the journal is interested in submissions that include the innovative use and adoption of spatial concepts, geospatial data, spatial technologies, and spatial analytic methods that further our understanding of demographic and policy-related related questions. The journal publishes both substantive and methodological papers from across the discipline of demography and its related fields (including economics, geography, sociology, anthropology, environmental science) and in applications ranging from local to global scale. In addition to research articles the journal will consider for publication review essays, book reviews, and reports/reviews on data, software, and instructional resources.